69 turkey earthquake

Visualizing Maxar Open Data for the 2023 Turkey-Syria Earthquake

The Maxar Open Data Program 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.

• Maxar Open Data Program
• Maxar Open Data on AWS
• Maxar Open Data on STAC Index
• Maxar Open Data on STAC Browser
• Maxar Open Data in CSV, GeoJSON, and MosaicJSON formats

This notebook shows how to visualize and download the Maxar Open Data for the 2023 Turkey Earthquake using leafmap.

First, install libraries and import modules.

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# !pip install -U leafmap geopandas

# !pip install -U leafmap geopandas

In [ ]:

import leafmap
import geopandas as gpd

import leafmap import geopandas as gpd

Retrieve all collections from the Maxar Open Data STAC catalog. Each collection represents a single event.

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leafmap.maxar_collections()

leafmap.maxar_collections()

The collection ID for the 2023 Turkey Earthquake is Kahramanmaras-turkey-earthquake-23. We can get the footprints (geojson, tsv) of the event from the Maxar Open Data GitHub repo:

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collection = "Kahramanmaras-turkey-earthquake-23"
url = leafmap.maxar_collection_url(collection, dtype="geojson")
url

collection = "Kahramanmaras-turkey-earthquake-23" url = leafmap.maxar_collection_url(collection, dtype="geojson") url

Let's find out how many images are available for the event:

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gdf = gpd.read_file(url)
print(f"Total number of images: {len(gdf)}")
gdf.head()

gdf = gpd.read_file(url) print(f"Total number of images: {len(gdf)}") gdf.head()

Visualize the footprints of the images on the map:

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m = leafmap.Map()
m.add_gdf(gdf, layer_name="Footprints")
m

m = leafmap.Map() m.add_gdf(gdf, layer_name="Footprints") m

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:

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pre_gdf = leafmap.maxar_search(collection, end_date="2023-02-06")
print(f"Total number of pre-event images: {len(pre_gdf)}")
pre_gdf.head()

pre_gdf = leafmap.maxar_search(collection, end_date="2023-02-06") print(f"Total number of pre-event images: {len(pre_gdf)}") pre_gdf.head()

Set the start_date to 2023-02-06 to get all images captured after the earthquake:

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post_gdf = leafmap.maxar_search(collection, start_date="2023-02-06")
print(f"Total number of post-event images: {len(post_gdf)}")
post_gdf.head()

post_gdf = leafmap.maxar_search(collection, start_date="2023-02-06") print(f"Total number of post-event images: {len(post_gdf)}") post_gdf.head()

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:

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m = leafmap.Map()
pre_style = {"color": "red", "fillColor": "red", "opacity": 1, "fillOpacity": 0.5}
m.add_gdf(pre_gdf, layer_name="Pre-event", style=pre_style, info_mode="on_click")
m.add_gdf(post_gdf, layer_name="Post-event", info_mode="on_click")
m

m = leafmap.Map() pre_style = {"color": "red", "fillColor": "red", "opacity": 1, "fillOpacity": 0.5} m.add_gdf(pre_gdf, layer_name="Pre-event", style=pre_style, info_mode="on_click") m.add_gdf(post_gdf, layer_name="Post-event", info_mode="on_click") m

Use the draw tools to select a region of interest (ROI) and get the bounding box coordinates:

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bbox = m.user_roi_bounds()
if bbox is None:
    bbox = [36.8715, 37.5497, 36.9814, 37.6019]

bbox = m.user_roi_bounds() if bbox is None: bbox = [36.8715, 37.5497, 36.9814, 37.6019]

Search the Maxar Open Data catalog for images within the ROI before the earthquake:

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pre_event = leafmap.maxar_search(collection, bbox=bbox, end_date="2023-02-06")
pre_event.head()

pre_event = leafmap.maxar_search(collection, bbox=bbox, end_date="2023-02-06") pre_event.head()

Search the Maxar Open Data catalog for images within the ROI after the earthquake:

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post_event = leafmap.maxar_search(collection, bbox=bbox, start_date="2023-02-06")
post_event.head()

post_event = leafmap.maxar_search(collection, bbox=bbox, start_date="2023-02-06") post_event.head()

Get the catalog id of the pre-event tile, which contain several images. Each of the image has a unique quadkey:

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pre_tile = pre_event["catalog_id"].values[0]
pre_tile

pre_tile = pre_event["catalog_id"].values[0] pre_tile

Get the catalog id of the post-event tile, which contain several images. Each of the image has a unique quadkey:

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post_tile = post_event["catalog_id"].values[0]
post_tile

post_tile = post_event["catalog_id"].values[0] post_tile

Get the MosaicJSON for the pre-event tile:

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pre_stac = leafmap.maxar_tile_url(collection, pre_tile, dtype="json")
pre_stac

pre_stac = leafmap.maxar_tile_url(collection, pre_tile, dtype="json") pre_stac

Get the MosaicJSON for the post-event tile:

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post_stac = leafmap.maxar_tile_url(collection, post_tile, dtype="json")
post_stac

post_stac = leafmap.maxar_tile_url(collection, post_tile, dtype="json") post_stac

Create a split map to compare the pre-event and post-event images. Note the the ipyleaflet split map has some bugs (source), so we will use the folium plotting backend instead.

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import leafmap.foliumap as leafmap

import leafmap.foliumap as leafmap

In [ ]:

m = leafmap.Map()
m.split_map(
    left_layer=pre_stac,
    right_layer=post_stac,
    left_label="Pre-event",
    right_label="Post-event",
)
m.set_center(36.9265, 37.5762, 16)
m

m = leafmap.Map() m.split_map( left_layer=pre_stac, right_layer=post_stac, left_label="Pre-event", right_label="Post-event", ) m.set_center(36.9265, 37.5762, 16) m

Get download links for the pre-event and post-event images:

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pre_images = pre_event["visual"].tolist()
post_images = post_event["visual"].tolist()

pre_images = pre_event["visual"].tolist() post_images = post_event["visual"].tolist()

Download the pre-event and post-event images:

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leafmap.maxar_download(pre_images)

leafmap.maxar_download(pre_images)

In [ ]:

# leafmap.maxar_download(post_images)

# leafmap.maxar_download(post_images)