WMS Geoserver for the MAPS portal of Eurac Research WFS WMS GEOSERVER Eurac maps Andrea Vianello Eurac Research

Bolzano 39100 Italy andrea.vianello@eurac.edu NONE NONE text/xml image/png application/atom+xml application/json;type=utfgrid application/pdf application/rss+xml application/vnd.google-earth.kml+xml application/vnd.google-earth.kml+xml;mode=networklink application/vnd.google-earth.kmz image/geotiff image/geotiff8 image/gif image/jpeg image/png; mode=8bit image/svg+xml image/tiff image/tiff8 image/vnd.jpeg-png image/vnd.jpeg-png8 text/html; subtype=openlayers text/html; subtype=openlayers2 text/html; subtype=openlayers3 text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml; subtype=gml/3.1.1 text/html text/javascript application/json XML INIMAGE BLANK JSON JSONP Geoserver for the MAPS portal of Eurac Research EPSG:4326 EPSG:3785 EPSG:3857 EPSG:900913 EPSG:32647 EPSG:32736 CRS:84 -180.0 180.0 -90.0 90.0 EO_CDR:2021-04-20_transalp_aut-study-area_vaia_storm-damage-areas No abstract provided 2021-04-20_transalp_aut-study-area_vaia_storm-damage-areas features EPSG:31254 CRS:84 12.230231994519677 12.969605402187764 46.65712608161675 47.13127285728213 other other other other text/xml other other geonode:4dmed_stations Hydrological station data collected in the 4DMED projects: https://www.4dmed-hydrology.org/ Data for these stations are available for project partners only, via API using the token. river piezometer features water snow 4dmed_stations hydrology EPSG:4326 CRS:84 -7.920587 35.05198851 31.124746 46.687021 other other other other text/xml other other geonode:ADO_boundaries The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions. The ADO project consortium includes 11 institutions from 6 Alpine countries with a wide range of expertise, covering meteorological and hydrological monitoring, specific knowledge on modeling, drought risk and impact assessment, as well as water governance in the different sectors. Further information about the ADO project can be found here: https://www.alpine-space.eu/projects/ado/en/about. ADO_region ADO ADO_boundaries EPSG:4326 CRS:84 3.48965167999268 16.930850982666 42.9521179199219 50.0616493225098 other other other other text/xml other other geonode:Aree Mappatura delle 4 Aree di progetto del territorio di Como Risultati del progetto Interreg IT-CH "BIPV meets History" Attivita' 4 - Mappatura del potenziale solare www.bipvmeetshistory.eu features photovoltaic Aree integrated photovoltaic BIPV solar potential EPSG:4326 CRS:84 8.987318817 9.142599503 45.76676702 45.82922317 other other other other text/xml other other geonode:BIPVmeetsHistory0 Edifici casi studio del progetto di ricerca Interregionale "BIPV meets History" nel territorio di Como. Attivita' di Mappatura solare dell'area di progetto. Sito del progetto: www.bipvmeetshistory.eu BIPVmeetsHistory features building EPSG:4326 CRS:84 9.04749566 9.11861085 45.77718591 45.82321346 other other other other text/xml other other geonode:Bdi_trans_distance_roads_meters_per_colline_mean_utm35S No abstract provided WCS Bdi_trans_distance_roads_meters_per_colline_mean_utm35S GeoTIFF EPSG:32735 CRS:84 28.999723145995862 30.85672194300612 -4.4724335572914775 -2.308156014490136 other other other other text/xml other other geonode:Biotop_Bletterbach Biotope area of the Bletterbach geological Park features Biotop_Bletterbach EPSG:25832 CRS:84 11.355511718845097 11.44656497771153 46.34421567744473 46.36794974878706 other other other other other other other text/xml text/xml other other other other other geonode:CDD_Nuts_RG_01M_2021_4326_level_0 Cooling Degree Days at country level (NUTS level 0) is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. CDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded CDD is aggregated and subsequently presented on NUTS-0 level. cct Nuts0 CDD EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:CDD_Nuts_RG_01M_2021_4326_level_2 Cooling Degree Day (CDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level. cct features cooling Nuts2 EPSG:4326 CRS:84 -63.1511917114258 55.8357810974121 -21.3888511657715 71.1841659545898 other other other other other other other other other other text/xml text/xml other other geonode:CE_demo_cases The layer shows the following information about Plus Energy Buildings (PEB) demo cases: 1) context, 2) key features, 3) owner, 4) Location, 5) Technologies integration, 6) Demo community and 7) partnership. CE_demo_cases features EPSG:32632 CRS:84 1.5068385444318086 11.761424739771575 44.54809961929767 59.974288898987695 other other text/xml other other other other geonode:DinAlpConnect_Project_area This data set represents the considered area between the Alps and Dinaric mountains to analyze the situation of ecological connectivity. Link to map: https://maps.eurac.edu/maps/1140/view File name: DinAlpConnect_Project_area.shp Project website: https://dinalpconnect.adrioninterreg.eu/ DinAlpConnect_Project_area Dinaric Alps features EPSG:3035 CRS:84 10.335858508973606 26.637194357492536 37.842558800258786 47.18583888378881 other other other other text/xml other other geonode:DinaricAlps_CSI This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on land use, population pressure, protection status, fragmentation and topography. File name: DinaricAlps_CSI.tif Project website: https://dinalpconnect.adrioninterreg.eu/ GeoTIFF Dinaric Alps WCS DinaricAlps_CSI EPSG:3035 CRS:84 10.335604893728048 26.63763431115246 37.84223444689503 47.18608296540474 other other other other text/xml other other geonode:DinaricAlps_ENV This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on the protection status of protected areas. File name: DinaricAlps_ENV.tif Project website: https://dinalpconnect.adrioninterreg.eu/ Dinaric Alps GeoTIFF DinaricAlps_ENV WCS EPSG:3035 CRS:84 10.068524463473505 26.67579661561148 37.58978497549814 47.18674028621767 other other other other text/xml other other geonode:DinaricAlps_FRA1 This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on the effective mesh size and the effective mesh density. File name: DinaricAlps_FRA.tif Project website: https://dinalpconnect.adrioninterreg.eu/ fragmentation GeoTIFF WCS DinaricAlps_FRA Dinaric Alps EPSG:3035 CRS:84 10.33474511202766 26.64672826925205 37.84133268801136 47.19418812532962 other other other other other text/xml other geonode:DinaricAlps_LAN This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on land cover classes. File name: DinaricAlps_LAN.tif Project website: https://dinalpconnect.adrioninterreg.eu/ GeoTIFF DinaricAlps_LAN Dinaric Alps WCS EPSG:3035 CRS:84 10.33474511202766 26.637949733374466 37.84219151412605 47.186086476555815 other other other other text/xml other other geonode:DinaricAlps_POP0 This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on population density data. File name: DinaricAlps_POP.tif Project website: https://dinalpconnect.adrioninterreg.eu/ DinaricAlps_POP GeoTIFF population density WCS Dinaric Alps EPSG:3035 CRS:84 10.33474511202766 26.637949733374466 37.84219151412605 47.186086476555815 other other other other text/xml other other geonode:DinaricAlps_SACA1_Ecological_Conservation_Are This data set shows important Ecological Conservation Areas (SACA1), bigger than 100ha, in the Dinaric Alps. These areas are expected to have a high biological value and that ecological connectivity is functioning well. Strategic Connectivity Areas (SACAs) derive from the Continuum Suitability Index. This approach is a way to display via GIS the most important sites for the overall ecological network on a macro-regional level (SACA1). Here, the most important ones were selected by expert evaluation. Filename: DinaricAlps_SACA1_Ecological_Conservation_Areas.shp Project website: https://dinalpconnect.adrioninterreg.eu/ DinaricAlps_SACA1_Ecological_Conservation_Areas features EPSG:3035 CRS:84 10.398384077426597 24.702475755900682 38.09544553116135 47.18604998536828 other other other other text/xml other other geonode:DinaricAlps_SACA1_Ecological_Stepping_Stones Stepping stones are representing areas with a high ecological value, important for ecological linkages. They are calculated by the Continuum Suitability Index (CSI) and the Strategic Connectivity Areas, considering small and less important Ecological Conservation Areas. File name: DinaricAlps_SACA1_Ecological_Stepping_Stones.shp Project website: https://dinalpconnect.adrioninterreg.eu/ DinaricAlps_SACA1_Ecological_Stepping_Stones Dinaric Alps features EPSG:3035 CRS:84 10.388997878107789 26.621376881816488 37.85025510961702 47.137552205525736 other other other other other text/xml other geonode:DinaricAlps_SACA2_Reg_ecological_corridors This layer shows the designed width of ecological corridors, that connect Ecological Conservation Areas. An approximate width of 2km was designed by truncating the normalized cost-weighted distances of the corridors at 40.000 km. File name: DinaricAlps_SACA2_Reg_ecological_corridors.shp Project website: https://dinalpconnect.adrioninterreg.eu/ DinaricAlps_SACA2_Reg_ecological_corridors Dinaric Alps features EPSG:3035 CRS:84 10.531176098953953 24.43679038703587 38.15358219719727 47.150503421831125 other other other other other text/xml other geonode:DinaricAlps_SACA2_Regional_ecological_linkage Ecological linkages are least cost paths, connecting the most important Ecological Conservation Areas (SACA1). They are part of the ecological intervention areas (SACA2). File name: DinaricAlps_SACA2_Regional_ecological_linkages_LCP_Assessment.shp Project website: https://dinalpconnect.adrioninterreg.eu/ Dinaric Alps DinaricAlps_SACA2_Regional_ecological_linkages_LCP_Assessment features EPSG:3035 CRS:84 10.552505441795116 24.360636685862627 38.224687669261925 47.132254747017775 other other other other text/xml other other geonode:DinaricAlps_SACA2_motorway_barriers This layer is showing motorway barriers with potential ecological linkages in the Dinaric Alps. File Name: DinaricAlps_SACA2_motorway_barriers.shp Project website: https://dinalpconnect.adrioninterreg.eu/ Dinaric Alps DinaricAlps_SACA2_motorway_barriers features EPSG:3035 CRS:84 10.999996929878943 24.260964629142034 38.35049505320991 46.8316999386691 other other other other text/xml other other geonode:DinaricAlps_SACA3_Ecological_Barriers Ecological Restoration Areas represent important barriers and have a low continuum suitability index (CSI). Ecological Restoration Areas (SACA3) are those ones, where ecological movements are not possible at the current stage and where it is necessary to implement restoration measures. These areas are currently the main barriers. For the calculation of these areas, all areas with a CSI of 1-4 were selected. File name: DinaricAlps_SACA3_Ecological_Barriers.shp Project website: https://dinalpconnect.adrioninterreg.eu/ Dinaric Alps DinaricAlps_SACA3_Ecological_Barriers features EPSG:3035 CRS:84 10.472431390595078 26.452004182057124 37.9565959502963 47.115243580561994 text/xml other other other other other other geonode:DinaricAlps_TOP This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on altitude and slope conditions. File name: DinaricAlps_TOP Project website: https://dinalpconnect.adrioninterreg.eu/ GeoTIFF DinaricAlps_TOP Dinaric Alps WCS EPSG:3035 CRS:84 10.335603750772185 26.637565732682265 37.842012137359696 47.1858579191323 other other other other text/xml other other geonode:Fragsburg_rgb_flight1_3035 UAV orthophoto of apple orchard maintained by Laimburg Research Centre near Fragsburg for precision mapping of Apple Proliferation Fragsburg_rgb_flight1_3035 GeoTIFF WCS EPSG:25832 CRS:84 11.193030999807208 11.19457890194365 46.64117692771583 46.64357614129063 other other other other other other text/xml geonode:GDP Layer about Gross Domestic Product prices components for household consumers - annual data, derived by Eurostat datasets at country level. GDP features EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:HDD_Nuts_RG_01M_2021_4326_level_0 Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-0 level. HDD cct Nuts0 energy EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 other other other other text/xml other other geonode:HDD_Nuts_RG_01M_2021_4326_level_2 Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level. HDD cct NUT temp EPSG:4326 CRS:84 -63.1511917114258 55.8357810974121 -21.3888511657715 71.1841659545898 other other other text/xml other other other geonode:IEQ This layer contents specific Indoor Environmental Quality (IEQ) data sets provided which are rereferred to the closest possible locations of the Cultural-E demo cases. Specific locations and coordinates are included for each one in the IEQ related files that have been made available for downloading. Each geo-referred location contains IEQ related information and graphics, in the specific: 1. Reference year -.epw file-. 2. Climatic statistical data -.txt file-. Weather data plots, elaborated with Climate consultant and merged in a unique document -.pdf file-. 4. Weather data summary elaborated -.xls file-. Relevant information is provided in relation to: data source, used tool, implemented comfort tool, weather stations spec, software download links. This aims to illustrate the accuracy and "data fairness" of the data provided. households weather IEQ cct features EPSG:4326 CRS:84 2.39999985694885 11.3000001907349 44.5299987792969 59.9000015258789 other other other other text/xml other other geonode:KG_climate_class_clip The most frequently used climate classification map is that of Wladimir Köppen, presented in its latest version 1961 by Rudolf Geiger. A huge number of climate studies and subsequent publications adopted this or a former release of the Köppen-Geiger map. While the climate classification concept has been widely applied to a broad range of topics in climate and climate change research as well as in physical geography, hydrology, agriculture, biology and educational aspects, a well-documented update of the world climate classification map is still missing. Based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service, we present here a new digital Köppen-Geiger world map on climate classification for the second half of the 20th century. climate KG_climate_class_clip GeoTIFF cct WCS EPSG:4326 CRS:84 -61.83333333333334 55.833333333333314 -21.3611111111111 71.16666666666667 other other text/xml other other other other geonode:Locali_convenzionati_01092021_90pcmatched0 This layer shows 90 % of the locations of bars/restaurants where the Eurac lunchcard can be used. As of: 01.09.2021. restaurant lunch EPSG:32632 CRS:84 10.3 12.3676700592041 46.0 47.0502319335938 other other text/xml other other other other geonode:MEC_VENT_BUILD_summer In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories. IndoorEnvironmentalQuality Thermalcomfort IEQ Thermalfeeling EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other text/xml other other other other geonode:MEC_VENT_BUILD_winter In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories. IndoorEnvironmentalQuality Thermalcomfort IEQ Thermalfeeling EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other text/xml other other other other geonode:NATURALLY VENTILATED BUILDINGS in summer In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories. IndoorEnvironmentalQuality Thermalcomfort IEQ Thermalfeeling EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other text/xml other other other other geonode:NAT_VENT_BUILD_winter In this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories. IndoorEnvironmentalQuality Thermalcomfort IEQ Thermalfeeling EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other text/xml other other other other geonode:OBM_v02 This layer aims at creating a knowledge base related to research on Occupant Behaviour Modelling. The main Cultural-E contribution referrers to the translation into a GIS format of the provision of open-access available review tables focused on occupants' perception and behaviour in buildings (OPA)(*). In contrast to tables often found in supplementary materials, which are static, the tables used are dynamically growing with new evidence appearing in scientific literature. Authors of original research are welcome to add their published and peer-reviewed research items to these tables. (*) Schweiker M, Andersen RK, Berger C, Carlucci S, Chinazzo G, Edappilly LP, Favero M, Mahdavi A, Piselli C, Bourikas L, Hong T, Dong B, Syndicus M and Hahn J (2021) Dynamic review tables for topical reviews on occupants’ perception and behaviour in buildings. OSF. Available at: osf.io/gnvp2 features building energy EPSG:4326 CRS:84 -31.6483516693115 45.200813293457 27.4207515716553 71.4018936157227 other other text/xml other other other other geonode:Ortler_Alpen_Specialkarte_Meurer-Freytag Historical Map (1:50.000) of the Ortler Alps made by Julius Meurer (1838-1923), Gustav Freytag (?-1938) in 1884. Ortler_Alpen_Specialkarte_Meurer-Freytag GeoTIFF WCS EPSG:4326 CRS:84 10.311452509333568 10.818740953784447 46.31002024963413 46.60177117487301 other other text/xml other other other other geonode:PEB_guidelines This layer provides exemplary national initiatives and guidelines materials for the design and experimentation for high standards of energy efficient buildings such as PEB, ZEB and NZEB. The different national initiatives referenced intends to represent the 4 EU climates which are object of study of CULTURAL-E. PEB_guidelines features cct energy households EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TPS Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018. AI4EBV WTE land cover EPSG:32632 CRS:84 10.290484068720964 11.755584916049663 45.93349650437617 46.94616825349617 other other other other text/xml other other geonode:RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TPT Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018. AI4EBV land cover WTE EPSG:32632 CRS:84 10.311886885505716 11.80284668290898 46.83262831873423 47.84592105160621 other other other other other text/xml other geonode:RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TQS Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018. land cover WTE AI4EBV EPSG:32632 CRS:84 11.579459455542398 13.065657132671932 45.895735251105855 46.92357305779549 other other text/xml other other other other geonode:RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TQT Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018. land cover WTE AI4EBV EPSG:32632 CRS:84 11.622195655498638 13.135258475251888 46.79367068731306 47.82260755947559 other other text/xml other other other other geonode:ST_pop_100m_Aug2015_0200_WD_AllAgeGroups_ras Dynamic population density model for South Tyrol (IT) at 100m of spatial resolution at 2:00 AM (Aug 2015 run). GeoTIFF Population ST_pop_100m_Aug2015_0200_WD_AllAgeGroups_ras WCS August EPSG:25832 CRS:84 10.359946978317863 12.541775092830905 46.1804379006299 47.674555898292844 other other other other text/xml other other geonode:ST_pop_100m_Aug2015_1400_WD_AllAgeGroups_ras Dynamic population density model for South Tyrol (IT) at 100m of spatial resolution at 2:00 PM (Aug 2015 run). ST_pop_100m_Aug2015_1400_WD_AllAgeGroups_ras GeoTIFF Population WCS August EPSG:25832 CRS:84 10.359946978317863 12.50726789768248 46.1804379006299 47.15641766229224 other other other other text/xml other other geonode:ST_pop_100m_Feb2015_1400_WD_AllAgeGroups_ras Dynamic population density model for South Tyrol (IT) at 100m of spatial resolution at 2:00 PM (Feb 2015 run). GeoTIFF Population WCS ST_pop_100m_Feb2015_1400_WD_AllAgeGroups_ras February EPSG:25832 CRS:84 10.359946978317863 12.50726789768248 46.1804379006299 47.15641766229224 other other other other text/xml other other geonode:Sardinia_after_fire On 24 July 2021, a large fire broke out on the Italian island of Sardinia. With strong winds, high temperatures, and dry vegetation, the blaze spread rapidly. In this image, taken about one week after, it is evident the extension area of the fire. credit: produced from ESA remote sensing data fire GeoTIFF WCS EPSG:32632 CRS:84 7.81682739340122 9.11544306761928 39.655752572233034 40.65081233542978 other other other other text/xml other other geonode:Sardinia_before_fire On 24 July 2021, a large fire broke out on the Italian island of Sardinia. With strong winds, high temperatures, and dry vegetation, the blaze spread rapidly. In this image, taken about two days before of the event, is it possible to see the destroyed vegetated area. credit: produced from ESA remote sensing data GeoTIFF WCS EPSG:32632 CRS:84 7.81682739340122 9.11544306761928 39.655752572233034 40.65081233542978 other other other other text/xml other other geonode:SolarIrradiation_MonthlyMean_AnnualAverageValue_kWh Monthly Mean Annual Average of Solar Irradiation in kW/h. WCS GeoTIFF solar irradiation EPSG:25832 CRS:84 10.204491044471874 12.652956473016461 46.047911765375744 47.267646031600044 other other other other text/xml other other geonode:SolarIrradiation_MonthlyMean_CDTE_kWh Annual Mean Value of photovoltaic energy produced for a Cadmium-Tellurid module. GeoTIFF irradiation WCS photovoltaic solar EPSG:25832 CRS:84 10.204491044471874 12.652956473016461 46.047911765375744 47.267646031600044 other other other other text/xml other other geonode:SolarIrradiation_MonthlyMean_PCSI_kWh Annual Mean Value of photovoltaic energy produced for a Polykristallines-Silizium modul. WCS GeoTIFF solar irradiation EPSG:25832 CRS:84 10.204491044471874 12.652956473016461 46.047911765375744 47.267646031600044 other other other other text/xml other other geonode:TI_weighted_sum_bike Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in Canton Ticino (Switzerland). e-mobility EPSG:32632 CRS:84 8.384796357730655 9.06454213047133 45.8179735290265 46.583136270347936 other other other other text/xml other other geonode:TI_weighted_sum_car Layer to represent the most suitable locations for installing charging infrastructure for e-cars in Canton Ticino (Switzerland). e-mobility EPSG:32632 CRS:84 8.38477425343788 9.094043667645508 46.0726456394536 46.58509826032905 other other other other text/xml other other geonode:Tree_Mask_2018 Final Tree Mask of 2018 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2015-2018 as well as slope and elevation information from the EU-DEM. Forest forest map south tyrol EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.127638995280456 other other other other text/xml other other geonode:Tree_Mask_20180 This layer represents the first draft of a forest mask for the province of South Tyrol based on classification of Sentinel-2 images acquired between 2015 and 2018. Forest forest mask south tyrol EPSG:32632 CRS:84 10.29675840479992 12.700747764998992 46.17634141967703 47.30619533266724 other other other other other text/xml other geonode:Tree_Mask_2019 Tree Mask of 2019 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2017-2019 as well as slope and elevation information from the EU-DEM. Changes since mapping began in 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data). Forest forest mask south tyrol EPSG:32632 CRS:84 10.37044294937178 12.50407018765979 46.18796882085018 47.127625590949535 other other other other text/xml other other geonode:Tree_Mask_2020 Tree Mask of 2020 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2018-2020 as well as slope and elevation information from the EU-DEM. Changes since 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data). Forest forest mask south tyrol EPSG:32632 CRS:84 10.37044294937178 12.50407018765979 46.18796882085018 47.127625590949535 other other other other text/xml other other geonode:Tree_Mask_2021 Tree Mask of 2021 for the province of South Tyrol based on the classification of spectral-temporal metrics of Sentinel-2 images between 2019-2021 as well as slope and elevation information from the EU-DEM. Changes since 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data). Forest forest map south tyrol EPSG:32632 CRS:84 10.37044294937178 12.50407018765979 46.18796882085018 47.127625590949535 other other other other text/xml other other geonode:Tree_Mask_2022 Tree Mask of 2022 for the province of South Tyrol based on on the classification of spectral-temporal metrics of Sentinel-2 images between 2020-2022 as well as slope and elevation information from the EU-DEM. Changes since 2018 were accounted for by assessing vegetation changes using the NDVI threshold for the respective year (based on Sentinel-2 data). Forest forest map south tyrol EPSG:32632 CRS:84 10.37044294937178 12.50407018765979 46.18796882085018 47.127625590949535 other other other other text/xml other other geonode:World_Land_Cover_Himalayas_2015_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Land_Cover_Himalayas_2016_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Land_Cover_Himalayas_2017_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Land_Cover_Himalayas_2018_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Land_Cover_Himalayas_2019_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Land_Cover_Himalayas_2020_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Land_Cover_Province_2015_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Land_Cover_Province_2016_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Land_Cover_Province_2017_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Land_Cover_Province_2018_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Land_Cover_Province_2019_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Land_Cover_Province_2020_v1 Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Himalayas_2015_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Himalayas_2016_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Himalayas_2017_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Himalayas_2018_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Himalayas_2019_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Himalayas_2020_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:32645 CRS:84 84.9485680623775 87.10013630215465 27.019735585226233 28.928245496280947 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Province_2015_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Province_2016_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Province_2017_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Province_2018_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Province_2019_v1 The World Terrestrial Ecosystems map of the AI4EBV project. landcover ecosystem EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:World_Terrestrial_Ecosystems_Province_2020_v1 The World Terrestrial Ecosystems map of the AI4EBV project. ecosystem landcover EPSG:3035 CRS:84 10.290372838936035 13.13637519318898 45.89485552204146 47.846024287617794 other other other other text/xml other other geonode:alpineconvention_2f19551311fb14f3075b85124055c19d Perimeter of the Alpine Convention 2023 alpine convention perimeter alpineconvention_2f19551311fb14f3075b85124055c19d features EPSG:32632 CRS:84 4.573826984976227 16.60201848772918 43.26008976710103 48.591311460909935 other other other other text/xml other other geonode:altern_fuel_10 iMONITRAF layer, Alternative fuel pag 10 of the document features altern_fuel_10 EPSG:4326 CRS:84 2.85388994216919 17.1608009338379 42.9745788574219 50.555492401123 other other other other text/xml other other geonode:archivio_1 Archivio Tirolese per la documentazione e l'arte fotografica di Lienz (TAP): - Collezione Lisl Gaggl-Meirer (Paesaggio, montagna; Tirolo Orientale; 1970-1990) - Collezione Klebelsberg, Istituto di Geologia, Università di Innsbruck (Paesaggio, montagna, militari; Dolomiti; 1907-1910) - Collezione Hans Peter Falkner (Città, Lienz; ca. 1965-1985) - Collezione Foto Baptist (Paesaggio, montagna; Tirolo Orientale; 1965-1975) foto archivio archive features picture EPSG:4326 CRS:84 11.3055591583252 11.8031244277954 46.4780807495117 46.9187622070312 other other other other text/xml other other geonode:auf_den_spuren_der_tracks Auf den spuren der tracks in Bletterback Park park track features EPSG:4326 CRS:84 11.4030799865723 11.4245204925537 46.359748840332 46.3696174621582 other other other other other other text/xml other other other other other other text/xml geonode:bdi_LC100m_v3_2019_classified_copernicus_ras Land classification over Burundi (from Copernicus Land Cover product). copernicus GeoTIFF WCS EPSG:4326 CRS:84 28.999999999999968 30.85119047619044 -4.470238095238079 -2.308531746031738 other other other other text/xml other other EO_CDR:bdi_adm2_test Burundi Level 2 administrative boundaries. bdi_adm2_test features EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other EO_CDR:bdi_adm3_reference_v01 The dataset represents the collines of Burundi. collines burundi EPSG:32735 CRS:84 28.99034059460317 30.866003467679295 -4.483274818120883 -2.2971208117248914 other other other other text/xml other other EO_CDR:bdi_adm_adm0_igebu_ocha_itos_2017_utm35s The dataset represents the international boundaries of Burundi. bdi_adm_adm0_igebu_ocha_itos_2017_utm35s features EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other EO_CDR:bdi_adm_adm1_igebu_ocha_2017_utm35s The dataset represents the provinces of Burundi. features bdi_adm_adm1_igebu_ocha_2017_utm35s EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other EO_CDR:bdi_adm_adm2_igebu_ocha_2017_utm35s The dataset represents the communes of Burundi. bdi_adm_adm2_igebu_ocha_2017_utm35s features EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other EO_CDR:bdi_all_roads All roads and footways of Burundi from OpenStreetMap (OSM). features bdi_all_roads EPSG:4326 CRS:84 29.0021057128906 30.8435230255127 -4.46676969528198 -2.32193064689636 other other other other text/xml other other EO_CDR:bdi_bldg_taxonomy_s3_idom_pp_commune Taxonomy of buildings in Burundi, per each commune (IDOM). burundi taxonomy buildings EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other EO_CDR:bdi_bldg_taxonomy_s3_idom_pp_province Taxonomy of buildings in Burundi, per each province (IDOM). burundi taxonomy buildings EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other geonode:bdi_bldgs_buildings_Worldpop_v2_0_count_ras_100m his layer shows gridded buildings with 100m resolution for Burundi. It was extracted from Gridded maps of building patterns throughout sub-Saharan Africa, version 2.0. This raster contains counts of buildings that fall within a grid cell. Each buildings was counted in the grid cell that contained the centroid of its building footprint burundi buildings EPSG:4326 CRS:84 29.000416566 30.850416559 -4.469583114 -2.308749789 other other other other text/xml other other EO_CDR:bdi_drive_roads Year-round drivable roads of Burundi from OpenStreetMap (OSM). features bdi_drive_roads EPSG:4326 CRS:84 29.0003051757812 30.8521842956543 -4.44812345504761 -2.31427049636841 other other other other text/xml other other EO_CDR:bdi_edu_ecoles_v1_pnt_bcg This layer shows the location of schools in Burundi. The data originates from BCG. bdi_edu_ecoles_v1_pnt_bcg features EPSG:32735 CRS:84 10.236582579843425 43.76341742015655 -80.01343130977129 0.0 other other other other text/xml other other EO_CDR:bdi_energy_dams_aquastat_pp Dam locations in Burundi extracted from Aquastat Dam database for Africa. AQUASTAT gathers detailed information about dams in each country, especially on location, height, reservoir capacity, surface area and main purpose. http://www.fao.org/aquastat/en/databases/dams bdi_energy_dams_aquastat_pp features EPSG:4326 CRS:84 29.2189311981201 30.7956008911133 -3.923663854599 -2.37557315826416 other other other other text/xml other other EO_CDR:bdi_env_protectedareas_wdpa_pol This layer shows protected areas in Burundi according to the World Database of protected areas. burundi nature protected EPSG:4326 CRS:84 29.1872291564941 30.8539199829102 -4.32354593276978 -2.31806802749634 other other other other text/xml other other geonode:bdi_gmted2010_stdev0 Terrain ruggedness (elevation standard deviation) over Burundi at 7.5 arc-sec (225 m) of spatial resolution. Cropped from the original GMTED2010 global topographic elevation model from USGS/NGA. GeoTIFF WCS EPSG:4326 CRS:84 29.001944444444444 30.849861111110815 -4.468888888888544 -2.310555555555556 other other other other text/xml other other EO_CDR:bdi_haz_landslides_pol_s1_eurac_pp_geounits_l2 Lithological units of Burundi (second step of aggregation). bdi_haz_landslides_pol_s1_eurac_pp_geounits_l2 geological units features EPSG:32735 CRS:84 28.989911892900004 30.867526925912554 -4.4806359260503985 -2.2909966611646606 other other other other text/xml other other EO_CDR:bdi_haz_landslides_pol_s1_eurac_pp_geounits_l3 Lithological units of Burundi (third step of aggregation). geological units features bdi_haz_landslides_pol_s1_eurac_pp_geounits_l3 EPSG:32735 CRS:84 28.989911892900004 30.867526925912554 -4.4806359260503985 -2.2909966611646606 other other other other text/xml other other EO_CDR:bdi_haz_landslides_pol_s1_eurac_pp_geounits_l4 Lithological units of Burundi (fourth step of aggregation). geological units features bdi_haz_landslides_pol_s1_eurac_pp_geounits_l4 EPSG:32735 CRS:84 28.989911892900004 30.867526925912554 -4.4806359260503985 -2.2909966611646606 other other other other text/xml other other EO_CDR:bdi_haz_landslides_pol_s4_eurac_pp_prio_areas TBD bdi_haz_landslides_pol_s4_eurac_pp_prio_areas features EPSG:32735 CRS:84 29.048020236705977 29.61073820677718 -3.990762093858628 -2.583050102649156 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_april The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. GeoTIFF burundi bdi_haz_landslides_ras_s4_eurac_re_april April landslides WCS EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_august The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. landslides GeoTIFF bdi_haz_landslides_ras_s4_eurac_re_august burundi WCS August EPSG:32735 CRS:84 28.999723145995866 30.856721943006125 -4.4724335572914775 -2.308156014490136 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_december The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. landslides GeoTIFF December bdi_haz_landslides_ras_s4_eurac_re_december WCS burundi EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_february The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. GeoTIFF burundi bdi_haz_landslides_ras_s4_eurac_re_february landslides WCS February EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_january The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. WCS bdi_haz_landslides_ras_s4_eurac_re_january burundi GeoTIFF landslides January EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_july The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. GeoTIFF burundi July bdi_haz_landslides_ras_s4_eurac_re_july landslides WCS EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_june The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. GeoTIFF burundi June landslides bdi_haz_landslides_ras_s4_eurac_re_june WCS EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_march The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. WCS burundi March GeoTIFF landslides bdi_haz_landslides_ras_s4_eurac_re_march EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_may The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. GeoTIFF burundi May bdi_haz_landslides_ras_s4_eurac_re_may landslides WCS EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_november The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. GeoTIFF burundi bdi_haz_landslides_ras_s4_eurac_re_november landslides WCS November EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_october The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. burundi landslides WCS bdi_haz_landslides_ras_s4_eurac_re_october GeoTIFF October EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other geonode:bdi_haz_landslides_ras_s4_eurac_re_september The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included. GeoTIFF burundi September bdi_haz_landslides_ras_s4_eurac_re_september landslides WCS EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other other other text/xml other other EO_CDR:bdi_haz_landslides_ras_s5_eurac_re National-scale unclassified landslide susceptibility map for Burundi. natural hazards burundi landslides EPSG:32735 CRS:84 28.999723145995862 30.857261545197105 -4.4724335572914775 -2.3081545471449956 other other text/xml other other other other geonode:bdi_haz_landslides_ras_s5_eurac_re_prio_areas Landslide susceptibility map for the priority areas of Burundi that incorporates landslide release susceptibilities and potential runout paths. natural hazard WCS landslides bdi_haz_landslides_ras_s5_eurac_re_prio_areas EPSG:32735 CRS:84 29.05080198764252 29.60792038430837 -3.9837935557729067 -2.5899787565431738 other other other text/xml other other other EO_CDR:bdi_heal_health_sites_pnt_fosa_gps_v4_minesante_utm35s This layer contains the health sites in Burundi classified by type. The data source is the Burundi Ministry of Health. bdi_heal_health_sites_pnt_fosa_gps_v4_minesante_utm35s EPSG:32735 CRS:84 29.046853993528366 30.83904082744847 -4.436701908296792 -2.341469576054239 other other other other text/xml other other EO_CDR:bdi_health_facilities_access Accessibility to nearest health facility on drivable roads in Burundi. bdi_health_facilities_access features EPSG:4326 CRS:84 28.9989585876465 30.8481159210205 -4.45980596542358 -2.31337428092957 other other other other text/xml other other geonode:bdi_lc100m_v3_2019_cropland_copernicus_utm35s Land classified as cropland over Burundi (from Copernicus Land Cover product). bdi_lc100m_v3_2019_cropland_copernicus_utm35s GeoTIFF WCS EPSG:32735 CRS:84 28.99556338527807 30.860535320753346 -4.477725824061256 -2.3046991128293652 other other other other text/xml other other EO_CDR:bdi_osm_discr_class OpenStreetMap intrinsic complete analysis by discrete classification of its collines using terrain ruggedness and gridded population estimates as auxiliary predictors. osm intrinsic completeness bdi_osm_discr_class features EPSG:32635 CRS:84 28.999605878237837 30.85669176910313 -4.472437996406693 -2.3079406850409434 other other other other text/xml other other EO_CDR:bdi_pop2020_worldpop_aggregated_collinesbcg2020 100m population distribution of Burundi by Worldpop aggregated to colline level. bdi_pop2020_worldpop_aggregated_collinesbcg2020 features EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other EO_CDR:bdi_pop_adm2_isteebu_2019_pol From the third general population and housing census of Burundi made by ISTEEBU Institute in 2008. bdi_pop_adm2_isteebu_2019_pol features EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other EO_CDR:bdi_pop_percommune_2021_isteebu_unfpa_pol_pp Population estimation by UNFPA with Institut de Statistiques et d'Etudes Economiques du Burundi (ISTEEBU). Burundi administrative level 0-2 2021 sex and age disaggregated projections from 2008 population census statistics Population distribution EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other geonode:bdi_pop_ppp_2020_UNadj_constrained_Worldpop_ras_12092020 Estimated total number of people per grid-cell at a resolution of 3 arc seconds. bdi_pop_ppp_2020_UNadj_constrained_Worldpop_ras_12092020 WCS GeoTIFF EPSG:4326 CRS:84 29.000416566 30.850416559 -4.469583114 -2.308749789 other other other other text/xml other other geonode:bdi_pop_worldpop100mrescaled_ras_pp Gridded population estimates from WorldPop (10.5258/SOTON/WP00682) over Burundi calibrated to match the 2021 population projections by commune by ISTEEBU/UNFPA (https://data.humdata.org/dataset/burundi-administrative-level-0-2-population-statistics-2018). bdi_pop_worldpop100mrescaled_ras_pp WCS GeoTIFF EPSG:32735 CRS:84 28.995981100271887 30.85964064146694 -4.47718386843126 -2.304917632183345 other other other other text/xml other other geonode:bdi_pop_worldpop_2020_rescaled_to_2019isteebuadm2_ras_ Gridded population estimates from WorldPop (10.5258/SOTON/WP00682) over Burundi calibrated to match the 2019 population projections by commune by ISTEEBU/UNFPA (https://data.humdata.org/dataset/burundi-administrative-level-0-2-population-statistics-2018). bdi_pop_worldpop_2020_rescaled_to_2019isteebuadm2_ras_ GeoTIFF WCS EPSG:4326 CRS:84 28.995416566018918 30.85958322563198 -4.477083113971622 -2.3045831221918918 other other other other text/xml other other EO_CDR:bdi_powerplants Power plants in Burundi with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006). https://datacatalog.worldbank.org/dataset/burundi-power-plants bdi_powerplants features EPSG:4326 CRS:84 29.1174983978271 29.622501373291 -3.95530009269714 -2.88469982147217 other other text/xml other other other other EO_CDR:bdi_risk_aal_py_s3_eurac_prov Average Annual Loss (AAL) measured in USDs, estimated for multiple hazards over the provinces of Burundi. bdi_risk_aal_py_s3_eurac_prov provinces loss risk aal features EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other text/xml other other other other EO_CDR:bdi_risk_aal_py_s4_eurac_comm Average Annual Loss (AAL) measured in USDs, estimated for multiple hazards over the communes of Burundi. communes loss risk bdi_risk_aal_py_s4_eurac_comm aal features EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other EO_CDR:bdi_settl_extents_built_up_area_pol Built-up areas (BUAs) A built-up area (BUA) is generally an area of urbanisation with moderately-todensely- spaced buildings and a visible grid of streets and blocks. Built up areas are characterized by contours with an area greater than or equal to 400,000 meters square that maintains a building density of thirteen or more across the entire area. The settlement extents and classification are derived solely from the building footprints, and no ancillary datasets are used. The centerpoints of building footprint features are converted to a 3 arc-second raster grid of building densities. Shell-up contours are then generated using the building density grid to delineate settled vs non-settled areas. The shell-up method includes contours that start at the lower bounds, but includes all grid cells with building densities to the upper bounds of the grid. For example, a shell up contour of 10 would include all grid cells with a building density of 10 or more. Contours with a building density of one or more are used to create the settlement extent polygons. The GRID3 Burundi settlement extents characterised building density into three (3) classes: built-up areas (bua_extents), small settlement areas (ssa_extents), and hamlets (hamlet_extents) built-up area settlements burundi EPSG:4326 CRS:84 29.0579662322998 30.8474140167236 -4.43932199478149 -2.32789397239685 other other other other text/xml other other EO_CDR:bdi_settl_extents_hamlets_pol A hamlet is a collection of several compounds or sleeping houses in isolation from small settlements or urban areas. Hamlets are characterized as a collection of lowdensity settlements between one and 50 buildings and falls within 65 meters of one another. settlements burundi EPSG:4326 CRS:84 28.9923496246338 30.8565845489502 -4.47444295883179 -2.30216836929321 other other other other text/xml other other EO_CDR:bdi_settl_extents_small_settlement_area_pol A small settlement (SSA) is a settled area of permanently inhabited structures and compounds of roughly a few hundred to a few thousand inhabitants. The housing pattern in SSAs is an assemblage of family compounds adjoining other similar habitations. Small settlement areas are characterized by having 50 or more buildings and are not a BUA. bdi_settl_extents_small_settlement_area_pol features EPSG:4326 CRS:84 29.0200309753418 30.8530731201172 -4.46833992004395 -2.3096296787262 other other other other text/xml other other EO_CDR:bdi_stle_places_nga_12jul2021_p Geographic names of populated places in Burundi by NGA Geonet Names Server (NGA). Last updated: 12. July 2021. settlements burundi EPSG:4326 CRS:84 29.0131530761719 30.8449687957764 -4.46383142471313 -2.31636881828308 other other other other text/xml other other EO_CDR:bdi_topology_indicators_edges The layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality. bdi_topology_indicators_edges features EPSG:4326 CRS:84 28.9989280700684 30.8541622161865 -4.45980167388916 -2.31421232223511 other other other other text/xml other other EO_CDR:bdi_topology_indicators_edges_adm1 The layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality. The data has been computed separately on each province of Burundi, then merged on the same file. bdi_topology_indicators_edges_adm1 features EPSG:4326 CRS:84 29.0000648498535 30.8481121063232 -4.43627405166626 -2.3134913444519 other other other other text/xml other other EO_CDR:bdi_topology_indicators_main_edges The layer contains the Betweenness Centrailty indicator computed on the edges of the OpenStreetMap (OSM) main roads (up to tertiary). bdi_topology_indicators_main_edges features EPSG:4326 CRS:84 29.0330066680908 30.8493995666504 -4.4576530456543 -2.33004927635193 other other other other text/xml other other EO_CDR:bdi_touristic_sites_pnt_bcg_iom This layers shows touristic sites in Burundi. The data was provided by BCG. bdi_touristic_sites_pnt_bcg_iom features EPSG:32735 CRS:84 29.034171273964603 30.55967184401572 -4.418122337682959 -2.4952702477025053 other other other other text/xml other other EO_CDR:bdi_trans_roads_bridges_osm_ln_p Bridges of Burundi (OSM). OSM Download from September 2020. bdi_trans_roads_bridges_osm_ln_p features EPSG:4326 CRS:84 29.0333423614502 30.5743503570557 -4.44842386245728 -2.51653790473938 other other other other text/xml other other EO_CDR:bdi_trans_roads_ln_dsnisv3_minesante This layer contains primary and secondary roads in Burundi catagorising the roads into three classes and providing information on surface and usability. The data originates from the Burundi Ministry of Health. bdi_trans_roads_ln_dsnisv3_minesante features EPSG:32735 CRS:84 29.008266566672038 30.860276900313213 -4.452214497960899 -2.327149312649401 other other other other text/xml other other EO_CDR:bdi_vuln_seva_py_s2_glomos_prov_re Vulnerability indices over Burundi for each province. provinces burundi vulnerability EPSG:32735 CRS:84 28.990337223920097 30.8666880903388 -4.483274827351837 -2.2971189544811113 other other other other text/xml other other EO_CDR:bdi_vuln_seva_py_s4_glomos_coll_re Vulnerability indices over Burundi at colline level (where available). collines burundi vulnerability EPSG:32735 CRS:84 28.99034059460317 30.866003467679295 -4.483274818120883 -2.2971208117248914 other other other other text/xml other other geonode:bletterbachshclucht_tracks Bletterbachshclucht Parck tracks park track features EPSG:4326 CRS:84 11.4073238372803 11.4552335739136 46.3470191955566 46.3723526000977 other other other other other other other other other text/xml other other text/xml other EO_CDR:burundi_grid Electricity transmission network of Burundi (World Bank+REGIDISO). https://energydata.info/dataset/burundi-electricity-transmission-network-2007 burundi_grid features EPSG:4326 CRS:84 29.0175933837891 30.5604152679443 -4.35298776626587 -2.57117891311646 other other other other text/xml other other geonode:census_tracts_bolzano_2011_exposure census_tracts_bolzano_2011_exposure features bolzano RETURN EPSG:32632 CRS:84 11.273221307883968 11.434270791283508 46.44256726442115 46.531940518456395 other other other other text/xml other other geonode:changes_monthly_damages_latest Forest changes between 2020 and 2026 at a monthly scale. The outputs shown here are based on the analysis of Sentinel 2 time series. The date corresponds to the first date at which a change was detected. Forest changes_monthly_damages_latest GeoTIFF WCS EPSG:25832 CRS:84 10.370413873759015 12.504112232742086 46.18797724335426 47.127639035888166 other other other other text/xml other other geonode:changes_yearly_damages_latest Forest changes between 2020 and 2026 at a yearly scale. The outputs shown here are based on the analysis of Sentinel 2 time series. The date corresponds to the first date at which a change was detected. Forest changes_yearly_damages_latest GeoTIFF WCS EPSG:25832 CRS:84 10.370413873759015 12.504112232742086 46.18797724335426 47.127639035888166 other other other other text/xml other other geonode:climate_class_nut0 Climate classification in european countries. The climates were extracted by the Koppen-Geiger classification features climate cct climate_class_nut0 EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other text/xml other other other other geonode:cntr_bn_01m_2020_3035_897b59ec32758c492f9e2d5d379c1677 europe features cntr_bn_01m_2020_3035_897b59ec32758c492f9e2d5d379c1677 EPSG:3035 CRS:84 -180.0 180.0 -90.0 90.0 other other other other text/xml other other geonode:comuni_TN_BZ Layer to display municipalities labels in Trento and Bolzano provinces features municipality comuni_TN_BZ label EPSG:32632 CRS:84 10.356887450269356 12.50402823778642 45.63857859904044 47.12766182576525 other other other other other other other other text/xml other other text/xml other other geonode:cooking_household_gwh This Layer shows the share of fuels in the final energy consumption in the residential sector for coocking. The Frequency is annual. households energy cooking EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:crownshapes_f1 This data set includes crown shapes which are extracted from the Canopy Height Model with the use of the ForestTools package in R. The related CHM and RGB point cloud are included in related resources. In the attributes, the average and mean RGB values of the corresponding segmented tree in the RGB point cloud are included, together with the tree species, treeID, coordinates of the crown tip, tree height and crown area. features Crown Shapes crownshapes_f1 Tree Detection Forest EPSG:32632 CRS:84 10.57712432411548 10.579497021895117 46.67650915044178 46.67839788448524 other other other other text/xml other other geonode:crownshapes_f2 This data set includes crown shapes which are extracted from the Canopy Height Model with the use of the ForestTools package in R. The related CHM and RGB point cloud are included in related resources. In the attributes, the average and mean RGB values of the corresponding segmented tree in the RGB point cloud are included, together with the tree species, treeID, coordinates of the crown tip, tree height and crown area. crownshapes_f2 features Crown Shapes Tree detection Forest EPSG:32632 CRS:84 10.611094016451421 10.613775928279537 46.69332605275291 46.6955300032533 other other other other text/xml other other geonode:crownshapes_f5 This data set includes crown shapes which are extracted from the Canopy Height Model with the use of the ForestTools package in R. The related CHM and RGB point cloud are included in related resources. In the attributes, the average and mean RGB values of the corresponding segmented tree in the RGB point cloud are included, together with the treeID, the coordinates of the crown tip, tree height and crown area. crownshapes_f5 features Crown Shapes Tree detection Forest EPSG:32632 CRS:84 10.686466651270168 10.690119867487889 46.73794806045066 46.74014185677327 other other other other text/xml other other geonode:dps_urban_monitoring_old Low-cost cloud-connected position-enriched sensors for mobile monitoring of several environmental parameters have been tested in the city of Bolzano (Italy), proving their suitability in identifying the spatial variability of the local climate in relation to the urban morphology, and for highlighting the presence of urban heat island. An exploratory field campaign has been carried out in May 2021 to monitor the diurnal evolution of the microclimate conditions (Tair/RH fields). Data have been acquired performing three sessions during daytime on weekdays: at morning (i.e. 08:30-10:30), noon (i.e. 12:00-14:00), and afternoon (i.e. 16:00-18:00). The measurements have been carried out in 8 days, chosen for the stationary weather conditions (i.e. clear sky and absence of wind). The selected pathway has a length of 9 km, starting and ending at NOI Techpark, crosses the city center and reaches the northern part of the city. It is specifically designed to monitor areas of Bolzano characterized by different land use, urban morphology, and human activities. urban solar features temperature dps4eslab sensor humidity EPSG:4326 CRS:84 11.082055059 11.351882799 46.032395182 46.512605931 other other other other text/xml other other geonode:ecological_network_red_deer_south_tyrol Dieser Datensatz zeigt ein ökologisches Netzwerkmodell für den Rothirsch in Südtirol. Es umfasst sowohl bestehende als auch potenzielle Querungsmöglichkeiten und soll eine Orientierung für die Definition von konkreten Korridoren darstellen. Auf lokaler Ebene kann das Modell von der Realität abweichen, weshalb die Nutzung durch Wildtiere vor Ort kontrolliert werden muss. Das Modell besitzt keine rechtliche Gültigkeit und ist noch keinem wissenschaftlichen Peer-Review-Prozess unterzogen worden.   Questo dataset mostra un modello di rete ecologica per il cervo in Alto Adige. Include sia i passaggi esistenti che quelli potenziali, e può essere usato come riferimento per definire corridoi concreti. A livello locale, il modello può differire dalla realtà, pertanto è necessario controllare l'utilizzo da parte della fauna selvatica in loco. Il modello non ha validità giuridica e non è stato ancora sottoposto ad alcun processo di revisione "Peer-review" scientifica.   