67 maxar open data

Visualizing Maxar Open Data with Leafmap

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

The Maxar Open Data STAC catalog URL is: https://maxar-opendata.s3.amazonaws.com/events/catalog.json

In [1]:

# !pip install -U leafmap

# !pip install -U leafmap

In [2]:

import leafmap

import leafmap

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

In [3]:

leafmap.maxar_collections()

leafmap.maxar_collections()

Out[3]:

['Gambia-flooding-8-11-2022',
 'Hurricane-Fiona-9-19-2022',
 'Hurricane-Ian-9-26-2022',
 'Indonesia-Earthquake22',
 'Kahramanmaras-turkey-earthquake-23',
 'New-Zealand-Flooding22',
 'New-Zealand-Flooding23',
 'Sudan-flooding-8-22-2022',
 'afghanistan-earthquake22',
 'cyclone-emnati22',
 'kentucky-flooding-7-29-2022',
 'pakistan-flooding22',
 'southafrica-flooding22',
 'tonga-volcano21',
 'volcano-indonesia21',
 'yellowstone-flooding22']

Retrieve all collections for a specific event:

In [4]:

collections = leafmap.maxar_child_collections('Kahramanmaras-turkey-earthquake-23')
print(f"The number of collections: {len(collections)}")

collections = leafmap.maxar_child_collections('Kahramanmaras-turkey-earthquake-23') print(f"The number of collections: {len(collections)}")

The number of collections: 54

In [5]:

collections[:5]

collections[:5]

Out[5]:

['1050050044DE7E00',
 '1050050044DE7F00',
 '1040010082B85E00',
 '1040010082698700',
 '10300500D8F90C00']

Retrieve all items (tiles) for a specific collection and generate the footprints:

In [6]:

gdf = leafmap.maxar_items(
    collection_id='Kahramanmaras-turkey-earthquake-23',
    child_id='1050050044DE7E00',
    return_gdf=True,
    assets=['visual'],
)
gdf.head()

gdf = leafmap.maxar_items( collection_id='Kahramanmaras-turkey-earthquake-23', child_id='1050050044DE7E00', return_gdf=True, assets=['visual'], ) gdf.head()

Out[6]:

	geometry	datetime	platform	gsd	ard_metadata_version	catalog_id	utm_zone	quadkey	view:off_nadir	view:azimuth	...	view:sun_azimuth	view:sun_elevation	proj:epsg	proj:geometry	grid:code	proj:bbox	tile:data_area	tile:clouds_area	tile:clouds_percent	visual
0	POLYGON ((36.57098 37.22216, 36.57318 37.22217...	2023-02-07T08:39:03Z	GE01	0.46	0.0.1	1050050044DE7E00	37	031133010322	20.8	312.2	...	159.7	35.2	32637	{'type': 'Polygon', 'coordinates': [[[284487.9...	MXRA-Z37-031133010322	284419.5556640625,4119843.75,285156.25,4122282...	1.7	0.4	26	https://maxar-opendata.s3.amazonaws.com/events...
1	POLYGON ((36.57499 37.22208, 36.57569 37.20028...	2023-02-07T08:39:03Z	GE01	0.46	0.0.1	1050050044DE7E00	37	031133010323	20.7	311.7	...	159.7	35.2	32637	{'type': 'Polygon', 'coordinates': [[[284843.7...	MXRA-Z37-031133010323	284843.75,4119843.75,290156.25,4122264.0991210938	11.4	2.1	18	https://maxar-opendata.s3.amazonaws.com/events...
2	POLYGON ((36.63144 37.21884, 36.63198 37.20142...	2023-02-07T08:39:03Z	GE01	0.47	0.0.1	1050050044DE7E00	37	031133010332	21.0	311.1	...	159.7	35.2	32637	{'type': 'Polygon', 'coordinates': [[[289843.7...	MXRA-Z37-031133010332	289843.75,4119843.75,295156.25,4121777.34375	8.8	0.7	8	https://maxar-opendata.s3.amazonaws.com/events...
3	POLYGON ((36.68789 37.21518, 36.68828 37.20253...	2023-02-07T08:39:03Z	GE01	0.47	0.0.1	1050050044DE7E00	37	031133010333	21.3	310.5	...	159.7	35.2	32637	{'type': 'Polygon', 'coordinates': [[[294843.7...	MXRA-Z37-031133010333	294843.75,4119843.75,300156.25,4121247.55859375	5.3	0.0	0	https://maxar-opendata.s3.amazonaws.com/events...
4	POLYGON ((36.74440 37.20960, 36.74458 37.20362...	2023-02-07T08:39:03Z	GE01	0.47	0.0.1	1050050044DE7E00	37	031133011222	21.5	310.4	...	159.7	35.2	32637	{'type': 'Polygon', 'coordinates': [[[299843.7...	MXRA-Z37-031133011222	299843.75,4119843.75,302031.55517578125,412050...	1.1	0.2	25	https://maxar-opendata.s3.amazonaws.com/events...

