{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "[![image](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://demo.leafmap.org/lab/index.html?path=notebooks/69_turkey_earthquake.ipynb)\n",
    "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/leafmap/blob/master/docs/notebooks/69_turkey_earthquake.ipynb)\n",
    "[![image](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/opengeos/leafmap/HEAD)\n",
    "\n",
    "**Visualizing Maxar Open Data for the 2023 Turkey-Syria Earthquake**\n",
    "\n",
    "The [Maxar Open Data Program](https://www.maxar.com/open-data) provides pre- and post-event high-resolution satellite imagery in support of emergency planning, risk assessment, monitoring of staging areas and emergency response, damage assessment, and recovery. Check out the links below for more information.\n",
    "- [Maxar Open Data Program](https://www.maxar.com/open-data)\n",
    "- [Maxar Open Data on AWS](https://registry.opendata.aws/maxar-open-data/)\n",
    "- [Maxar Open Data on STAC Index](https://stacindex.org/catalogs/maxar-open-data-catalog-ard-format#/)\n",
    "- [Maxar Open Data on STAC Browser](https://radiantearth.github.io/stac-browser/#/external/maxar-opendata.s3.amazonaws.com/events/catalog.json?.language=en)\n",
    "- [Maxar Open Data in CSV, GeoJSON, and MosaicJSON formats](https://github.com/giswqs/maxar-open-data)\n",
    "\n",
    "This notebook shows how to visualize and download the Maxar Open Data for the 2023 Turkey Earthquake using leafmap.\n",
    "\n",
    "First, install libraries and import modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install -U leafmap geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import leafmap\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "Retrieve all collections from the Maxar Open Data STAC catalog. Each collection represents a single event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "leafmap.maxar_collections()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "The collection ID for the 2023 Turkey Earthquake is `Kahramanmaras-turkey-earthquake-23`. We can get the footprints ([geojson](https://github.com/giswqs/maxar-open-data/blob/master/datasets/Kahramanmaras-turkey-earthquake-23.geojson), [tsv](https://github.com/giswqs/maxar-open-data/blob/master/datasets/Kahramanmaras-turkey-earthquake-23.tsv)) of the event from the [Maxar Open Data GitHub repo](https://github.com/giswqs/maxar-open-data):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "collection = \"Kahramanmaras-turkey-earthquake-23\"\n",
    "url = leafmap.maxar_collection_url(collection, dtype=\"geojson\")\n",
    "url"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "Let's find out how many images are available for the event:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.read_file(url)\n",
    "print(f\"Total number of images: {len(gdf)}\")\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "Visualize the footprints of the images on the map:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = leafmap.Map()\n",
    "m.add_gdf(gdf, layer_name=\"Footprints\")\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "The earthquake started on February 6, 2023. We can use the `start_date` and `end_date` parameters to filter the images by date. Set `end_date` to `2023-02-06` to get all images captured before the earthquake:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_gdf = leafmap.maxar_search(collection, end_date=\"2023-02-06\")\n",
    "print(f\"Total number of pre-event images: {len(pre_gdf)}\")\n",
    "pre_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13",
   "metadata": {},
   "source": [
    "Set the `start_date` to `2023-02-06` to get all images captured after the earthquake:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14",
   "metadata": {},
   "outputs": [],
   "source": [
    "post_gdf = leafmap.maxar_search(collection, start_date=\"2023-02-06\")\n",
    "print(f\"Total number of post-event images: {len(post_gdf)}\")\n",
    "post_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15",
   "metadata": {},
   "source": [
    "Visualize the pre-event and post-event image footprints on the map. The red footprints represent the pre-event images and the blue footprints represent the post-event images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = leafmap.Map()\n",
    "pre_style = {\"color\": \"red\", \"fillColor\": \"red\", \"opacity\": 1, \"fillOpacity\": 0.5}\n",
    "m.add_gdf(pre_gdf, layer_name=\"Pre-event\", style=pre_style, info_mode=\"on_click\")\n",
    "m.add_gdf(post_gdf, layer_name=\"Post-event\", info_mode=\"on_click\")\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "Use the draw tools to select a region of interest (ROI) and get the bounding box coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18",
   "metadata": {},
   "outputs": [],
   "source": [
    "bbox = m.user_roi_bounds()\n",
    "if bbox is None:\n",
    "    bbox = [36.8715, 37.5497, 36.9814, 37.6019]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19",
   "metadata": {},
   "source": [
    "Search the Maxar Open Data catalog for images within the ROI before the earthquake:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_event = leafmap.maxar_search(collection, bbox=bbox, end_date=\"2023-02-06\")\n",
    "pre_event.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21",
   "metadata": {},
   "source": [
    "Search the Maxar Open Data catalog for images within the ROI after the earthquake:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22",
   "metadata": {},
   "outputs": [],
   "source": [
    "post_event = leafmap.maxar_search(collection, bbox=bbox, start_date=\"2023-02-06\")\n",
    "post_event.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23",
   "metadata": {},
   "source": [
    "Get the catalog id of the pre-event tile, which contain several images. Each of the image has a unique `quadkey`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_tile = pre_event[\"catalog_id\"].values[0]\n",
    "pre_tile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25",
   "metadata": {},
   "source": [
    "Get the catalog id of the post-event tile, which contain several images. Each of the image has a unique `quadkey`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26",
   "metadata": {},
   "outputs": [],
   "source": [
    "post_tile = post_event[\"catalog_id\"].values[0]\n",
    "post_tile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27",
   "metadata": {},
   "source": [
    "Get the MosaicJSON for the pre-event tile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_stac = leafmap.maxar_tile_url(collection, pre_tile, dtype=\"json\")\n",
    "pre_stac"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29",
   "metadata": {},
   "source": [
    "Get the MosaicJSON for the post-event tile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30",
   "metadata": {},
   "outputs": [],
   "source": [
    "post_stac = leafmap.maxar_tile_url(collection, post_tile, dtype=\"json\")\n",
    "post_stac"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31",
   "metadata": {},
   "source": [
    "Create a split map to compare the pre-event and post-event images. Note the the ipyleaflet split map has some bugs ([source](https://github.com/jupyter-widgets/ipyleaflet/issues/1066)), so we will use the folium plotting backend instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import leafmap.foliumap as leafmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = leafmap.Map()\n",
    "m.split_map(\n",
    "    left_layer=pre_stac,\n",
    "    right_layer=post_stac,\n",
    "    left_label=\"Pre-event\",\n",
    "    right_label=\"Post-event\",\n",
    ")\n",
    "m.set_center(36.9265, 37.5762, 16)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34",
   "metadata": {},
   "source": [
    "Get download links for the pre-event and post-event images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_images = pre_event[\"visual\"].tolist()\n",
    "post_images = post_event[\"visual\"].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36",
   "metadata": {},
   "source": [
    "Download the pre-event and post-event images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37",
   "metadata": {},
   "outputs": [],
   "source": [
    "leafmap.maxar_download(pre_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# leafmap.maxar_download(post_images)"
   ]
  }
 ],
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