File: Ecological_network_Red_deer_South_Tyrol.shp Bericht zur Erstellung des Modells (Englisch): https://www.datocms-assets.com/31538/1737642672-d2-3-1_project-of-ecological-network-south-tyrol.pdf  Bericht zu technischen Verbesserungsvorschlägen: https://www.datocms-assets.com/31538/1770718944-d-2-5-1_technischer-vorschlag_okologisches-netzwerk_hirsch_sudtirol.pdf  Proposta tecnica: https://www.datocms-assets.com/31538/1757008589-d-2-5-1_proposta-tecnica_rete-ecologica-per-il-cervo_alto-adige.pdf  Eurac project website: https://www.eurac.edu/en/institutes-centers/institute-for-regional-development/projects/plantoconnect  Red Deer corridor features South-Tyrol ecological_network_red_deer_south_tyrol Ecological Connectivity EPSG:3857 CRS:84 10.186462982232102 12.70138918219613 46.05790494025893 47.24890361234763 other other other other text/xml other other geonode:electricity_household_gwh Layer about Electricity Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset. features households Electricty EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:electricity_price Electricity prices components for household consumers -annual data (from 2007 onwards). Annual values taken from the Eurostat dataset with a national level resolution. Household price Electricty EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:elevation Elevation (Hypsometry) of South Tyrol. cartography defibrillator drone EPSG:25832 CRS:84 10.373096503278832 12.504124575946575 46.187665102906365 47.12796562783076 other other other other other text/xml other geonode:energy_cultures The information provided intends to support a profiling exercise of users’ domestic energy use and how these variations are translated into different domestic energy-intensity practices across EU territories. This layer includes descriptive information on the energy demand dynamics at household level by means of taking into account the cultural-climatic aspects which characterise the EU climatic areas as part of this research. energy_culutral_drivers features cct energy households EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:eusalp_alpspace_perimeter_001d862197332066b6fd6f7566b83908 Perimeter of the Interreg Alpine Space Programme (2022) eusalp_alpspace_perimeter_001d862197332066b6fd6f7566b83908 features alpine space EPSG:3035 CRS:84 3.092552726499899 17.507259104151906 42.8436524243783 50.56388852168553 other other other other text/xml other other geonode:f1_canopy_height_model_260324 The canopy height model (CHM) of the site F1, part of the long-term monitoring sites of the LTER project. The data is based on a LiDAR drone flight (sensor: Riegl MiniVUX-1UAV). The creation of the CHM is done in R with the package lidR by first normalizing the height of the point cloud and then rasterizing it. WCS Canopy Height Model f1_canopy_height_model_260324 GeoTIFF Forest EPSG:32632 CRS:84 10.576031263617242 10.579896848173005 46.67626448271674 46.67876729082815 other other other other text/xml other other geonode:f2_canopy_height_model_260324 The canopy height model (CHM) of the site F2, part of the long-term monitoring sites of the LTER project. The data is based on a LiDAR drone flight (sensor: Riegl MiniVUX-1UAV). The creation of the CHM is done in R with the package lidR by first normalizing the height of the point cloud and then rasterizing it. f2_canopy_height_model_260324 WCS Canopy Height Model GeoTIFF Forest EPSG:32632 CRS:84 10.61095143807965 10.614385029143856 46.6930512038824 46.6955705811647 other other other other text/xml other other geonode:f5_canopy_height_model_251022 The canopy height model (CHM) of the site F5, part of the long-term monitoring sites of the LTER project. The data is based on a LiDAR drone flight (sensor: Riegl MiniVUX-1UAV). The creation of the CHM is done in R with the package lidR by first normalizing the height of the point cloud and then rasterizing it. WCS Canopy Height Model f5_canopy_height_model_251022 GeoTIFF Forest EPSG:32632 CRS:84 10.686457982808484 10.69037342069351 46.73766643445786 46.74030017352597 other other other other text/xml other other geonode:farmhouses_line Layer to represent the "tour" to eight case study buildings of exemplary energy efficient interventions in historic buildings. All buildings are retrofitted farm houses located in South Tyrol province. historic buildings virtual tour EPSG:4326 CRS:84 10.8784198760986 12.236780166626 46.3137969970703 46.8334922790527 other other other other text/xml other other geonode:farmhouses_point_new Layer to represent the position of eight case study buildings of exemplary energy efficient interventions in historic buildings. All buildings are retrofitted farm houses located in South Tyrol province. historic buildings energy refurbishment EPSG:4326 CRS:84 10.8784198760986 12.236780166626 46.3137969970703 46.8334922790527 other other other other other text/xml other EO_CDR:forestprotectivefunction_polygon Surfaces with potential forest auto- and hetero-protective function. features forestprotectivefunction_polygon EPSG:25832 CRS:84 10.393050311228347 12.458072980047724 46.19299158632682 47.10703861229649 other other other other text/xml other other geonode:gas_household_gwh Layer about Gas Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset. features gas houshold EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:gas_price Layer about Gas prices components for household consumers - annual data, derived by Eurostat datasets at country level. features gas price EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:greening_2019 Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data. greening_2019 WCS GeoTIFF EPSG:25832 CRS:84 10.370413873759015 12.504112232742086 46.18797724335426 47.127639035888166 other other other other text/xml other other geonode:greening_2020 Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data. greening_2020 WCS GeoTIFF EPSG:25832 CRS:84 10.370413873759015 12.504112232742086 46.18797724335426 47.127639035888166 other other other other text/xml other other geonode:greening_2021 Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data. greening_2021 WCS GeoTIFF EPSG:25832 CRS:84 10.370413873759015 12.504112232742086 46.18797724335426 47.127639035888166 other other other other text/xml other other geonode:greening_2022 Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data. greening_2022 WCS GeoTIFF EPSG:25832 CRS:84 10.370413873759015 12.504112232742086 46.18797724335426 47.127639035888166 other other other other text/xml other other geonode:greening_2023 Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data. greening_2023 WCS GeoTIFF EPSG:25832 CRS:84 10.370413873759015 12.504112232742086 46.18797724335426 47.127639035888166 other other other other text/xml other other geonode:greening_2024 Information on greening trends in forest areas that had undergone prior changes derived from Sentinel-2 image data. greening_2024 WCS GeoTIFF EPSG:25832 CRS:84 10.370413873759015 12.504112232742086 46.18797724335426 47.127639035888166 other other other other text/xml other other EO_CDR:hotosm_bdi_airports_points Airports of Burundi (OSM). hotosm_bdi_airports_points features EPSG:4326 CRS:84 29.3160305023193 30.2529296875 -4.04065990447998 -2.53808760643005 other other other other text/xml other other EO_CDR:hotosm_bdi_education_facilities_points Education facilities of Burundi (OSM). hotosm_bdi_education_facilities_points features EPSG:4326 CRS:84 28.9909915924072 30.4682559967041 -4.34746599197388 -2.46590089797974 other other other other text/xml other other EO_CDR:hotosm_bdi_health_facilities_points Health facilities in Burundi (OSM). hotosm_bdi_health_facilities_points features EPSG:4326 CRS:84 29.0927639007568 30.5599479675293 -4.35514068603516 -2.43888235092163 other other other other text/xml other other EO_CDR:hotosm_bdi_populated_places_points This layer contains populated places extracted from OpenStreetMap 01 July 2021. settlements burundi place EPSG:4326 CRS:84 28.9910621643066 30.8698329925537 -4.48755836486816 -2.34265041351318 other other other other text/xml other other EO_CDR:hotosm_bdi_sea_ports_points Sea ports of Burundi (OSM). hotosm_bdi_sea_ports_points features EPSG:4326 CRS:84 29.3438987731934 29.343900680542 -3.37767028808594 -3.37767004966736 other other other other text/xml other other geonode:hsi_reddeer The Habitat Suitability Index for red deer in South Tyrol was created to model the ecological network for this species. The habitat suitability was resampled to a cell size of 20 m by the bilinear method in ArcGIS. The area of investigation is the administrative boundary of South Tyrol with a 15 km buffer. South Tyrol GeoTIFF WCS hsi_reddeer Red deer EPSG:3035 CRS:84 10.164981104359487 12.722737429329275 46.03747476490336 47.26918671610433 other other other other text/xml other other geonode:hydro_station_ado_32632 Hydrological stations with discharge values for ADO project discharge river hydro_station_ado_32632 features water cct EPSG:32632 CRS:84 3.5870695659532816 16.97423184722133 43.64845410214505 50.05916807444189 other other other other text/xml other other geonode:hydro_station_wtl_ado_32632 Hydrological station with Water level values for ADO project hydro_station_wtl_ado_32632 water level cct features ADO EPSG:32632 CRS:84 5.734663940848378 16.97423184722133 43.660243988747155 50.062155715117406 text/xml other other other other other other geonode:in_der_bletterbachschl_track In Der Bletterback park track track features EPSG:4326 CRS:84 11.3951244354248 11.4171085357666 46.3610191345215 46.382453918457 other other other other text/xml other other geonode:indicator_adaptive_capacity Adaptive capacity indicator of the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project vulnerability indicator_adaptive_capacity Alps Eurac features snow tourism destinations EPSG:3035 CRS:84 3.096737675840527 17.507156809022316 42.84370617682738 50.563883087432174 other other other other text/xml other other geonode:indicator_exposure Exposure indicator for the Vulnerability Map of Snow Toursim Destinations - BeyondSnow project vulnerability indicator_exposure Alps Eurac features snow tourism destinations EPSG:3035 CRS:84 3.096737675840527 17.507156809022316 42.84370617682738 50.563883087432174 other other other other text/xml other other geonode:indicator_potential_impacts Potential impacts indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project vulnerability Alps Eurac features indicator_potential_impacts snow tourism destinations EPSG:3035 CRS:84 3.096737675840527 17.507156809022316 42.84370617682738 50.563883087432174 other other other other text/xml other other geonode:indicator_sensitivity Sensitivity indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project vulnerability Alps Eurac features indicator_sensitivity snow tourism destinations EPSG:3035 CRS:84 3.096737675840527 17.507156809022316 42.84370617682738 50.563883087432174 other other other other text/xml other other geonode:industries_line20 Layer to represent the "tour" to eight case study buildings of exemplary energy efficient interventions in historic buildings. The case studies represent historic buildings with a particular use, such as for industry, trade or education. historic buildings virtual tour EPSG:4326 CRS:84 -4.7285213470459 14.0395936965942 41.6536102294922 55.7031173706055 other other other other text/xml other other geonode:industries_point_new Layer to represent the position of eight case study buildings of exemplary energy efficient interventions in historic buildings. The case studies represent historic buildings with a particular use, such as for industry, trade or education. historic buildings energy refurbishment EPSG:4326 CRS:84 -4.7285213470459 14.0395936965942 41.6536102294922 55.7031173706055 other other other other text/xml other other geonode:land_use UAS Feasibility Land Use of South Tyrol. cartography defibrillator drone EPSG:25832 CRS:84 10.373091877979117 12.504072098599668 46.18770504752999 47.12796970469726 other other other other text/xml other other geonode:landuse0 This layer shows the land use land cover for the Transalp study area Agordino-Valle del Cordevole. land cover landuse Veneto EPSG:3003 CRS:84 11.764621786338369 12.174087836126871 46.16230194046746 46.55080671613287 other other other other text/xml other other geonode:lau_rg_01m_2021_3035_alpinespace405ca54aa9ce features lau_rg_01m_2021_3035_alpinespace405ca54aa9ce Alps vulnerability Eurac snow tourism destinations EPSG:3035 CRS:84 3.096737675840527 17.507156809022316 42.84370617682738 50.563883087432174 other other other other text/xml other other geonode:lcp_regional_linkages_and_distance_local_linkages0db696d087f4 This layer shows the Least Cost Path (LCP), defining regional linkages and linkages less than 2.5 km.  “A Least-Cost-Path is defined as the pathway that offers the least resistance to an animal moving from one patch to another (Cushman et al., 2013) and is represented as the linear element (least-cost pathway) that connects two patches.” (Lumia et al., 2023) File name: LCP_Regional_Linkages_and_distance_local_linkages.shp Project website: https://www.alpine-space.eu/project/plantoconnect/ Alps features Spatial planning least cost path eusalp lcp_regional_linkages_and_distance_local_linkages0db696d087f4 EPSG:3035 CRS:84 3.2229602301352998 17.27155946627389 42.96381650608801 50.530868205896795 other other other other text/xml other other geonode:lighting_appliances_perc This layer describes the energy consumption of lights and appliances on National Level for the households. The frequency of data is annual and it is connected with the Nuts0 Level. consumption Appliances Lighting energy EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:local_policies The information provided in this layer gives an overview of the legislation and requirements in each country and shows how they impact the spread of PEB concepts, with the help of a practical example. Different policies and related boundary conditions in each country have a great influence on the successful implementation of plus energy concepts.Therefore, national funding schemes and local policies are analysed in regard to support renewable energy generation in buildings and favour the connection with the electric grid and other district buildings (e.g. direct delivery of power to neighbour buildings, grid feed-in) as well as the local energy market (e.g. energy prices, feed-in tariff) and foreseen developments and environmental aspects. local_policies cct boundary_conditions PEB EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:metadata Layer with informations about meteorological stations for Trentino-Alto Adige region, Austria and Switzerland. The layer describe the Climate-Database of Eurac Research that contains meteorological time series of daily temperature (maximum, minimum and mean) and daily total precipitation for more than 250 station sites. metadata cct meteo climate EPSG:4326 CRS:84 10.0 12.6146259307861 45.7391891479492 47.2610015869141 other other other other other other other other other other text/xml other text/xml other EO_CDR:municipalities_polygon This layer shows the municipality boundaries for Southtyrol, the Autonomous Province of Bolzano/Bozen. municipality administrative boundaries EPSG:25832 CRS:84 10.362601105547137 12.51496584959338 46.18287112383401 47.13258268966183 other other other other text/xml other other geonode:nuts2_simplified NUTS region, level 2 for the EUSALP area. The border are simplified respect to the original data source to get a lighter version. features nuts2_simplified EPSG:4326 CRS:84 3.69093990325928 17.1608009338379 43.0284385681152 50.5637321472168 other other other other text/xml other other geonode:nuts_rg_01m_2021_3035 features Alps vulnerability nuts_rg_01m_2021_3035 Eurac snow tourism destinations EPSG:3035 CRS:84 -90.23528381409486 103.45691358201807 -29.85708214170704 79.15047047071009 other other other other text/xml other other EO_CDR:osttirol_test_site_extent_pol_eurostat_pp This layer shows the spatial extent of the TRANSALP study area East Tyrol. study area East tyrol EPSG:3035 CRS:84 12.103165518386291 12.989414557529686 46.638887652283216 47.16826701651944 other other other other text/xml other other geonode:pilot_areas_updated features pilot_areas_updated beyondsnow EPSG:4326 CRS:84 6.35 13.95572038 44.075833 49.120234 other other other other text/xml other other EO_CDR:places-census_polygon No abstract provided settlements Southtyrol EPSG:25832 CRS:84 10.438140100692559 12.4028559174228 46.20449769144988 47.08977263700124 other other other other text/xml other other geonode:plantoconnect_motorway_barriers This layer is showing motorway barriers with potential ecological linkages in the Alps. File Name: PlanToConnect_Motorway_barriers.shp Project website: https://www.alpine-space.eu/project/plantoconnect/ Alps Ecological Connectivity Urban planning features plantoconnect_motorway_barriers Motorway barriers EPSG:3035 CRS:84 3.5668296746016512 17.11866253941121 43.12475247105326 50.29899865357443 other other other other text/xml other other geonode:plantoconnect_potential_ecological_network_eusalp This data set shows regional potential ecological network in EUSALP areas. Filename: PlanToConnect_Potential_ecological_network_EUSALP.shp Project website: https://www.alpine-space.eu/project/plantoconnect/ Alps Ecological Connectivity features plantoconnect_potential_ecological_network_eusalp Spatial planning eusalp EPSG:3035 CRS:84 3.0970600465027394 17.467303409930103 42.894812242522 50.56381664296285 other other other other text/xml other other geonode:poligoni_riferimento_bostrico No abstract provided poligoni_riferimento_bostrico features EPSG:32632 CRS:84 10.414799291236251 12.451493952082476 46.197145712038115 47.08856838596426 other other other other text/xml other other geonode:population_density The ratio between the annual average population and the land area. The land area concept (excluding inland waters) should be used wherever available; if not available then the total area, including inland waters (area of lakes and rivers) is used. The frequency is annual. Population Density EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:rund_um_dolomiten_tracks Rund um dolomiten tracks in the Bletterback park track features EPSG:4326 CRS:84 11.4057569503784 11.4552335739136 46.3470191955566 46.3721160888672 other other other other text/xml other other geonode:scd_20001001_20190930_16bit_3035 Average Mean Snow Cover Duration (SCD) based on cloud-filtered MODIS maps at 250m resolution and 19 years of observations from 2000-10-01 to 2019-09-30. The value represents snow covered days [0-365]. clirsnow snow scd EPSG:3035 CRS:84 3.54466776567458 18.990559099334657 42.66156863451433 48.95790687264097 other other other text/xml other other other geonode:scd_2041_2070_rcp26_noglacier_16bit_3035 Annual Mean Snow Cover Duration (SCD) according to climate projections under the RCP2.6 scenario from 2041 to 2070. The value represents snow covered days [0-365]. clirsnow snow scd cct EPSG:3035 CRS:84 3.5446677656745846 18.99055909933465 42.66156863451429 48.95790687264093 other other other other other text/xml other geonode:scd_2041_2070_rcp85_noglacier_16bit_3035 Annual Mean Snow Cover Duration (SCD) according to climate projections under the RCP8.5 scenario from 2041 to 2070. The value represents snow covered days [0-365]. clirsnow snow scd cct EPSG:3035 CRS:84 3.5446677656745846 18.99055909933465 42.66156863451429 48.95790687264093 other other other other text/xml other other geonode:scd_2071_2100_rcp26_noglacier_16bit_3035 Annual Mean Snow Cover Duration (SCD) according to climate projections under the RCP2.6 scenario from 2071 to 2100. The value represents snow covered days [0-365]. clirsnow snow scd cct EPSG:3035 CRS:84 3.5446677656745846 18.99055909933465 42.66156863451429 48.95790687264093 other other other other text/xml other other geonode:scd_2071_2100_rcp85_noglacier_16bit_3035 Annual Mean Snow Cover Duration (SCD) according to climate projections under the RCP8.5 scenario from 2071 to 2100. The value represents snow covered days [0-365]. clirsnow snow scd cct EPSG:3035 CRS:84 3.5446677656745846 18.99055909933465 42.66156863451429 48.95790687264093 other other other other text/xml other other geonode:sentinel2_mosaic_20170601_20170930_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2017. Displayed as false color (CIR) representation combining the near infrared, red and green bands. GeoTIFF WCS SENTINEL2_MOSAIC_20170601_20170930_CIR sentinel2_mosaic_20170601_20170930_cir EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 text/xml other other other other other other geonode:sentinel2_mosaic_20170601_20170930_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2017. Displayed as true color (RGB) representation combining the red, green and blue bands. GeoTIFF SENTINEL2_MOSAIC_20170601_20170930_CIR WCS SENTINEL2_MOSAIC_20170601_20170930_RGB sentinel2_mosaic_20170601_20170930_rgb EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other text/xml other other other geonode:sentinel2_mosaic_20180601_20180930_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2018. Displayed as false color (CIR) representation combining the near infrared, red and green bands. SENTINEL2_MOSAIC_20180601_20180930_CIR GeoTIFF WCS sentinel2_mosaic_20180601_20180930_cir EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20180601_20180930_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2018. Displayed as true color (RGB) representation combining the red, green and blue bands. WCS SENTINEL2_MOSAIC_20180601_20180930_RGB GeoTIFF sentinel2_mosaic_20180601_20180930_rgb EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other text/xml other other other other other geonode:sentinel2_mosaic_20190601_20190930_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2019. Displayed as false color (CIR) representation combining the near infrared, red and green bands. sentinel2_mosaic_20190601_20190930_cir GeoTIFF WCS SENTINEL2_MOSAIC_20190601_20190930_CIR EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20190601_20190930_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2019. Displayed as true color (RGB) representation combining the red, green and blue bands. WCS GeoTIFF sentinel2_mosaic_20190601_20190930_rgb SENTINEL2_MOSAIC_20190601_20190930_RGB EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20200601_20200930_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2020. Displayed as false color (CIR) representation combining the near infrared, red and green bands. GeoTIFF sentinel2_mosaic_20200601_20200930_cir WCS SENTINEL2_MOSAIC_20200601_20200930_CIR EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20200601_20200930_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2020. Displayed as true color (RGB) representation combining the red, green and blue bands. WCS GeoTIFF SENTINEL2_MOSAIC_20200601_20200930_RGB sentinel2_mosaic_20200601_20200930_rgb EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20210601_20210930_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2021. Displayed as false color (CIR) representation combining the near infrared, red and green bands. WCS GeoTIFF sentinel2_mosaic_20210601_20210930_cir SENTINEL2_MOSAIC_20210601_20210930_CIR EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20210601_20210930_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2021. Displayed as true color (RGB) representation combining the red, green and blue bands. WCS SENTINEL2_MOSAIC_20210601_20210930_RGB GeoTIFF sentinel2_mosaic_20210601_20210930_rgb EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20220601_20220930_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2022. Displayed as false color (CIR) representation combining the near infrared, red and green bands. WCS GeoTIFF SENTINEL2_MOSAIC_20220601_20220930_CIR sentinel2_mosaic_20220601_20220930_cir EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20220601_20220930_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2022. Displayed as true color (RGB) representation combining the red, green and blue bands. WCS GeoTIFF sentinel2_mosaic_20220601_20220930_rgb SENTINEL2_MOSAIC_20220601_20220930_RGB EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20230601_20230930_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2023. Displayed as false color (CIR) representation combining the near infrared, red and green bands. WCS SENTINEL2_MOSAIC_20230601_20230930_CIR sentinel2_mosaic_20230601_20230930_cir GeoTIFF EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other text/xml other other other geonode:sentinel2_mosaic_20230601_20230930_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2023. Displayed as true color (RGB) representation combining the red, green and blue bands. GeoTIFF sentinel2_mosaic_20230601_20230930_rgb WCS SENTINEL2_MOSAIC_20230601_20230930_RGB EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 text/xml other other other other other other geonode:sentinel2_mosaic_20240601_20240930_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2024. Displayed as false color (CIR) representation combining the near infrared, red and green bands. WCS sentinel2_mosaic_20240601_20240930_cir GeoTIFF SENTINEL2_MOSAIC_20240601_20240930_CIR EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20240601_20240930_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2024. Displayed as true color (RGB) representation combining the red, green and blue bands. GeoTIFF SENTINEL2_MOSAIC_20240601_20240930_RGB WCS sentinel2_mosaic_20240601_20240930_rgb EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other text/xml other other other other other geonode:sentinel2_mosaic_20250501_20250531_rgb Monthly RGB mosaic for South Tyrol with no clouds derived from Sentinel-2 image data. sentinel2 mosaic rgb WCS sentinel2_mosaic_20250501_20250531_rgb GeoTIFF EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20250601_20250930_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2025. Displayed as false color (CIR) representation combining the near infrared, red and green bands. WCS sentinel2_mosaic_20250601_20250930_cir GeoTIFF SENTINEL2_MOSAIC_20250601_20250930_CIR EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other text/xml other other other geonode:sentinel2_mosaic_20250601_20250930_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2025. Displayed as true color (RGB) representation combining the red, green and blue bands. WCS GeoTIFF sentinel2_mosaic_20250601_20250930_rgb SENTINEL2_MOSAIC_20250601_20250930_RGB EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20260501_20260611_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within May and June 2026. Displayed as false color (CIR) representation