5 rows × 21 columns

Add the footprints to the map:

In [7]:

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

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

Out[7]:

Retrieve the COG URLs for all tiles in a collection:

In [8]:

images = gdf['visual'].tolist()
images[:5]

images = gdf['visual'].tolist() images[:5]

Out[8]:

['https://maxar-opendata.s3.amazonaws.com/events/Kahramanmaras-turkey-earthquake-23/ard/37/031133010322/2023-02-07/1050050044DE7E00-visual.tif',
 'https://maxar-opendata.s3.amazonaws.com/events/Kahramanmaras-turkey-earthquake-23/ard/37/031133010323/2023-02-07/1050050044DE7E00-visual.tif',
 'https://maxar-opendata.s3.amazonaws.com/events/Kahramanmaras-turkey-earthquake-23/ard/37/031133010332/2023-02-07/1050050044DE7E00-visual.tif',
 'https://maxar-opendata.s3.amazonaws.com/events/Kahramanmaras-turkey-earthquake-23/ard/37/031133010333/2023-02-07/1050050044DE7E00-visual.tif',
 'https://maxar-opendata.s3.amazonaws.com/events/Kahramanmaras-turkey-earthquake-23/ard/37/031133011222/2023-02-07/1050050044DE7E00-visual.tif']

Create a mosaic json file for the collection. You need to install cogeo-mosaic first using pip install cogeo-mosaic. Creating a mosaic json file might take a few minutes. Please be patient.

In [9]:

# leafmap.create_mosaicjson(images, output='1050050044DE7E00.json')

# leafmap.create_mosaicjson(images, output='1050050044DE7E00.json')

Make the mosaic json file available on the web, then you can add the mosaic to the map:

In [10]:

m = leafmap.Map()
url = 'https://geospatial.glitch.me/1050050044DE7E00.json'
m.add_stac_layer(url, name="Mosaic")
m.add_gdf(gdf, layer_name="Footprints")
m

m = leafmap.Map() url = 'https://geospatial.glitch.me/1050050044DE7E00.json' m.add_stac_layer(url, name="Mosaic") m.add_gdf(gdf, layer_name="Footprints") m

Out[10]:

Retrieve the footprint of all tiles for a specific event. This might take 15+ minutes. Please be patient.

In [11]:

# gdf = leafmap.maxar_all_items(
#     collection_id='Kahramanmaras-turkey-earthquake-23',
#     return_gdf=True,
#     verbose=True
#     )
# gdf.to_file('maxar_footprints.geojson', driver='GeoJSON')
# gdf

# gdf = leafmap.maxar_all_items( # collection_id='Kahramanmaras-turkey-earthquake-23', # return_gdf=True, # verbose=True # ) # gdf.to_file('maxar_footprints.geojson', driver='GeoJSON') # gdf

Add the footprints to the map:

In [12]:

m = leafmap.Map(center=[36.844461, 37.386475], zoom=8)
# m.add_gdf(gdf, layer_name="Footprints")
url = 'https://cdn.glitch.global/cc5b7737-d8d0-4b07-bf2f-867b9009e986/maxar_footprints.geojson?v=1676583955698'
m.add_geojson(url, layer_name="Footprints")
m

m = leafmap.Map(center=[36.844461, 37.386475], zoom=8) # m.add_gdf(gdf, layer_name="Footprints") url = 'https://cdn.glitch.global/cc5b7737-d8d0-4b07-bf2f-867b9009e986/maxar_footprints.geojson?v=1676583955698' m.add_geojson(url, layer_name="Footprints") m

Out[12]: