{ "cells": [ { "cell_type": "markdown", "id": "c90bd57f-f2f3-4883-b823-d2d25d30abcd", "metadata": {}, "source": [ "# Short excursion through the UP42 Spatial Asset Service API\n", "\n", "## Introduction\n", "\n", "[STAC](https://stacspec.org/en) is a specification for the handling of\n", "geospatial asset catalogs. It has emerged from the efforts of several\n", "companies, governmental and non-governmental organizations in the\n", "geospatial domain.\n", "\n", "In a previous\n", "[article](https://up42.com/blog/automate-your-pipeline-with-stac-and-our-brand-new-data-management)\n", "we gave you an overview of STAC and our new storage capabilities with\n", "STAC, explaining the specification and its benefits in a nutshell. In\n", "this blog post we are going to further explain why we brought STAC to\n", "UP42 storage and introduced STAC-compatible endpoints for all geospatial\n", "assets in\n", "[storage](https://docs.up42.com/getting-started/concepts#storage) by\n", "giving you real-world examples. We will show you how to overcome common\n", "data management challenges by customizing assets in storage, searching\n", "for a specific asset property or creating a consolidated view of your\n", "assets through sort and filter operations. This first article uses a\n", "Python HTTP client library, in this case\n", "[httpx](https://www.python-httpx.org/). The way the authentication is\n", "done in httpx, via [httpx-auth](https://pypi.org/project/httpx-auth/)\n", "makes it very transparent how the Client Credentials OAuth flow is used\n", "to authenticate with the UP42 API.\n", "\n", "You can run the code below in console based\n", "[IPython](https://ipython.org/) or in [Jupyter](https://jupyter.org/)\n", "lab or classic notebook. For simplicity’s sake we refer to it simply as\n", "notebook.\n", "\n", "It explores the API entry points and then proceeds towards real use\n", "cases, e.g., searching for assets that intersect a given AOI, etc.\n", "\n", "## Creating the environment and importing the needed packages.\n", "\n", "1. Create a virtual environment.\n", "\n", "``` bash\n", " mkvirtualenv --python=$(which python3) up42-asset-service-api\n", "```\n", "\n", "2. Activate the environment.\n", "\n", "``` bash\n", " workon up42-asset-service-api\n", "```\n", "\n", "3. Install Jupyter Lab.\n", "\n", "``` bash\n", " pip install jupyterlab\n", "```\n", "\n", "4. Done.\n", "\n", "Now we can install the needed modules from inside Jupyter." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install requests" ] }, { "cell_type": "code", "execution_count": 6, "id": "015334cd-bd80-4d4f-9f3b-ca529132b152", "metadata": {}, "outputs": [], "source": [ "import requests" ] }, { "cell_type": "markdown", "id": "aabf7908-05e9-4214-8657-4967119c20c3", "metadata": {}, "source": [ "## Authenticate with the UP42 API\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "USERNAME = \"your-username\"\n", "PASSWORD = \"your-password\"\n", "WORKSPACE_ID = \"your-workspace-id\"" ] }, { "cell_type": "code", "execution_count": 7, "id": "1bc84207-9c76-4126-a0d0-69c516d1fb73", "metadata": {}, "outputs": [], "source": [ "\n", "def get_auth_token_prod(username, password):\n", " \"\"\"Authentication based on UP42 username and password\"\"\"\n", " AUTH_URL = \"https://api.up42.com/oauth/token\"\n", " req_headers = {\n", " \"Content-Type\": \"application/x-www-form-urlencoded\",\n", " }\n", " req_body = {\n", " \"grant_type\": \"password\",\n", " \"username\": username,\n", " \"password\": password}\n", " response = requests.post(AUTH_URL, data=req_body, headers=req_headers)\n", " return response.json()['data']['accessToken']" ] }, { "cell_type": "markdown", "id": "c6460494-4985-4568-a05a-8d33b5b245b1", "metadata": {}, "source": [ "## Some auxiliary functions\n", "\n", "Let us create a few auxiliary functions that will be useful throughout\n", "the notebook." ] }, { "cell_type": "code", "execution_count": 9, "id": "a9194616-3ea7-487f-bec1-1b41cdc13c91", "metadata": {}, "outputs": [], "source": [ "from IPython.display import JSON, DisplayObject\n", "\n", "def up42_asset_stac_url(url: str) -> str:\n", " \"\"\"Create a UP42 Spatial Asset service URL.\"\"\"\n", " return f\"https://api.up42.com/v2/assets/stac{url}\"\n", "\n", "def ppjson(json_data: dict, expand:bool=False) -> DisplayObject:\n", " \"\"\"Pretty print JSON data.\"\"\"\n", " return JSON(json_data, expanded=expand)" ] }, { "cell_type": "markdown", "id": "5ad8ddad-82e9-4727-99c4-3dc027eec97e", "metadata": {}, "source": [ "## Spatial Asset Service entry point\n" ] }, { "cell_type": "markdown", "id": "fdbdcf95-1c43-4867-b672-3b6ace68b422", "metadata": {}, "source": [ "\n", "## Listing collections\n", "\n", "Let us *naively* ask for the available collections." ] }, { "cell_type": "code", "execution_count": 6, "id": "19e5af35-80ad-4608-b8b2-d06183e6865b", "metadata": {}, "outputs": [ { "data": { "application/json": { "collections": [ { "assets": {}, "description": "High-resolution 1.5m SPOT images acquired daily on a global basis. The datasets are available starting from 2012.", "extent": { "spatial": { "bbox": [ [ 13.351, 52.523611111111, 13.35725000005, 52.526763888914 ] ] }, "temporal": { "interval": [ [ "2021-05-31T09:51:52.100000Z", "2021-05-31T09:51:52.100000Z" ] ] } }, "id": "0c6a851f-9780-488b-ae79-47fa5c7eab68", "keywords": [ "Airbus", "SPOT" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/0c6a851f-9780-488b-ae79-47fa5c7eab68", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/0c6a851f-9780-488b-ae79-47fa5c7eab68/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com/" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com/" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "ORT_SPOT7_20210531_095152300_000", "type": "Collection", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "048c8a94-eaaf-47fa-9789-b5b930404ddf", "up42-system:metadata_version": "0.0.5", "up42-system:workspace_id": "8710989c-fe75-46d8-9cbe-49e54bf70350" }, { "assets": {}, "description": "Very high-resolution 30cm Pleiades Neo images acquired bidaily on a global basis. The datasets are available starting from November 2021.", "extent": { "spatial": { "bbox": [ [ 54.31793246860514, 17.019094292371896, 54.45325584020295, 17.088556590348343 ] ] }, "temporal": { "interval": [ [ "2023-04-07T06:56:15Z", "2023-04-07T06:56:15Z" ] ] } }, "id": "4ecf5965-3fa0-4e9b-a822-ad74a5dd18a4", "keywords": [ "Airbus", "PléiadesNeo" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/4ecf5965-3fa0-4e9b-a822-ad74a5dd18a4", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/4ecf5965-3fa0-4e9b-a822-ad74a5dd18a4/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com/" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com/" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-order/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "PNEO4_202304070656155_PAN_SEN", "type": "Collection", "up42-order:id": "d7574551-aa40-4214-8c38-d1741a983192", "up42-order:status": "BEING_FULFILLED", "up42-product:collection_name": "pneo-tasking", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-product:product_id": "07c33a51-94b9-4509-84df-e9c13ea92b84", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "405527cf-1a7e-428f-85d0-8f19cfdb0d5b", "up42-system:metadata_version": "0.0.5", "up42-system:source": "TASKING", "up42-system:workspace_id": "313ba781-8aee-4585-b045-77eafa1fb592" }, { "assets": {}, "description": "Very high-resolution 30cm Pleiades Neo images acquired bidaily on a global basis. The datasets are available starting from November 2021.", "extent": { "spatial": { "bbox": [ [ 54.205398908250345, 17.01098855638842, 54.34225242252421, 