combining the near infrared, red and green bands. WCS SENTINEL2_MOSAIC_20260501_20260611_CIR GeoTIFF sentinel2_mosaic_20260501_20260611_cir EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other text/xml other other other other other geonode:sentinel2_mosaic_20260501_20260611_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within May and June 2026. Displayed as true color (RGB) representation combining the red, green and blue bands. SENTINEL2_MOSAIC_20260501_20260611_RGB WCS GeoTIFF sentinel2_mosaic_20260501_20260611_rgb EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other text/xml other other other other geonode:sentinel2_mosaic_20260601_20260719_cir Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and July 2026. Displayed as false color (CIR) representation combining the near infrared, red and green bands. SENTINEL2_MOSAIC_20260601_20260719_CIR sentinel2_mosaic_20260601_20260719_cir GeoTIFF WCS EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:sentinel2_mosaic_20260601_20260719_rgb Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and July 2026. Displayed as true color (RGB) representation combining the red, green and blue bands. sentinel2_mosaic_20260601_20260719_rgb SENTINEL2_MOSAIC_20260601_20260719_RGB GeoTIFF WCS EPSG:32632 CRS:84 10.370413873759238 12.50411223275276 46.187977202527264 47.12763899528046 other other other other text/xml other other geonode:simulation3 Simulation for the START project of the path of 3 person in the Park site. In the future this simulation will be replaced by a near real time updated layer. You can see on the map different position at different time using the arrow of the timeline tool on the map. person simulation features path EPSG:4326 CRS:84 11.3964538574219 11.4368095397949 46.358268737793 46.381778717041 2020-09-26T00:00:00.000Z,2020-09-27T00:00:00.000Z,2020-09-28T00:00:00.000Z,2020-09-29T00:00:00.000Z,2020-09-30T00:00:00.000Z,2020-10-01T00:00:00.000Z,2020-10-02T00:00:00.000Z,2020-10-03T00:00:00.000Z,2020-10-04T00:00:00.000Z,2020-10-05T00:00:00.000Z,2020-10-06T00:00:00.000Z,2020-10-07T00:00:00.000Z other other other other other text/xml other geonode:solar_line Layer to represent the "tour" to seven case study buildings of exemplary energy efficient interventions in historic buildings. In all buildings, a photovoltaic or solar thermal system was integrated as one of the renovation measures. historic buildings virtual tour EPSG:4326 CRS:84 7.28434562683105 16.3290176391602 45.9442367553711 48.1916885375977 other other text/xml other other other other geonode:solar_point_new Layer to represent the position of seven case study buildings of exemplary energy efficient interventions in historic buildings. In all buildings, a photovoltaic or solar thermal system was integrated as one of the renovation measures. historic buildings energy refurbishment EPSG:4326 CRS:84 7.28434562683105 16.3290176391602 45.9442367553711 48.1916885375977 other other other other text/xml other other EO_CDR:southtyrol_tran_traffic_report_pt_s4_pa_pp Accumulated records of the traffic situation over the roads of South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs). features southtyrol_tran_traffic_report_pt_s4_pa_pp EPSG:4326 CRS:84 11.1094999313354 11.9448003768921 46.3412971496582 46.8896026611328 other other other other text/xml other other EO_CDR:southtyrol_tran_traffic_report_pt_s4_pa_pp_mroads_and_passes Accumulated records of traffic events over mountain roads and passes in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs). southtyrol_tran_traffic_report_pt_s4_pa_pp_mroads_and_passes features EPSG:4326 CRS:84 10.4527997970581 12.4203004837036 46.3545989990234 46.8881034851074 other other other other text/xml other other EO_CDR:southtyrol_tran_traffic_report_pt_s4_pa_pp_neigh_countries Accumulated records of traffic events related to border areas in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs). features southtyrol_tran_traffic_report_pt_s4_pa_pp_neigh_countries EPSG:4326 CRS:84 10.4907999038696 11.5084009170532 46.9191970825195 47.0049018859863 other other other other text/xml other other EO_CDR:southtyrol_tran_traffic_report_pt_s4_pa_pp_public_transports Accumulated records of traffic events related to public transports in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs). features southtyrol_tran_traffic_report_pt_s4_pa_pp_public_transports EPSG:4326 CRS:84 11.1490993499756 11.364200592041 46.4078979492188 46.6734008789062 other other other other text/xml other other EO_CDR:southtyrol_tran_traffic_report_pt_s4_pa_pp_works_and_locks Accumulated records of works and locks over the roads of South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs). features southtyrol_tran_traffic_report_pt_s4_pa_pp_works_and_locks EPSG:4326 CRS:84 10.5111999511719 12.2151002883911 46.2415962219238 47.0427017211914 other other other other text/xml other other geonode:space_cooling_gwh The Layers shows the share of final energy consumption in the residential sector for space cooling. The frequency of data is annual. cct houshold cooling Space EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:space_heating_gwh The Layers shows the share of fuels in the final energy consumption in the residential sector for space heating. The frequency of data is annual. Household cct Space heating EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other EO_CDR:st_glaciers_outlines_pol_s4_20152020_eurac High-resolution outline of glaciers from both Sentinel-1 and Sentinel-2 satellites over South Tyrol (years 2015 to 2020, included). st_glaciers_outlines_pol_s4_20152020_eurac features EPSG:32632 CRS:84 8.999733724826543 13.091123903027226 45.9812441907275 47.64189870353996 other other other other text/xml other other EO_CDR:st_hazard_events_avalanche_pol_apb_2020_pp Southtyrol: past avalanche events. events hazard avalanche EPSG:3035 CRS:84 10.39392931460448 12.425708581430879 46.31264811704347 47.1074568816563 other other other other text/xml other other EO_CDR:st_hazard_plan_water_2013_apb_pol_pp Hydrological risk maps of the province of Bolzano. st_hazard_plan_water_2013_apb_pol_pp features EPSG:25832 CRS:84 10.372639748215283 12.462230325513369 46.19074700894833 46.97965456527684 other other other other text/xml other other EO_CDR:st_hzd_evnt_hydro_ed30_apb_pnt_all Impacts of flood events in South Tyrol (IT) taken from the ED30 database. st_hzd_evnt_hydro_ed30_apb_pnt_all ed30 features EPSG:25832 CRS:84 10.427342558034912 12.407176708417248 46.22703137105401 47.11892174303518 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2008-08-05T00:00:00.000Z,2008-09-04T00:00:00.000Z,2009-07-15T00:00:00.000Z,2009-07-31T00:00:00.000Z,2009-08-16T00:00:00.000Z,2009-08-20T00:00:00.000Z,2009-09-03T00:00:00.000Z,2009-12-23T00:00:00.000Z,2010-01-03T00:00:00.000Z,2010-05-31T00:00:00.000Z,2010-06-09T00:00:00.000Z,2010-06-11T00:00:00.000Z,2010-06-12T00:00:00.000Z,2010-07-09T00:00:00.000Z,2010-07-10T00:00:00.000Z,2010-07-11T00:00:00.000Z,2010-07-13T00:00:00.000Z,2010-07-16T00:00:00.000Z,2010-07-20T00:00:00.000Z,2010-07-28T00:00:00.000Z,2010-07-31T00:00:00.000Z,2010-08-01T00:00:00.000Z,2010-08-09T00:00:00.000Z,2010-08-13T00:00:00.000Z,2010-08-14T00:00:00.000Z,2010-08-23T00:00:00.000Z,2010-08-26T00:00:00.000Z,2010-09-30T00:00:00.000Z,2011-06-07T00:00:00.000Z,2011-06-17T00:00:00.000Z,2011-06-22T00:00:00.000Z,2011-07-03T00:00:00.000Z,2011-07-12T00:00:00.000Z,2011-07-14T00:00:00.000Z,2011-08-02T00:00:00.000Z,2011-08-04T00:00:00.000Z,2011-08-07T00:00:00.000Z,2011-08-13T00:00:00.000Z,2011-08-16T00:00:00.000Z,2011-08-25T00:00:00.000Z,2011-09-03T00:00:00.000Z,2011-09-04T00:00:00.000Z,2011-09-10T00:00:00.000Z,2011-09-11T00:00:00.000Z,2012-06-03T00:00:00.000Z,2012-06-20T00:00:00.000Z,2012-06-28T00:00:00.000Z,2012-07-01T00:00:00.000Z,2012-07-04T00:00:00.000Z,2012-07-10T00:00:00.000Z,2012-07-13T00:00:00.000Z,2012-07-18T00:00:00.000Z,2012-07-19T00:00:00.000Z,2012-07-29T00:00:00.000Z,2012-08-02T00:00:00.000Z,2012-08-03T00:00:00.000Z,2012-08-04T00:00:00.000Z,2012-08-15T00:00:00.000Z,2012-08-21T00:00:00.000Z,2012-08-24T00:00:00.000Z,2012-11-03T00:00:00.000Z,2012-11-09T00:00:00.000Z,2012-11-10T00:00:00.000Z,2013-04-27T00:00:00.000Z,2013-05-01T00:00:00.000Z,2013-07-21T00:00:00.000Z,2013-07-26T00:00:00.000Z,2013-10-22T00:00:00.000Z,2014-07-30T00:00:00.000Z,2015-07-19T00:00:00.000Z,2015-07-21T00:00:00.000Z,2015-07-24T00:00:00.000Z,2015-07-28T00:00:00.000Z,2015-08-03T00:00:00.000Z,2016-06-23T00:00:00.000Z,2016-06-29T00:00:00.000Z,2016-07-09T00:00:00.000Z,2016-07-10T00:00:00.000Z,2016-07-11T00:00:00.000Z,2016-08-24T00:00:00.000Z,2017-03-27T00:00:00.000Z,2017-05-29T00:00:00.000Z,2017-06-23T00:00:00.000Z,2017-07-08T00:00:00.000Z,2017-07-09T00:00:00.000Z,2017-07-23T00:00:00.000Z,2017-08-04T00:00:00.000Z,2018-05-31T00:00:00.000Z,2018-08-22T00:00:00.000Z,2018-08-29T00:00:00.000Z,2018-10-26T00:00:00.000Z,2018-10-27T00:00:00.000Z,2018-10-28T00:00:00.000Z,2019-07-25T00:00:00.000Z,2020-06-28T00:00:00.000Z,2020-07-09T00:00:00.000Z,2020-07-30T00:00:00.000Z,2020-08-15T00:00:00.000Z,2020-08-16T00:00:00.000Z,2020-08-21T00:00:00.000Z,2020-08-28T00:00:00.000Z,2020-08-29T00:00:00.000Z other other other other text/xml other other EO_CDR:st_hzd_evnt_landslides_pt_s4_p_iffi Impacts of landslides events in South Tyrol (IT) taken from the IFFI (Inventory of Landslide Phenomena in Italy) database [last update 18 Nov 2022]. events south tyrol landslides st_hzd_evnt_landslides_pt_s4_p_iffi features IFFI EPSG:32632 CRS:84 10.381549361507759 12.430726985267956 46.19895386009795 47.1166319811976 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other other other other text/xml other other EO_CDR:st_hzd_pred_landslides_pol_s4_p_polygonized Polygonized time-series of landslide susceptibility (%) over South Tyrol. susceptibility st_hzd_pred_landslides_pol_s4_p_polygonized landslides EPSG:32632 CRS:84 10.373172280345136 12.504228171807831 46.19005352134215 47.1276189831466 2018-09-30T00:00:00.000Z,2018-10-01T00:00:00.000Z,2018-10-02T00:00:00.000Z,2018-10-03T00:00:00.000Z,2018-10-04T00:00:00.000Z,2018-10-05T00:00:00.000Z,2018-10-06T00:00:00.000Z,2018-10-07T00:00:00.000Z,2018-10-08T00:00:00.000Z,2018-10-09T00:00:00.000Z,2018-10-10T00:00:00.000Z,2018-10-11T00:00:00.000Z,2018-10-12T00:00:00.000Z,2018-10-13T00:00:00.000Z,2018-10-14T00:00:00.000Z,2018-10-15T00:00:00.000Z,2018-10-16T00:00:00.000Z,2018-10-17T00:00:00.000Z,2018-10-18T00:00:00.000Z,2018-10-19T00:00:00.000Z,2018-10-20T00:00:00.000Z,2018-10-21T00:00:00.000Z,2018-10-22T00:00:00.000Z,2018-10-23T00:00:00.000Z,2018-10-24T00:00:00.000Z,2018-10-25T00:00:00.000Z,2018-10-26T00:00:00.000Z,2018-10-27T00:00:00.000Z,2018-10-28T00:00:00.000Z,2018-10-29T00:00:00.000Z,2018-10-30T00:00:00.000Z,2018-10-31T00:00:00.000Z,2018-11-01T00:00:00.000Z,2018-11-02T00:00:00.000Z,2018-11-03T00:00:00.000Z,2018-11-04T00:00:00.000Z,2018-11-05T00:00:00.000Z,2018-11-06T00:00:00.000Z,2018-11-07T00:00:00.000Z,2018-11-08T00:00:00.000Z,2018-11-09T00:00:00.000Z,2018-11-10T00:00:00.000Z,2018-11-11T00:00:00.000Z,2018-11-12T00:00:00.000Z,2018-11-13T00:00:00.000Z,2018-11-14T00:00:00.000Z,2018-11-15T00:00:00.000Z,2018-11-16T00:00:00.000Z,2018-11-17T00:00:00.000Z,2018-11-18T00:00:00.000Z,2018-11-19T00:00:00.000Z,2018-11-20T00:00:00.000Z,2018-11-21T00:00:00.000Z,2018-11-22T00:00:00.000Z,2018-11-23T00:00:00.000Z,2018-11-24T00:00:00.000Z,2018-11-25T00:00:00.000Z,2018-11-26T00:00:00.000Z,2018-11-27T00:00:00.000Z,2018-11-28T00:00:00.000Z,2018-11-29T00:00:00.000Z other other other other text/xml other other EO_CDR:st_landuse_level1_pol_pp_2001 This layer shows the level 1 land use land cover classes for the Province of South Tyrol. land cover landuse South Tyrol EPSG:32632 CRS:84 10.362528659438649 12.5149779749724 46.18285786752428 47.13288044098754 other other other other text/xml other other EO_CDR:st_pop_flow_rds_ln_s3_apb_250m Projection of home->work trips in South Tyrol onto OSM drivable roads network. Roads themselves are projected onto a 250m hexagonal tessellation. Population features flow tessellated st_pop_flow_rds_ln_s3_apb_250m EPSG:25832 CRS:84 10.451959028864628 12.390197062096277 46.20779227520579 47.07511525888605 other other other other text/xml other other EO_CDR:st_pop_flow_rds_ln_s3_apb_250m_dyn Projection of home->work trips in South Tyrol onto OSM drivable roads network. Roads themselves are projected onto a 250m hexagonal tessellation. Edge load is updated at every new trip projection. flow st_pop_flow_rds_ln_s3_apb_250m_dyn features EPSG:25832 CRS:84 10.451959028864628 12.390197062096277 46.20779227520579 47.07511525888605 text/xml other other other other other other EO_CDR:st_pop_pol_s3_250m_daynight Multi-temporal aggregated population data over South Tyrol onto an hexagonal tessellation of ~250m. Day-time, night-time and commuting time are available. day night Population features st_pop_pol_s3_250m_daynight EPSG:25832 CRS:84 10.371049263673868 12.505750542745076 46.18640906033353 47.12930390251141 other other other other text/xml other other EO_CDR:st_tess_voronoi_pol_s4_apb Partition of the the municipality of Bolzano into Voronoi regions for proper aggregation of sensitive data (created by APB Osservatorio del Lavoro). st_tess_voronoi_pol_s4_apb apb features EPSG:25832 CRS:84 11.203855102861972 11.492032445620918 46.38062662202115 46.581911941087014 other other other text/xml other other other EO_CDR:st_tessellation_250m_bolzano_area_pol Tessellation onto