quickstarts/leafmap-example.ipynb

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    "## Using leafmap for interactive mapping \n",
    "\n",
    "[Leafmap](https://leafmap.org/) is a Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment.\n",
    "\n",
    "Leafmap includes great support for visualizing data using STAC metadata. For example, you can provide the name of a collection, item, and asset it visualize."
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       "Map(center=[20, 0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text…"
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   "source": [
    "import leafmap\n",
    "\n",
    "m = leafmap.Map()\n",
    "m.add_stac_layer(\n",
    "    collection=\"landsat-8-c2-l2\", item=\"LC08_L2SP_047027_20201204_02_T1\", assets=\"SR_B2\"\n",
    ")\n",
    "m"
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    "You can use [`pystac_client`](https://pystac-client.readthedocs.io/en/latest/) or the Planteary Computer [Explorer](https://planetarycomputer.microsoft.com/docs/overview/explorer/) to find the STAC collections and item IDs.\n",
    "\n",
    "Internally, that `add_stac_layer` call is using the Planteary Computer's [STAC API](https://planetarycomputer.microsoft.com/api/stac/v1) ([documentation](https://planetarycomputer.microsoft.com/docs/reference/stac/)) to get the spatiotemporal metadata and the Planetary Computer's [data API](https://planetarycomputer.microsoft.com/api/data/v1/) ([documentation](https://planetarycomputer.microsoft.com/docs/reference/data/)) to get the actual images to display on the map. The data API uses [TiTiler](https://developmentseed.org/titiler/) to dynamically render the images, which means you can provide TiTiler expressions to dynamically render the values you want.\n",
    "\n",
    "For example, we can render NDVI:"
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       "Map(center=[20, 0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text…"
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     "output_type": "display_data"
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   "source": [
    "import leafmap\n",
    "\n",
    "m = leafmap.Map()\n",
    "\n",
    "collection = \"landsat-8-c2-l2\"\n",
    "item = \"LC08_L2SP_047027_20201204_02_T1\"\n",
    "\n",
    "m.add_stac_layer(\n",
    "    collection=collection,\n",
    "    item=item,\n",
    "    assets=[\"SR_B5, SR_B4\"],\n",
    "    expression=\"(SR_B5 - SR_B4) / (SR_B5 + SR_B4)\",\n",
    "    rescale=\"-.5,0.5\",\n",
    "    colormap_name=\"rdylgn\",\n",
    "    name=\"NDVI\",\n",
    ")\n",
    "m"
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   "source": [
    "Or plot a natural-color image from Sentinel-2:"
   ]
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       "Map(center=[20, 0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text…"
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   "source": [
    "import leafmap\n",
    "\n",
    "m = leafmap.Map()\n",
    "\n",
    "collection = \"\"\n",
    "item = \"LC08_L2SP_047027_20201204_02_T1\"\n",
    "\n",
    "m.add_stac_layer(\n",
    "    collection=\"sentinel-2-l2a\",\n",
    "    item=\"S2A_MSIL2A_20211216T015621_R117_T51KXB_20211216T153808\",\n",
    "    assets=[\"B04\", \"B03\", \"B02\"],\n",
    "    color_formula=\"Gamma RGB 3.7 Saturation 1.5 Sigmoidal RGB 15 0.35\",\n",
    "    nodata=0,\n",
    "    rescale=\"0,12000\",\n",
    ")\n",
    "m"
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   "source": [
    "### Next steps\n",
    "\n",
    "Leafmap includes many more features for working with geospatial data, including the ability to add data from [GeoJSON](https://leafmap.org/notebooks/00_key_features/#add-geojson) or [GeoDataFrame](https://leafmap.org/notebooks/00_key_features/#add-geodataframe) sources, [creating heat maps](https://leafmap.org/notebooks/00_key_features/#create-heat-map), and more. Visit the [leafmap](https://leafmap.org/) documentation to learn more."
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