17.136973752039395 ] ] }, "temporal": { "interval": [ [ "2023-03-15T07:03:47Z", "2023-03-15T07:03:47Z" ] ] } }, "id": "f9ab4697-8bd4-4284-870c-58472a31f909", "keywords": [ "Airbus", "PléiadesNeo" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/f9ab4697-8bd4-4284-870c-58472a31f909", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/f9ab4697-8bd4-4284-870c-58472a31f909/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com/" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com/" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-order/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "PNEO3_202303150703480_PAN_SEN", "type": "Collection", "up42-order:id": "d7574551-aa40-4214-8c38-d1741a983192", "up42-order:status": "BEING_FULFILLED", "up42-product:collection_name": "pneo-tasking", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-product:product_id": "07c33a51-94b9-4509-84df-e9c13ea92b84", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "7652f727-c071-4f06-a8d2-31ee15681403", "up42-system:metadata_version": "0.0.5", "up42-system:source": "TASKING", "up42-system:workspace_id": "313ba781-8aee-4585-b045-77eafa1fb592" }, { "assets": {}, "description": "Very high-resolution 30cm Pleiades Neo images acquired bidaily on a global basis. The datasets are available starting from November 2021.", "extent": { "spatial": { "bbox": [ [ 54.09102449711633, 16.9951895173228, 54.22532839880994, 17.156656996877444 ] ] }, "temporal": { "interval": [ [ "2023-04-10T07:03:40.400000Z", "2023-04-10T07:03:40.400000Z" ] ] } }, "id": "772816ae-be60-4e89-b35a-0b7ec1d8db82", "keywords": [ "Airbus", "PléiadesNeo" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/772816ae-be60-4e89-b35a-0b7ec1d8db82", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/772816ae-be60-4e89-b35a-0b7ec1d8db82/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com/" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com/" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-order/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "PNEO3_202304100703416_PAN_SEN", "type": "Collection", "up42-order:id": "d7574551-aa40-4214-8c38-d1741a983192", "up42-order:status": "BEING_FULFILLED", "up42-product:collection_name": "pneo-tasking", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-product:product_id": "07c33a51-94b9-4509-84df-e9c13ea92b84", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "d20acd73-4327-47b8-95ad-e5628f4376ff", "up42-system:metadata_version": "0.0.5", "up42-system:source": "TASKING", "up42-system:workspace_id": "313ba781-8aee-4585-b045-77eafa1fb592" }, { "assets": {}, "description": "Very high-resolution 30cm Pleiades Neo images acquired bidaily on a global basis. The datasets are available starting from November 2021.", "extent": { "spatial": { "bbox": [ [ -9.423687389795216, 38.69123683111964, -9.415284316365145, 38.69608402308488 ] ] }, "temporal": { "interval": [ [ "2023-01-27T11:45:45.200000Z", "2023-01-27T11:45:45.200000Z" ] ] } }, "id": "36a2cd2f-e4d9-49b5-897c-4cda35dcdbef", "keywords": [ "Airbus", "PléiadesNeo" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/36a2cd2f-e4d9-49b5-897c-4cda35dcdbef", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/36a2cd2f-e4d9-49b5-897c-4cda35dcdbef/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com/" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com/" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-order/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "PNEO3_202301271145452_PMS-FS_ORT", "type": "Collection", "up42-order:host_id": "390bbfcc-4066-4853-bacc-f6e6c4f35bd5", "up42-order:id": "53b9a6c5-5707-47a3-8bbc-d23d3f38d2ed", "up42-order:status": "FULFILLED", "up42-product:collection_name": "pneo", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-product:product_id": "17745de8-6e7d-4751-99cd-3f8e9e9d290e", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "519917e7-d6a0-467d-8128-26577710ce8b", "up42-system:metadata_version": "0.0.5", "up42-system:source": "ARCHIVE", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004" }, { "assets": {}, "description": "High-resolution 1.5m SPOT images acquired daily on a global basis. 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These assets can be\n", "multiple, e.g., a triple of images for a tri-stereo optical satellite\n", "acquisition. The list of collections is **paginated**, by default **10**\n", "items are returned and we get a link with the `next` relationship\n", "attribute that tells us how to get the next page of items. In this case:" ] }, { "cell_type": "code", "execution_count": 7, "id": "c01cd63d-f93a-405c-9b8e-207914b41fb1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://api.up42.com/v2/assets/stac/collections?token=next:ab984c55-d07b-4aac-992d-44243a77c55c'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "next(filter(lambda e: e.get(\"rel\") == \"next\", collections[\"links\"])).get(\"href\")" ] }, { "cell_type": "markdown", "id": "8a1e12fb-31e3-43cd-8c16-fb2209532def", "metadata": {}, "source": [ "But what if we want to have more than 10 items? Is there a way to do it?\n", "\n", "The answer is yes. In fact there is a query parameter `limit` that has a\n", "default value of 10 that we can set. Let’s ask for the first 20\n", "collections." ] }, { "cell_type": "code", "execution_count": 8, "id": "2c4ec405-8211-439f-8eb4-dc1207429021", "metadata": {}, "outputs": [ { "data": { "application/json": { "collections": [ { "assets": {}, "description": "High-resolution 1.5m SPOT images acquired daily on a global basis. 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If you want to\n", "constrain it just to your\n", "[**workspace**](https://docs.up42.com/getting-started/account/workspaces),\n", "then you can pass it as a **query string** argument: `workspace_id`." ] }, { "cell_type": "code", "execution_count": 10, "id": "e0cc075d-b6b1-4b0e-82eb-f9639321b041", "metadata": {}, "outputs": [ { "data": { "application/json": { "collections": [ { "assets": {}, "description": "Very high-resolution 30cm Pleiades Neo images acquired bidaily on a global basis. 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The datasets are available starting from November 2021.", "extent": { "spatial": { "bbox": [ [ 150.863532335, -24.2082879539, 151.016833932, -23.928920342 ] ] }, "temporal": { "interval": [ [ "2022-07-27T00:04:39.600000Z", "2022-07-27T00:04:39.600000Z" ] ] } }, "id": "da65f6fe-b41e-4938-8539-a25d0a9da7a3", "keywords": [ "Airbus", "PléiadesNeo" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/da65f6fe-b41e-4938-8539-a25d0a9da7a3", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/da65f6fe-b41e-4938-8539-a25d0a9da7a3/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com/" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com/" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "PNEO3_202207270004416_PAN_SEN", "type": "Collection", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "ab2a1132-ff56-4087-b59f-66619a56d5de", "up42-system:metadata_version": "0.0.4", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004" }, { "assets": {}, "description": "Very high-resolution 30cm Pleiades Neo images acquired bidaily on a global basis. The datasets are available starting from November 2021.", "extent": { "spatial": { "bbox": [ [ 150.859160849, -24.2112617425, 151.020613262, -24.0320209238 ] ] }, "temporal": { "interval": [ [ "2022-07-27T00:04:10.200000Z", "2022-07-27T00:04:10.200000Z" ] ] } }, "id": "554e0670-53e2-43dd-89f2-ff9635b262af", "keywords": [ "Airbus", "PléiadesNeo" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/554e0670-53e2-43dd-89f2-ff9635b262af", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/554e0670-53e2-43dd-89f2-ff9635b262af/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com/" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com/" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "PNEO3_202207270004114_PAN_SEN", "type": "Collection", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "fc84f7ed-4019-42d0-baa1-16a53dc63b7f", "up42-system:metadata_version": "0.0.4", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004" }, { "assets": {}, "description": "Very high-resolution 50cm Pleiades images acquired daily on a global basis. The datasets are available starting from 2012.", "extent": { "spatial": { "bbox": [ [ 3.09636805555556, 47.7050763888871, 3.1974236111118, 47.7743541666667 ] ] }, "temporal": { "interval": [ [ "2019-08-30T10:45:03.800000Z", "2019-08-30T10:45:03.800000Z" ] ] } }, "id": "76138f12-c7d3-4b78-9b39-0a32634a346e", "keywords": [ "Airbus", "Pléiades" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/76138f12-c7d3-4b78-9b39-0a32634a346e", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/76138f12-c7d3-4b78-9b39-0a32634a346e/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "DS_PHR1A_201908301044268_FR1_PX_E003N47_0221_04822", "type": "Collection", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "b374430a-3b5e-47f2-aa9c-e04388d0edef", "up42-system:metadata_version": "0.0.4", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004" }, { "assets": {}, "description": "Very high-resolution 30cm Pleiades Neo images acquired bidaily on a global basis. The datasets are available starting from November 2021.", "extent": { "spatial": { "bbox": [ [ 20.20664077154385, 67.84421138189992, 20.26901388475906, 67.86710709184203 ] ] }, "temporal": { "interval": [ [ "2022-05-20T10:03:33.400000Z", "2022-05-20T10:03:33.400000Z" ] ] } }, "id": "a8809066-58ea-47a2-b0b7-882fe7033009", "keywords": [ "Airbus", "PléiadesNeo" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/a8809066-58ea-47a2-b0b7-882fe7033009", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/a8809066-58ea-47a2-b0b7-882fe7033009/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com/" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com/" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-order/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "PNEO4_202205201003335_PMS-FS_ORT", "type": "Collection", "up42-order:id": "55b607b6-d4ff-431e-b81a-bf65539406e9", "up42-order:status": "FULFILLED", "up42-product:collection_name": "pneo", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-product:product_id": "17745de8-6e7d-4751-99cd-3f8e9e9d290e", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "34016dc1-10c3-4f7f-8274-4850b608cf49", "up42-system:metadata_version": "0.0.4", "up42-system:source": "ARCHIVE", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004" } ], "links": [ { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections?workspace_id=d39fe05a-400c-44f6-b770-86990f64b004&token=next:a8809066-58ea-47a2-b0b7-882fe7033009", "method": "GET", "rel": "next", "type": "application/json" } ] }, "text/plain": [ "" ] }, "execution_count": 10, "metadata": { "application/json": { "expanded": false, "root": "root" } }, "output_type": "execute_result" } ], "source": [ "access_token = get_auth_token_prod(username=USERNAME, password=PASSWORD)\n", "my_collections = get_request(access_token, url = \"https://api.up42.com/v2/assets/stac/collections\"+ f\"?workspace_id={WORKSPACE_ID}\")\n", "ppjson(collections)" ] }, { "cell_type": "markdown", "id": "f77e34b3-a867-4c7c-a934-7aac5b97319c", "metadata": {}, "source": [ "As you can see now the `up42-system:workspace_id` field is scoped to my\n", "workspace, from which I got the first 10 collections. The ordering is by\n", "descending chronological order of the `createdAt` field. This field is\n", "part of the [STAC\n", "extensions](https://docs.up42.com/developers/api-assets/stac-extensions)\n", "we defined.\n", "\n", "### What is a collection for the UP42 asset service?\n", "\n", "Before we proceed, let us install a package that makes traversing\n", "dictionaries easier to do." ] }, { "cell_type": "code", "execution_count": 11, "id": "e62758ff-9f6b-493b-affa-48477bf539a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: toolz in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (0.12.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install toolz" ] }, { "cell_type": "code", "execution_count": 12, "id": "569ed542-a352-403b-a591-c5aa2b5f1542", "metadata": {}, "outputs": [], "source": [ "from toolz.dicttoolz import get_in" ] }, { "cell_type": "markdown", "id": "b5f9c620-ebb2-45a7-a27b-aff71f23fae3", "metadata": {}, "source": [ "Let us now look inside a given collection. The first item given by the\n", "entry with index `0` of the `collections` list is:" ] }, { "cell_type": "code", "execution_count": 13, "id": "c77e7da9-951f-4fd9-a450-95ffe23df5ff", "metadata": {}, "outputs": [ { "data": { "application/json": { "assets": {}, "description": "Very high-resolution 30cm Pleiades Neo images acquired bidaily on a global basis. The datasets are available starting from November 2021.", "extent": { "spatial": { "bbox": [ [ -9.423687389795216, 38.69123683111964, -9.415284316365145, 38.69608402308488 ] ] }, "temporal": { "interval": [ [ "2023-01-27T11:45:45.200000Z", "2023-01-27T11:45:45.200000Z" ] ] } }, "id": "36a2cd2f-e4d9-49b5-897c-4cda35dcdbef", "keywords": [ "Airbus", "PléiadesNeo" ], "license": "proprietary", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/36a2cd2f-e4d9-49b5-897c-4cda35dcdbef", "rel": "self", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/36a2cd2f-e4d9-49b5-897c-4cda35dcdbef/items", "rel": "items", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "providers": [ { "name": "AIRBUS DS GEO", "roles": [ "producer" ], "url": "http://www.geo-airbusds.com/" }, { "name": "AIRBUS DS GEO", "roles": [ "processor" ], "url": "http://www.geo-airbusds.com/" } ], "stac_extensions": [ "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-order/v1.0.0/schema.json" ], "stac_version": "1.0.0", "summaries": {}, "title": "PNEO3_202301271145452_PMS-FS_ORT", "type": "Collection", "up42-order:host_id": "390bbfcc-4066-4853-bacc-f6e6c4f35bd5", "up42-order:id": "53b9a6c5-5707-47a3-8bbc-d23d3f38d2ed", "up42-order:status": "FULFILLED", "up42-product:collection_name": "pneo", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-product:product_id": "17745de8-6e7d-4751-99cd-3f8e9e9d290e", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "519917e7-d6a0-467d-8128-26577710ce8b", "up42-system:metadata_version": "0.0.5", "up42-system:source": "ARCHIVE", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004" }, "text/plain": [ "" ] }, "execution_count": 13, "metadata": { "application/json": { "expanded": true, "root": "root" } }, "output_type": "execute_result" } ], "source": [ "ppjson(get_in([\"collections\", 0], my_collections), expand=True)" ] }, { "cell_type": "markdown", "id": "71ce4ced-0070-4a3e-8a73-3435d340a9c9", "metadata": {}, "source": [ "We get, among others:\n", "\n", "- The STAC collection properties relative to:\n", " - spatial and temporal extents - the geometry envelope and the\n", " temporal envelope — in this case a single date.\n", "- the collection ID.\n", "- STAC extensions - specific to UP42 with links to the respective\n", " [JSON Schema](https://json-schema.org/).\n", "\n", "One of the big benefits of using STAC is that by adhering to\n", "[HATEOAS](https://en.wikipedia.org/wiki/HATEOAShttps://en.wikipedia.org/wiki/HATEOAS)\n", "it allows us to more easily explore an archive.