regular hexagonal cells of the area of Bolzano at a resolution of ~250m. st_tessellation_250m_bolzano_area_pol tessellation features bozen EPSG:25832 CRS:84 11.271813085015884 11.41953098861481 46.44051238130469 46.53350094091436 other other other other text/xml other other EO_CDR:st_tessellation_250m_municipality_bolzano_pol Tessellation onto regular hexagonal cells of the municipality of Bolzano at a resolution of ~250m. st_tessellation_250m_municipality_bolzano_pol tessellation features bozen EPSG:25832 CRS:84 11.272166573520142 11.439214810527869 46.44054112491988 46.53417318783022 other other other other text/xml other other EO_CDR:st_tessellation_250m_pol Tessellation onto regular hexagonal cells of South Tyrol (IT) at a resolution of ~250m. st_tessellation_250m_pol suedtirol features tessellation EPSG:25832 CRS:84 10.360541894374531 12.5166704916045 46.18159905776697 47.13514448013207 other other other other text/xml other other EO_CDR:st_test_site_extent_pol_pp This layer shows the geographic extent of the TRANSALP study area South Tyrol. extent South Tyrol EPSG:25832 CRS:84 10.373090906664874 12.504144806990185 46.187664005069664 47.12796970469726 other other other other text/xml other other EO_CDR:st_traffic_vs_flow_250tess_2021 Absolute difference between traffic counts in the 5-9 AM time interval (2021 averages) and the commuting population flow model output. flow traffic st_traffic_vs_flow_250tess_2021 validation features commuting EPSG:25832 CRS:84 10.467075949215037 12.43530590309998 46.20755955511522 47.01568966667103 other other other other text/xml other other EO_CDR:st_tran_rds_ln_s4_osm_pp_drive_250tess Drivable roads from OpenStreetMap over South Tyrol (IT) onto an hexagonal tessellation of ~250m. st_tran_rds_ln_s4_osm_pp_drive_250tess tessellation south tyrol features drive EPSG:25832 CRS:84 10.415660344605858 12.421923199647185 46.20328725211625 47.093709182372045 other other other other text/xml other other EO_CDR:st_tran_rds_ln_s4_osm_pp_drive_250tess_bz Drivable roads from OpenStreetMap over the area of Bozen (South Tyrol) onto an hexagonal tessellation of ~250m. tessellated osm st_tran_rds_ln_s4_osm_pp_drive_250tess_bz features drive EPSG:25832 CRS:84 11.273764389139854 11.41757432261899 46.441685529955215 46.532325427280924 other other other other text/xml other other EO_CDR:st_tran_rds_ln_s4_osm_pp_drive_250tess_dyn Drivable roads from OpenStreetMap over South Tyrol (IT) onto an hexagonal tessellation of ~250m, where the weight of each edge is reduced by an amount thatis proportional to the simulation of home->work traffic of South Tyrol. features st_tran_rds_ln_s4_osm_pp_drive_250tess_dyn EPSG:25832 CRS:84 10.415660344605858 12.42207765870687 46.20328725211625 47.09612454817838 other other other other text/xml other other EO_CDR:st_trans_traffic_counts_hourly_average_2021_apb_pnt 2021 yearly averages of traffic counts per each hour of the day, over South Tyrol. st_trans_traffic_counts_hourly_average_2021_apb_pnt traffic features hour EPSG:25832 CRS:84 10.466127639418104 12.435781457477097 46.20742394434886 47.01548172507333 other other other other text/xml other other geonode:study_area This layer shows the spatial extent of the Transalp study area Agordino - Valle del Cordevole. extent study area Veneto EPSG:3003 CRS:84 11.764621786338369 12.174087836126871 46.16230194046746 46.55080671613287 other other other other text/xml other other EO_CDR:sudagoost_tran_rds_ln_s3_osm_pp_main Main roads from OpenStreetMap over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria). sudagoost_tran_rds_ln_s3_osm_pp_main osm main features EPSG:4326 CRS:84 10.4637994766235 12.8832788467407 46.2126884460449 47.1242218017578 other other other other text/xml other other EO_CDR:sudagoost_tran_rds_ln_s4_osm_pp_drive Drivable roads from OpenStreetMap over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria). drive osm sudagoost_tran_rds_ln_s4_osm_pp_drive features EPSG:4326 CRS:84 10.4131050109863 12.936466217041 46.1979637145996 47.1242942810059 other other other other text/xml other other EO_CDR:sudagoost_tran_rds_ln_s4_osm_pp_drive_betwcentr Betweenness centrality topologic indicator calculated on the OSM drivable roads over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria). sudagoost_tran_rds_ln_s4_osm_pp_drive_betwcentr osm betweenness centrality features drive EPSG:4326 CRS:84 10.4131050109863 12.936466217041 46.1979637145996 47.1242942810059 other other other other text/xml other other EO_CDR:sudagoost_tran_rds_ln_s4_osm_pp_drive_hospacc Hospital accessibility indicator calculated on the OSM drivable roads over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria). sudagoost_tran_rds_ln_s4_osm_pp_drive_hospacc osm features accessibilty EPSG:4326 CRS:84 10.4131050109863 12.936466217041 46.1985321044922 47.1235694885254 other other other other text/xml other other geonode:timeseries_sos Stations and timeseries of the MONALISA-SOS service. Environmental timeseries collected in the SouthTyrol province by the MONALISA project partners. Query the features and click the link to view last values collected features timeseries_sos EPSG:4326 CRS:84 10.5798807144165 12.2728328704834 46.3047027587891 47.0376014709473 other other other other text/xml other other EO_CDR:tirol_trans_roads_paths_ln_s5_pp Lineares Referenzsystem der Verkehrsinfrastrukturen von Tirol - beinhaltet Hochrangiges Strassen- und Bahnnetz bis hin zu den Fuss- und Wanderwegen. Originaldatensatz wird in der Graphenintegrations-Plattform Tirol gewartet. pa roads tirol_trans_roads_paths_ln_s5_pp features EPSG:31254 CRS:84 10.078461995783474 13.013815753810244 46.64685051195649 47.757838100502504 other other other other text/xml other other EO_CDR:transalp_exposure_assets_pol_s3_250m Aggregated data from exposed assets over the TRANSALP project cross-border study area onto an hexagonal tessellation of ~250m. exposure transalp_exposure_assets_pol_s3_250m features tessellation EPSG:25832 CRS:84 10.370206836694845 12.99450554094802 46.137276310174194 47.20420318312805 other other other other text/xml other other EO_CDR:transalp_landuse_corine_pol_pp_2018 The Copernicus "CORINE Land Cover" dataset of 2018, clipped over the TRANSALP project cross-border test area. transalp_landuse_corine_pol_pp_2018 features EPSG:3035 CRS:84 10.380924871849189 12.98498543784949 46.14495515903227 47.18582972020293 other other other other other other text/xml EO_CDR:transalp_study_area_tesselation_population_pol Aggregated population data over the TransAlp project's study area, which comprises South Tyrol (IT), Valle Agordina (Veneto) and East Tyrol (AU), onto an hexagonal tessellation of ~250m. transalp_study_area_tesselation_population_pol Population features tessellation EPSG:25832 CRS:84 10.370206836694845 12.99450554094802 46.137276310174194 47.20420318312805 other other other other text/xml other other EO_CDR:transalp_tesselation_250m_cross_border_studyarea Tessellation onto regular hexagonal cells of the TransAlp project's study area, which comprises South Tyrol (IT), Valle Agordina (Veneto) and East Tyrol (AU), at a resolution of ~250m. transalp_tesselation_250m_cross_border_studyarea transalp features tessellation EPSG:25832 CRS:84 10.370206836694845 12.99450554094802 46.137276310174194 47.20420318312805 other other other other text/xml other other EO_CDR:transalp_test_site_extent_pol_pp Extent of the cross-border study area for the TRANSALP project, which includes South Tyrol (IT), Valle Agordino (Veneto, IT), and East Tyrol (AT). study features transalp_test_site_extent_pol_pp extent EPSG:25832 CRS:84 10.372247176446415 12.991038464954475 46.13859804352061 47.20266987088882 other other other other text/xml other other EO_CDR:transalp_tran_rds_ln_s4_osm_pp_drive_250tess Drivable roads from OpenStreetMap over TransAlp project's study area (South Tyrol, East Tyrol, Valle Agordina) onto an hexagonal tessellation of ~250m. tessellation transalp_tran_rds_ln_s4_osm_pp_drive_250tess transalp features drive EPSG:25832 CRS:84 10.41531799932096 12.946662081065503 46.17376142335433 47.163772338652734 other other other other text/xml other other EO_CDR:transalp_tran_rds_ln_s4_osm_pp_drive_250tess_betwcentr Betweenness centrality topologic indicator calculated on the OSM drivable roads over the trans-national area of South Tyrol (IT), Agordino (Veneto, IT) and East Tyrol (AU). Roads have been projected onto a 250m regular hexagonal tessellation before analysis. features osm drive betweenness centrality transalp_tran_rds_ln_s4_osm_pp_drive_250tess_betwcentr tessellation EPSG:25832 CRS:84 10.41531799932096 12.946662081065503 46.17376142335433 47.163772338652734 other other other other other text/xml other EO_CDR:transalp_tran_rds_ln_s4_osm_pp_drive_250tess_hospacc Hospital accessibility indicator calculated on the tessellated OSM drivable roads over the trans-national area covering South Tyrol (IT), Agordino (Veneto, IT), and East Tyrol (AU). hospitals features tessellation accessibilty EPSG:25832 CRS:84 10.41531799932096 12.946662081065503 46.17376142335433 47.163772338652734 other other other other text/xml other other EO_CDR:tvo_tran_rds_ln_s3_osm_pp_drive OpenStreetMap drivable roads over Trentino Alto-Adige, Veneto and Osttirol (Austria). tvo_tran_rds_ln_s3_osm_pp_drive drive osm EPSG:4326 CRS:84 10.4470529556274 13.0935535430908 44.7853164672852 47.0284996032715 other other text/xml other other other other geonode:uas_aed_coverage Coverage of the proposed UAS-AED Network of South Tyrol. cartography defibrillator drone EPSG:25832 CRS:84 10.397309422918177 12.485715811878544 46.1918248333547 47.12688983119196 other other other other text/xml other other geonode:uas_aed_density_hex Density of the proposed UAS-AED Network of South Tyrol. cartography defibrillator drone EPSG:25832 CRS:84 10.392438691832746 12.48390718882297 46.18670112180444 47.133750829709726 other other other other text/xml other other geonode:uas_aed_network Potential UAS-AED Network of South Tyrol. cartography defibrillator drone EPSG:25832 CRS:84 10.422722221120353 12.45943939431831 46.208780939951204 47.11570723944323 other other other other text/xml other other geonode:uas_aed_network_thiessen UAS-AED Stations Influence Areas (Thiessen Polygons) of South Tyrol. cartography defibrillator drone EPSG:25832 CRS:84 10.373090906664874 12.504144806990185 46.187664005069664 47.12796970469726 other other other other text/xml other other geonode:uas_aed_suitab UAS-AED Suitability for the proposed UAS-AED Network of South Tyrol. cartography defibrillator drone EPSG:25832 CRS:84 10.373105114020492 12.504080731803514 46.1877075928778 47.1279666043836 other other other other text/xml other other EO_CDR:urbanplan-hazardzoneplan-landslides_polygon Gravitational mass movement hazard level maps of the province of Bolzano. features urbanplan-hazardzoneplan-landslides_polygon EPSG:25832 CRS:84 10.379860564457681 12.462256285905807 46.190743580891755 46.98052792729793 other other text/xml other other other other EO_CDR:veneto_study_area_extent_pol This layer shows the geographic extent of the TRANSALP study area Agordino - Valle del Cordevole. extent Veneto EPSG:3003 CRS:84 11.762587679377482 12.176149879048875 46.1603568190302 46.5527463680623 other other text/xml other other other other EO_CDR:veneto_tran_rds_ln_s4_pa_pp Rete stradale derivata da DataBase strati prioritario in scala 1:10.000 (Regione Veneto,Sezione Pianificazione Territoriale Strategica e Cartografia) features veneto_tran_rds_ln_s4_pa_pp pa roads EPSG:3003 CRS:84 10.594902637777082 13.156617179840023 44.766713170872066 46.64268587907434 other other text/xml other other other other geonode:water_heating_perc The Layer of share of final energy consumption in the residential sector for water heating. The frequency of data is annual. water energy heating cct consumption EPSG:4326 CRS:84 -31.2679100036621 44.8203735351562 27.6384792327881 71.1841659545898 other other other other text/xml other other geonode:weighted_sum_auto Layer to represent the most suitable locations for installing charging infrastructure for e-cars in Verbano-Cusio-Ossola province. e-mobility EPSG:25832 CRS:84 7.939265288345857 8.676406527831855 45.76415791895979 46.42470168573652 other other other other text/xml other other geonode:weighted_sum_bici Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in Verbano-Cusio-Ossola province. e-mobility EPSG:25832 CRS:84 7.956716882301227 8.720980910950855 45.76432119063834 46.46208000923177 other other other other text/xml other other geonode:weighted_sum_bike0 Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in South Tyrol. e-mobility EPSG:25832 CRS:84 10.404488977689212 12.392124220107902 46.19975616775574 47.094591183145944 other other other other text/xml other other geonode:weighted_sum_car0 Layer to represent the most suitable locations for installing charging infrastructure for e-cars in South Tyrol. e-mobility EPSG:25832 CRS:84 10.446039762245645 12.26518022234239 46.241138199509194 47.09942473802638 other other other other text/xml other other geonode:weighted_vulnerability Vulnerability indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow project vulnerability Alps Eurac weighted_vulnerability features snow tourism destinations EPSG:3035 CRS:84 3.096737675840527 17.507156809022316 42.84370617682738 50.563883087432174 other other other other text/xml other other