\n", "\n", "## Inspecting STAC items\n", "\n", "Inspecting STAC items requires a STAC collection ID. We can either use\n", "the ID field or just use the `links` field with the `items` relationship\n", "and extract the URL in the `href` field." ] }, { "cell_type": "code", "execution_count": 14, "id": "2bcab460-9d96-43a0-8f98-ec49a8395e44", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195/items'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_first_coll_url = next(filter(lambda e: e.get(\"rel\") == \"items\",\n", " get_in([\"collections\", 1, \"links\"], my_collections)))[\"href\"]\n", "my_first_coll_url" ] }, { "cell_type": "code", "execution_count": 15, "id": "c1c6aada-69de-43b4-acf8-f9fbef210861", "metadata": {}, "outputs": [ { "data": { "application/json": { "features": [ { "assets": { "data": { "description": "Storage Data", "href": "https://api.up42.com/v2/assets/92abea3e-b7e1-4204-8b10-cc611c5da1bd", "roles": [ "data" ], "title": "Data", "type": "application/zip" } }, "bbox": [ -36.0392325684, -6.24436643267, -35.9972216858, -6.19679710409 ], "collection": "bd7454d3-7e39-4c55-9aff-f3dbd362c195", "geometry": { "coordinates": [ [ [ -36.0392325684, -6.19959884357 ], [ -35.9975404153, -6.19679710409 ], [ -35.9972216858, -6.2416896393 ], [ -36.0390613043, -6.24436643267 ], [ -36.0392325684, -6.19959884357 ] ] ], "type": "Polygon" }, "id": "167125ac-d7f4-44d5-b7a4-7c67c3730826", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195/items/167125ac-d7f4-44d5-b7a4-7c67c3730826", "rel": "self", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195", "rel": "collection", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "properties": { "constellation": "PNEO", "datetime": "2022-03-01T13:01:29.700000+00:00", "end_datetime": "2022-03-01T13:01:29.700000+00:00", "eo:cloud_cover": 0, "gsd": 0.3656197339296341, "platform": "PNEO-4", "proj:epsg": null, "start_datetime": "2022-03-01T13:01:29.700000+00:00", "title": "PNEO4_202203011301300_PAN_SEN_R1C1", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "92abea3e-b7e1-4204-8b10-cc611c5da1bd", "up42-system:metadata_version": "0.0.5", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004", "view:azimuth": 254.54050912419873, "view:incidence_angle": 29.888618077250204, "view:sun_azimuth": 95.83586309859096, "view:sun_elevation": 66.327687348278 }, "stac_extensions": [ "https://stac-extensions.github.io/eo/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json", "https://stac-extensions.github.io/view/v1.0.0/schema.json", "https://stac-extensions.github.io/projection/v1.0.0/schema.json" ], "stac_version": "1.0.0", "type": "Feature" } ], "links": [ { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195/items", "rel": "self", "type": "application/json" } ], "type": "FeatureCollection" }, "text/plain": [ "" ] }, "execution_count": 15, "metadata": { "application/json": { "expanded": true, "root": "root" } }, "output_type": "execute_result" } ], "source": [ "my_first_assets = get_request(access_token, url=my_first_coll_url)\n", "ppjson(my_first_assets, expand=True)" ] }, { "cell_type": "markdown", "id": "ef5c3c7b-273b-430b-837a-76c3464e634a", "metadata": {}, "source": [ "In this case there is only one asset. But it may occur that there are\n", "multiple assets inside a collection. For example a pair of assets for\n", "stereo, and a triple of assets for a tri-stereo image. For further\n", "information on the STAC implementation for the asset service please\n", "consult the relevant\n", "[documentation](https://docs.up42.com/developers/api-assets/stac-about).\n", "\n", "## Listing a specific STAC item\n", "\n", "In this case there is only one asset, but it is instructive to see how\n", "you can obtain a specific item from a collection. For that we need the\n", "asset ID, given above as the feature ID, however it is more expedient to\n", "use the URL of the link with the `self` relationship given for the first\n", "(and unique, in this case) feature." ] }, { "cell_type": "code", "execution_count": 16, "id": "11e55001-c30c-4c80-ad60-faabf2389037", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195/items/167125ac-d7f4-44d5-b7a4-7c67c3730826'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_first_asset_url = next(filter(lambda e: e.get(\"rel\") == \"self\",\n", " get_in([\"features\", 0, \"links\"], my_first_assets)))[\"href\"]\n", "my_first_asset_url" ] }, { "cell_type": "code", "execution_count": 17, "id": "72434c97-198e-4de4-8afa-70ca1e65aea0", "metadata": {}, "outputs": [ { "data": { "application/json": { "assets": { "data": { "description": "Storage Data", "href": "https://api.up42.com/v2/assets/92abea3e-b7e1-4204-8b10-cc611c5da1bd", "roles": [ "data" ], "title": "Data", "type": "application/zip" } }, "bbox": [ -36.0392325684, -6.24436643267, -35.9972216858, -6.19679710409 ], "collection": "bd7454d3-7e39-4c55-9aff-f3dbd362c195", "geometry": { "coordinates": [ [ [ -36.0392325684, -6.19959884357 ], [ -35.9975404153, -6.19679710409 ], [ -35.9972216858, -6.2416896393 ], [ -36.0390613043, -6.24436643267 ], [ -36.0392325684, -6.19959884357 ] ] ], "type": "Polygon" }, "id": "167125ac-d7f4-44d5-b7a4-7c67c3730826", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195/items/167125ac-d7f4-44d5-b7a4-7c67c3730826", "rel": "self", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/bd7454d3-7e39-4c55-9aff-f3dbd362c195", "rel": "collection", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "properties": { "constellation": "PNEO", "datetime": "2022-03-01T13:01:29.700000+00:00", "end_datetime": "2022-03-01T13:01:29.700000+00:00", "eo:cloud_cover": 0, "gsd": 0.3656197339296341, "platform": "PNEO-4", "proj:epsg": null, "start_datetime": "2022-03-01T13:01:29.700000+00:00", "title": "PNEO4_202203011301300_PAN_SEN_R1C1", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "92abea3e-b7e1-4204-8b10-cc611c5da1bd", "up42-system:metadata_version": "0.0.5", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004", "view:azimuth": 254.54050912419873, "view:incidence_angle": 29.888618077250204, "view:sun_azimuth": 95.83586309859096, "view:sun_elevation": 66.327687348278 }, "stac_extensions": [ "https://stac-extensions.github.io/eo/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json", "https://stac-extensions.github.io/view/v1.0.0/schema.json", "https://stac-extensions.github.io/projection/v1.0.0/schema.json" ], "stac_version": "1.0.0", "type": "Feature" }, "text/plain": [ "" ] }, "execution_count": 17, "metadata": { "application/json": { "expanded": true, "root": "root" } }, "output_type": "execute_result" } ], "source": [ "my_first_assets = get_request(access_token, url=my_first_coll_url)\n", "ppjson(my_first_assets, expand=True)" ] }, { "cell_type": "markdown", "id": "d34f60c0-e9a5-4fec-ab09-12488995ddf5", "metadata": {}, "source": [ "## Search for assets across collections: queryables\n", "\n", "One of the most important aspects of the asset STAC service is that it\n", "provides a unified interface to search for data. It relies on the [STAC\n", "Item Search](https://api.stacspec.org/v1.0.0-rc.1/item-search/)\n", "specification.\n", "\n", "The UP42 Asset service implements the [full featured\n", "filtering](https://api.stacspec.org/v1.0.0-rc.1/item-search/#tag/Item-Search/operation/postItemSearch).\n", "It relies on the [STAC filter\n", "API](https://github.com/stac-api-extensions/filter/blob/main/openapi.yaml).\n", "Simple search filters and\n", "[CQL2](https://docs.ogc.org/DRAFTS/21-065.html) based filters are\n", "supported.\n", "\n", "We can obtain the list of the possible CQL2 based filters with:" ] }, { "cell_type": "code", "execution_count": 5, "id": "ea456540-7dd5-4492-9dc4-bd9a84aebcf8", "metadata": {}, "outputs": [ { "data": { "application/json": { "$id": "https://api.up42.com/v2/assets/stac/queryables", "$schema": "https://json-schema.org/draft/2019-09/schema", "description": "Queryables for searching STAC Storage.", "properties": { "asset_id": { "description": "UP42 Asset Id", "title": "asset_id", "type": "string" }, "cloud_cover": { "description": "Cloud Coverage", "title": "cloud_cover", "type": "float" }, "collection_name": { "description": "UP42 Collection Name", "title": "collection_name", "type": "string" }, "constellation": { "description": "Platform Constellation", "title": "constellation", "type": "string" }, "datetime": { "description": "Date and time the feature was captured", "title": "datetime", "type": "timestamp" }, "eo:cloud_cover": { "description": "Cloud Coverage", "title": "eo:cloud_cover", "type": "float" }, "gsd": { "description": "Ground Sampling Distance", "title": "gsd", "type": "float" }, "order_id": { "description": "UP42 Order Id", "title": "order_id", "type": "string" }, "tags": { "description": "UP42 Asset Tags", "title": "tags", "type": "array" }, "title": { "description": "User Title", "title": "title", "type": "string" }, "workspace_id": { "description": "UP42 Workspace Id", "title": "workspace_id", "type": "string" } }, "title": "STAC Storage Queryables", "type": "object" }, "text/plain": [ "" ] }, "execution_count": 5, "metadata": { "application/json": { "expanded": true, "root": "root" } }, "output_type": "execute_result" } ], "source": [ "\n", "queryables = get_request(access_token, url = \"https://api.up42.com/v2/assets/stac/queryables\")\n", "ppjson(queryables, expand=True)" ] }, { "cell_type": "markdown", "id": "8d352148-d33b-4e17-a734-eb320e2c943b", "metadata": {}, "source": [ "Let us now search for all data that intersects a given geometry. In this\n", "case I want to know of all AOIs that contain a particular Point Of\n", "Interest (POI)." ] }, { "cell_type": "code", "execution_count": 19, "id": "e10a16c7-310b-4928-943e-1ed09fe1281b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\"coordinates\": [-8.98691, 38.469947], \"type\": \"Point\"}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from geojson import Point, Polygon, MultiPolygon, FeatureCollection, Feature\n", "\n", "my_first_poi = Point((-8.986910071109419,38.46994674758601))\n", "my_first_poi" ] }, { "cell_type": "markdown", "id": "398dcc61-67e6-4ccd-8dad-1755fcbef6fa", "metadata": {}, "source": [ "We need now to build a request body, since the search is done with a\n", "POST request." ] }, { "cell_type": "code", "execution_count": 20, "id": "df98a178-0fed-4ec6-bde3-136f37d5a9f8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'intersects': {\"coordinates\": [-8.98691, 38.469947], \"type\": \"Point\"}}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "req_body_poi = dict(intersects = my_first_poi)\n", "req_body_poi" ] }, { "cell_type": "markdown", "id": "a03d0f3a-587b-49b8-bda9-48dcff19336f", "metadata": {}, "source": [ "Creating a convenience function." ] }, { "cell_type": "code", "execution_count": 11, "id": "19efd4de-b844-4d9d-bff1-9799199ef3e9", "metadata": {}, "outputs": [], "source": [ "def search_item(req_body: dict, access_token: str):\n", " \"\"\"Searches for a STAC item according to the given criteria.\"\"\"\n", " headers = {\n", " \"content-type\": \"application/json\",\n", " \"Authorization\": f\"Bearer {access_token}\"\n", " }\n", " return requests.post(\n", " url=\"https://api.up42.com/v2/assets/stac/search\",\n", " headers=headers,\n", " json=req_body,\n", " )\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "ad252123-2696-4c71-822e-4d943b325e50", "metadata": {}, "outputs": [ { "data": { "application/json": { "features": [ { "assets": { "data": { "description": "Storage Data", "href": "https://api.up42.com/v2/assets/630bd8cd-c4c2-4619-9a88-5b3421ffd1de", "roles": [ "data" ], "title": "Data", "type": "application/zip" } }, "bbox": [ -8.992222054150913, 38.46552423989927, -8.968630844922524, 38.483829164081016 ], "collection": "e3e8ecde-6328-4bb2-bf06-eeffcfcc0978", "geometry": { "coordinates": [ [ [ -8.970516192795307, 38.483829164081016 ], [ -8.969003136298957, 38.48288700320683 ], [ -8.968630844922524, 38.482111858604256 ], [ -8.968642327826503, 38.48207130696972 ], [ -8.969565423041054, 38.48174711275871 ], [ -8.969348067535785, 38.48060251189329 ], [ -8.97187629060627, 38.480166056393756 ], [ -8.97708711322504, 38.4802663253012 ], [ -8.981352415160988, 38.47907747662965 ], [ -8.981432678381337, 38.479041440729354 ], [ -8.984712266276473, 38.47600481509272 ], [ -8.982695061218294, 38.473814577442 ], [ -8.986891621124045, 38.469791186640876 ], [ -8.990422699558534, 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"2a1dfd20-deaf-4ce7-bc67-63c69ae55e7e", "geometry": { "coordinates": [ [ [ -8.970516192795307, 38.483829164081016 ], [ -8.969003136298957, 38.48288700320683 ], [ -8.968630844922524, 38.482111858604256 ], [ -8.968642327826503, 38.48207130696972 ], [ -8.969565423041054, 38.48174711275871 ], [ -8.969348067535785, 38.48060251189329 ], [ -8.97187629060627, 38.480166056393756 ], [ -8.97708711322504, 38.4802663253012 ], [ -8.981352415160988, 38.47907747662965 ], [ -8.981432678381337, 38.479041440729354 ], [ -8.984712266276473, 38.47600481509272 ], [ -8.982695061218294, 38.473814577442 ], [ -8.986891621124045, 38.469791186640876 ], [ -8.990422699558534, 38.46709688704083 ], [ -8.990164996618832, 38.46552423989927 ], [ -8.991964631241645, 38.4656190050795 ], [ -8.992222054150913, 38.47016566713853 ], [ -8.989779464974129, 38.47637035954537 ], [ -8.981065183212577, 38.4814115801504 ], [ -8.970516192795307, 38.483829164081016 ] ] ], "type": "Polygon" }, "id": "c6148201-7f57-47d2-81b3-08024cf3fcac", "links": [ { "href": "https://api.up42.com/v2/assets/stac/collections/2a1dfd20-deaf-4ce7-bc67-63c69ae55e7e/items/c6148201-7f57-47d2-81b3-08024cf3fcac", "rel": "self", "type": "application/geo+json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/2a1dfd20-deaf-4ce7-bc67-63c69ae55e7e", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/collections/2a1dfd20-deaf-4ce7-bc67-63c69ae55e7e", "rel": "collection", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "properties": { "constellation": "PHR", "datetime": "2022-03-18T11:24:06+00:00", "end_datetime": "2022-03-18T11:24:06+00:00", "eo:cloud_cover": 0, "gsd": 0.7562325080525973, "platform": "PHR-1B", "proj:epsg": 32629, "start_datetime": "2022-03-18T11:24:06+00:00", "title": "DS_PHR1B_202203181123290_FR1_PX_W009N38_0119_07844_R1C1", "up42-order:host_id": "89df2d1f-8b91-46c9-b89c-5457606066a4", "up42-order:id": "d28a249c-489b-4409-b939-dceb57d5b919", "up42-order:status": "FULFILLED", "up42-product:collection_name": "phr", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-product:product_id": "4f1b2f62-98df-4c74-81f4-5dce45deee99", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "696f6b22-523a-4f57-a6a3-16efa5b1edb5", "up42-system:metadata_version": "0.0.4", "up42-system:source": "ARCHIVE", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004", "view:azimuth": 179.08569011330297, "view:incidence_angle": 18.141957311334412, "view:sun_azimuth": 150.26387457509006, "view:sun_elevation": 46.60727885283659 }, "stac_extensions": [ "https://stac-extensions.github.io/view/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-order/v1.0.0/schema.json", "https://stac-extensions.github.io/eo/v1.0.0/schema.json", "https://stac-extensions.github.io/projection/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json" ], "stac_version": "1.0.0", "type": "Feature" } ], "links": [ { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/search", "rel": "self", "type": "application/json" } ], "type": "FeatureCollection" }, "text/plain": [ "" ] }, "execution_count": 22, "metadata": { "application/json": { "expanded": false, "root": "root" } }, "output_type": "execute_result" } ], "source": [ "search_results = search_item(my_first_poi, access_token).json()\n", "ppjson(search_results)" ] }, { "cell_type": "markdown", "id": "164b183e-4cec-4165-b7d1-2aa06518807b", "metadata": {}, "source": [ "Now that we have the assets that intersect the point we have given\n", "`my_first_poi` let us draw a map centered on the point and add a marker\n", "placed on it.\n", "\n", "## Visualizing the collection\n", "\n", "First we need to install\n", "[ipyleaflet](https://ipyleaflet.readthedocs.io/en/latest/)." ] }, { "cell_type": "code", "execution_count": 23, "id": "6f6bcc3d-dc2b-4871-a8b8-f64666c4aee3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: ipyleaflet in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (0.17.2)\n", "Requirement already satisfied: ipywidgets<9,>=7.6.0 in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (from ipyleaflet) (8.0.4)\n", "Requirement already satisfied: traittypes<3,>=0.2.1 in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (from ipyleaflet) (0.2.1)\n", "Requirement already satisfied: xyzservices>=2021.8.1 in 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satisfied: wcwidth in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.30->ipython>=6.1.0->ipywidgets<9,>=7.6.0->ipyleaflet) (0.2.6)\n", "Requirement already satisfied: executing>=1.2.0 in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets<9,>=7.6.0->ipyleaflet) (1.2.0)\n", "Requirement already satisfied: asttokens>=2.1.0 in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets<9,>=7.6.0->ipyleaflet) (2.2.1)\n", "Requirement already satisfied: pure-eval in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets<9,>=7.6.0->ipyleaflet) (0.2.2)\n", "Requirement already satisfied: six in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets<9,>=7.6.0->ipyleaflet) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install ipyleaflet" ] }, { "cell_type": "markdown", "id": "31dd642c-363e-4f03-a74c-c1396e400866", "metadata": {}, "source": [ "Now importing the needed methods/functions from that module." ] }, { "cell_type": "code", "execution_count": 24, "id": "34bb3318-9af4-46ba-b6ce-f9ab3ed4f48e", "metadata": {}, "outputs": [], "source": [ "from ipyleaflet import Map, GeoJSON, Marker, AwesomeIcon" ] }, { "cell_type": "markdown", "id": "50fd6c54-1553-4b03-b7f7-797820aa30c3", "metadata": {}, "source": [ "We need to define a center for the map. The center is given in EPSG:4326\n", "(WGS84 datum) as (latitude, longitude). In the definition above of POI\n", "we have considered a cartesian coordinate order, where longitude\n", "corresponds to $x$ and latitude to $y$. So they need to be reversed:\n", "$(x, y) \\rightarrow (y, x)$. It also needs to be in the form of a tuple." ] }, { "cell_type": "code", "execution_count": 13, "id": "82aa64be-3213-489b-8e6d-693d2cc83db8", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'my_first_poi' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m my_first_map_center \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\u001b[43mmy_first_poi\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoordinates\u001b[39m\u001b[38;5;124m\"\u001b[39m][::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n", "\u001b[0;31mNameError\u001b[0m: name 'my_first_poi' is not defined" ] } ], "source": [ "my_first_map_center = tuple(my_first_poi[\"coordinates\"][::-1])" ] }, { "cell_type": "markdown", "id": "5afe27de-4805-4e98-9458-95b68c28879b", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 26, "id": "e34d9735-f901-4728-807f-71556bdda803", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9468fadf793b49fb85b06c65d5b2875d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map(center=[38.469947, -8.98691], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', …" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mymap = Map(center=my_first_map_center, zoom=14)\n", "\n", "# Add the AOI to the map. First style it and then add it.\n", "aoi_layer = GeoJSON(\n", " data=search_results,\n", " style={\"opacity\": 1, \"dashArray\": \"9\", \"fillOpacity\": 0.5, \"weight\": 1},\n", " hover_style={\"color\": \"yellow\", \"dashArray\": \"0\", \"fillOpacity\": 0.5},\n", ")\n", "# Add a marker layer at the center.\n", "marker_layer = Marker(location=my_first_map_center,\n", " draggable=False,\n", " icon=AwesomeIcon(name=\"close\",\n", " color_marker=\"green\"))\n", "mymap.add_layer(aoi_layer)\n", "mymap.add_layer(marker_layer)\n", "mymap" ] }, { "cell_type": "markdown", "id": "7aa43c1f-2543-469d-b5e7-5d46148a1670", "metadata": {}, "source": [ "## Complex searches\n", "\n", "We can do complex searches using the [Common Query Language version\n", "2](https://docs.ogc.org/DRAFTS/21-065.html) (CQL2) as defined in the\n", "STAC\n", "[specification](https://api.stacspec.org/v1.0.0-rc.1/item-search/#tag/Item-Search/operation/postItemSearch)\n", "for full-featured search of STAC items.\n", "\n", "To illustrate this we are going to perform a search for all assets that\n", "intersect a given geometry and we want those with a cloud cover below\n", "10%. Of course that the latter only makes sense for optical imagery.\n", "\n", "### Building the query\n", "\n", "We need to build the query. It is composed of two parts:\n", "\n", "1. An `intersects` field set to the geometry we are using to compute\n", " the intersections.\n", "2. A CQL2 filter for obtaining only the assets where the cloud coverage\n", " is below 10%.\n", "\n", "#### Intersection with the geometry\n", "\n", "We are going to read a GeoJSON file with the geometry." ] }, { "cell_type": "code", "execution_count": 12, "id": "6e9c8d0f-f7ca-4bd3-a7f9-5a95a508ecbe", "metadata": {}, "outputs": [], "source": [ "path2geom = \"../examples/portugal_envelope.geojson\"" ] }, { "cell_type": "code", "execution_count": 28, "id": "3d12e169-2a65-41ec-9b27-8ad7ab15b2ef", "metadata": {}, "outputs": [], "source": [ "import json\n", "with open(path2geom, \"r\") as f:\n", " geom_map = json.load(f)" ] }, { "cell_type": "markdown", "id": "309fbf6b-b18a-46fc-9e81-054af5dbfab9", "metadata": {}, "source": [ "We want to draw a map of the geometry. We need to find the center. We\n", "can use [shapely](https://pypi.org/project/shapely/) for that." ] }, { "cell_type": "code", "execution_count": 29, "id": "50888b6a-aa2a-4d32-b733-2770f12eeb17", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: shapely in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (2.0.1)\n", "Requirement already satisfied: numpy>=1.14 in /Users/appa/.virtualenvs/httpx-notebooks/lib/python3.11/site-packages (from shapely) (1.24.2)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install shapely" ] }, { "cell_type": "code", "execution_count": 30, "id": "b954a17d-419a-4ed5-856c-76c5a1f546c5", "metadata": {}, "outputs": [], "source": [ "from shapely.geometry import shape, mapping" ] }, { "cell_type": "markdown", "id": "df9ce597-ddb5-4e15-bdc1-8149ed099496", "metadata": {}, "source": [ "We need to extract the geonetry from the collection. Let us see first\n", "how many features there are." ] }, { "cell_type": "code", "execution_count": 31, "id": "284eac17-9aa5-4285-aff7-506eb6b2cf94", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(get_in([\"features\"], geom_map))" ] }, { "cell_type": "markdown", "id": "253d531e-a75d-4706-91dc-e947f7f9e480", "metadata": {}, "source": [ "Only one feature. So to extract the geometry we need only to extract the\n", "value for the key `[\"features\"][0][\"geometry\"]`." ] }, { "cell_type": "code", "execution_count": 32, "id": "3863b905-f534-4a36-b57b-b1747f872dc6", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shape_geom_map = shape(get_in([\"features\", 0, \"geometry\"], geom_map))\n", "shape_geom_map" ] }, { "cell_type": "markdown", "id": "d4ea51ff-46c9-4d5e-9843-2b0e03c404a9", "metadata": {}, "source": [ "The centroid is given by:" ] }, { "cell_type": "code", "execution_count": 33, "id": "539fe806-bd9d-46a0-abe2-70366a845df3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "POINT (-8.115254781697322 39.67809052275443)\n" ] } ], "source": [ "intersect_map_center = shape_geom_map.centroid\n", "print(intersect_map_center)" ] }, { "cell_type": "markdown", "id": "58a16086-980a-4f56-8482-045b2b710dee", "metadata": {}, "source": [ "Let us draw the map." ] }, { "cell_type": "code", "execution_count": 34, "id": "69ce45a6-b3c7-4401-8156-da8eea5f21e9", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8fd206b2c75c40b096bf4e612e269424", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map(center=[39.67809052275443, -8.115254781697322], controls=(ZoomControl(options=['position', 'zoom_in_text',…" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "intersect_map = Map(center=(intersect_map_center.y, intersect_map_center.x), zoom=6)\n", "\n", "# Add the AOI to the map. First style it and then add it.\n", "intersect_layer = GeoJSON(\n", " data=geom_map,\n", " style={\"opacity\": 1, \"dashArray\": \"9\", \"fillOpacity\": 0.5, \"weight\": 1},\n", " hover_style={\"color\": \"yellow\", \"dashArray\": \"0\", \"fillOpacity\": 0.5},\n", ")\n", "# Add a marker layer at the center.\n", "intersect_marker_layer = Marker(location=(intersect_map_center.y, intersect_map_center.x),\n", " draggable=False, icon=AwesomeIcon(name=\"close\",\n", " color_marker=\"green\"))\n", "intersect_map.add_layer(intersect_layer)\n", "intersect_map.add_layer(intersect_marker_layer)\n", "intersect_map" ] }, { "cell_type": "markdown", "id": "bace9582-df90-460b-b5c3-4fb212a37ecd", "metadata": {}, "source": [ "This is the geometry for which we want to look for intersecting assets.\n", "We can start to build the request body dictionary. We need to get the\n", "shapely geometry to be a simple dictionary and not a shapely type." ] }, { "cell_type": "code", "execution_count": 35, "id": "5180f88d-6809-43df-abc9-19f713cfd5b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "shapely.geometry.polygon.Polygon" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(shape_geom_map)" ] }, { "cell_type": "code", "execution_count": 36, "id": "49c1f8dd-21fc-473e-aea4-75763002e316", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'intersects': {'type': 'Polygon',\n", " 'coordinates': (((-9.52171458613654, 36.853921776429516),\n", " (-7.1631974367996065, 36.82161569679076),\n", " (-5.996066469639629, 42.04883107989275),\n", " (-9.841226786721336, 42.14603469432703),\n", " (-9.52171458613654, 36.853921776429516)),)}}" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "intersect_req_body = dict(intersects = mapping(shape_geom_map))\n", "intersect_req_body" ] }, { "cell_type": "markdown", "id": "5bb1a03f-44f1-42bc-b724-d449fbb77e42", "metadata": {}, "source": [ "Now is the time to add the CQL2 filter. This is always something of the\n", "form: ` `. The arguments can be composed of\n", "other operators and args. In our case we want to limit all results to be\n", "with a cloud cover less than 10%. In its simplest form the arguments are\n", "logical clauses that must be satisfied. These clauses can be represented\n", "in JSON as an array of operands and operators. In this case, concretely:" ] }, { "cell_type": "code", "execution_count": 37, "id": "cad611cd-393d-4d11-96aa-6cfe46db2906", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'filter': {'args': [{'property': 'eo:cloud_cover'}, 10], 'op': '<'}}" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_cloud_cover_dict = dict(filter=dict(args=[\n", " dict(property=\"eo:cloud_cover\"), 10],\n", " op=\"<\"))\n", "my_cloud_cover_dict" ] }, { "cell_type": "markdown", "id": "e47e2d8b-8401-4931-a6ea-cf331d3f03da", "metadata": {}, "source": [ "The structure of CQL2 is basically a [syntax\n", "tree](https://en.wikipedia.org/wiki/Abstract_syntax_tree) with the nodes\n", "being the operators and the root being the node `filter`.\n", "\n", "Now we can update the request body with this filter." ] }, { "cell_type": "code", "execution_count": 38, "id": "114332f7-9e1e-4cdd-abfb-2e81a908d461", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'intersects': {'type': 'Polygon',\n", " 'coordinates': (((-9.52171458613654, 36.853921776429516),\n", " (-7.1631974367996065, 36.82161569679076),\n", " (-5.996066469639629, 42.04883107989275),\n", " (-9.841226786721336, 42.14603469432703),\n", " (-9.52171458613654, 36.853921776429516)),)},\n", " 'filter': {'args': [{'property': 'eo:cloud_cover'}, 10], 'op': '<'}}" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_intersect_req_body = intersect_req_body | my_cloud_cover_dict\n", "new_intersect_req_body" ] }, { "cell_type": "markdown", "id": "dc328550-7475-4946-a500-9f15f2d5f8e1", "metadata": {}, "source": [ "We can now perform the search:" ] }, { "cell_type": "code", "execution_count": 39, "id": "7b7ed597-e70b-428b-93bf-97ea603e3e27", "metadata": {}, "outputs": [ { "data": { "application/json": { "features": [ { "assets": { "data": { "description": "Storage Data", "href": 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"application/json" }, { "href": "https://api.up42.com/v2/assets/stac/search", "rel": "self", "type": "application/json" }, { "body": { "filter": { "args": [ { "property": "eo:cloud_cover" }, 10 ], "op": "<" }, "intersects": { "coordinates": [ [ [ -9.52171458613654, 36.853921776429516 ], [ -7.1631974367996065, 36.82161569679076 ], [ -5.996066469639629, 42.04883107989275 ], [ -9.841226786721336, 42.14603469432703 ], [ -9.52171458613654, 36.853921776429516 ] ] ], "type": "Polygon" }, "token": "next:7999821e-89be-4eef-82f5-f28c9f7b9578" }, "href": "https://api.up42.com/v2/assets/stac/search", "method": "POST", "rel": "next", "type": "application/json" } ], "type": "FeatureCollection" }, "text/plain": [ "" ] }, "execution_count": 39, "metadata": { "application/json": { "expanded": false, "root": "root" } }, "output_type": "execute_result" } ], "source": [ "complex_search_results = search_item(new_intersect_req_body, access_token).json()\n", "ppjson(complex_search_results)" ] }, { "cell_type": "markdown", "id": "56bea382-7286-4f1b-bbf4-e8d9c2e63b41", "metadata": {}, "source": [ "We have more than 10 results, since I want to have only the ones in my\n", "workspace I need to add my workspace as a CQL2 filter to the request." ] }, { "cell_type": "code", "execution_count": 40, "id": "ae495c81-9b49-4205-8d96-5326c091542f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'filter': {'args': [{'args': [{'property': 'eo:cloud_cover'}, 10], 'op': '<'},\n", " {'args': [{'property': 'workspace_id'},\n", " 'd39fe05a-400c-44f6-b770-86990f64b004'],\n", " 'op': '='}],\n", " 'op': 'and'}}" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cloud_cover_workspace_dict = dict(filter=\n", " dict(args=[\n", " dict(args=[dict(property=\"eo:cloud_cover\"), 10],\n", " op=\"<\"),\n", " dict(args=[dict(property=\"workspace_id\"), WORKSPACE_ID],\n", " op=\"=\")],\n", " op=\"and\")\n", " )\n", "cloud_cover_workspace_dict" ] }, { 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"https://api.up42.com/v2/assets/stac/collections/00d34533-5d69-4d71-9808-8333792e81bd", "rel": "collection", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" } ], "properties": { "constellation": "PHR", "datetime": "2022-04-18T11:36:44.900000+00:00", "end_datetime": "2022-04-18T11:36:44.900000+00:00", "eo:cloud_cover": 0, "gsd": 0.7593376882942882, "platform": "PHR-1B", "proj:epsg": 32629, "start_datetime": "2022-04-18T11:36:44.900000+00:00", "title": "DS_PHR1B_202204181136079_FR1_PX_W009N37_0324_01783_R1C1", "up42-order:host_id": "cab98f86-2b91-4487-9a50-0f02ca2fb8ed", "up42-order:id": "c74c90a3-a8d1-4433-be51-78c4b84b76c9", "up42-order:status": "FULFILLED", "up42-product:collection_name": "phr", "up42-product:data_type": "raster", "up42-product:modality": "multispectral", "up42-product:product_id": "4f1b2f62-98df-4c74-81f4-5dce45deee99", "up42-system:account_id": "3c86279b-610a-4dba-ad12-abe3bdc51428", "up42-system:asset_id": "55dfa9f4-361f-44dd-b4c4-f1f05ad02cd3", "up42-system:metadata_version": "0.0.4", "up42-system:source": "ARCHIVE", "up42-system:workspace_id": "d39fe05a-400c-44f6-b770-86990f64b004", "view:azimuth": 179.9190797665755, "view:incidence_angle": 18.791437964465626, "view:sun_azimuth": 150.68128503948043, "view:sun_elevation": 59.97044901720295 }, "stac_extensions": [ "https://stac-extensions.github.io/view/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-system/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-order/v1.0.0/schema.json", "https://stac-extensions.github.io/eo/v1.0.0/schema.json", "https://stac-extensions.github.io/projection/v1.0.0/schema.json", "https://api.up42.com/stac-extensions/up42-product/v1.0.0/schema.json" ], "stac_version": "1.0.0", "type": "Feature" } ], "links": [ { "href": "https://api.up42.com/v2/assets/stac/", "rel": "root", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/", "rel": "parent", "type": "application/json" }, { "href": "https://api.up42.com/v2/assets/stac/search", "rel": "self", "type": "application/json" } ], "type": "FeatureCollection" }, "text/plain": [ "" ] }, "execution_count": 41, "metadata": { "application/json": { "expanded": false, "root": "root" } }, "output_type": "execute_result" } ], "source": [ "complex_search_results = search_item(intersect_req_body | cloud_cover_workspace_dict, access_token).json()\n", "ppjson(complex_search_results)" ] }, { "cell_type": "markdown", "id": "13242d1e-8332-480b-b1f8-b66951685fc4", "metadata": {}, "source": [ "We have 7 results. Let us try to visualize the returned items on the\n", "map. We see that this is exactly the AOI we have above in\n", "@map-collection-point." ] }, { "cell_type": "code", "execution_count": 42, "id": "4d7c97ac-59e2-4b40-8a25-668e349ecd10", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8fd206b2c75c40b096bf4e612e269424", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map(center=[39.67809052275443, -8.115254781697322], controls=(ZoomControl(options=['position', 'zoom_in_text',…" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "complex_search_results_layer = GeoJSON(data=complex_search_results,\n", " style={\"color\":\"red\", \"opacity\": 1,\n", " \"dashArray\": \"9\", \"fillOpacity\": 0.5,\n", " \"weight\": 1},\n", " hover_style={\"color\": \"white\", \"dashArray\": \"0\", \"fillOpacity\": 0.5},\n", ")\n", "intersect_map.add_layer(complex_search_results_layer)\n", "intersect_map" ] }, { "cell_type": "markdown", "id": "b02d81b7-cc13-4972-b178-958cedbd18d1", "metadata": {}, "source": [ "The features returned are too small to be easily detected without\n", "panning & zooming. To make it clearer we are going to recenter the map\n", "on the point of interest we introduced above and display only the\n", "returned features." ] }, { "cell_type": "code", "execution_count": 43, "id": "a5ad784f-28d5-4849-8d0d-651e565b77bd", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9123c8b0913b47549b39be2090822dd2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map(center=[38.469947, -8.98691], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', …" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "complex_search_results_map = Map(center=my_first_map_center, zoom=12)\n", "complex_search_results_map.add_layer(complex_search_results_layer)\n", "complex_search_results_map" ] }, { "cell_type": "markdown", "id": "61261583-8856-4868-89db-850b45ece6ba", "metadata": {}, "source": [ "We can see clearly the returned features in this area.\n", "\n", "## Downloading the assets\n", "\n", "We can easily extract the assets ID for downloading." ] }, { "cell_type": "code", "execution_count": 44, "id": "fb621139-6f87-40dd-8d91-c81ae794a65b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['630bd8cd-c4c2-4619-9a88-5b3421ffd1de',\n", " '828b2da8-b322-4e19-9abc-3917e0e36986',\n", " 'f5080d5d-cb90-4cb5-9caf-b1e870865967',\n", " 'fa438b5e-0903-4787-8958-45a376dd9b04',\n", " '666191ca-0f28-4f49-9bae-597865187eb1',\n", " '696f6b22-523a-4f57-a6a3-16efa5b1edb5',\n", " '55dfa9f4-361f-44dd-b4c4-f1f05ad02cd3']" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "complex_search_asset_ids = list(map(lambda e: get_in([\"properties\", \"up42-system:asset_id\"], e),\n", " complex_search_results[\"features\"]))\n", "complex_search_asset_ids" ] }, { "cell_type": "markdown", "id": "135a8fd6-63f1-4e48-b346-0f33080ed5e4", "metadata": {}, "source": [ "Let us create a convenience function for downloading the assets." ] }, { "cell_type": "code", "execution_count": 45, "id": "16f075da-1aa5-4acc-9d80-d4ed52b17f7d", "metadata": {}, "outputs": [], "source": [ "def download_asset(asset_id: str, access_token: str) -> None:\n", " \"\"\"Downloads an asset with the given ID.\"\"\"\n", " \n", " with open(f\"asset_{asset_id}.zip\", 'wb') as output:\n", " headers = {\"Authorization\": f\"Bearer {access_token}\"}\n", " response = requests.get(f\"https://api.up42.com/v2/assets/{asset_id}\", headers=headers, stream=True)\n", " for data in response.iter_content(chunk_size=8192):\n", " output.write(data)\n" ] }, { "cell_type": "markdown", "id": "c873c949-cac5-4bdb-893b-5c37b0d29742", "metadata": {}, "source": [ "We can iterate on the list of asset IDs." ] }, { "cell_type": "code", "execution_count": 46, "id": "3a3db7da-a5ba-49dd-bd1b-d6648bfab573", "metadata": {}, "outputs": [], "source": [ "gen_assets = (download_asset(id) for id in complex_search_asset_ids)" ] }, { "cell_type": "markdown", "id": "790676bd-bacd-49a1-8d3a-46e984e968d4", "metadata": {}, "source": [ "Uncomment the line below to download all assets listed above." ] }, { "cell_type": "code", "execution_count": 47, "id": "3d621c1f-0079-4809-8bd3-8189953beeff", "metadata": {}, "outputs": [], "source": [ "# list(gen_assets)" ] }, { "cell_type": "markdown", "id": "1dc5d157-aac0-4fe1-8a70-a9da234bcb9f", "metadata": {}, "source": [ "## Conclusions\n", "\n", "STAC offers a very efficient and natural way to search catalogs of\n", "geospatial assets. The current implementation on top of the UP42 storage\n", "provides a large degree of flexibility, making it easy to access assets\n", "using a multitude of search options. All assets are provided as\n", "hypermedia, making it very simple to manipulate.\n", "\n", "Go ahead and try the UP42 Spatial Asset Service API." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "07284fa453644f70a68b1f995e3fab9b": { "model_module": "jupyter-leaflet", "model_module_version": "^0.17", "model_name": "LeafletAwesomeIconModel", "state": { "_model_module": "jupyter-leaflet", "_model_module_version": "^0.17", "_model_name": "LeafletAwesomeIconModel", "_view_count": "", "_view_module": "jupyter-leaflet", "_view_module_version": "^0.17", "_view_name": "LeafletAwesomeIconView", "base": false, "bottom": false, 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