Searching and downloading CSDA data¶

NASA's Commercial Satellite Data Acquisition (CSDA) program acquires, catalogs, and provides commercial satellite data for the scientific community. The program's services include a SpatioTemporal Asset Catalog API server for searching and discovering CSDA assets. We'll use pystac-client to search the CSDA STAC API for items that we can use. The CSDA STAC API is public and open, meaning that we don't need any authorization or authentication to use it.

Before getting started, we import all the Python packages, classes, and functions that we need, and define a few helpful constants.

In [ ]:

import getpass
from pathlib import Path
from tempfile import TemporaryDirectory

import contextily
import cql2
import humanize
import rasterio
import rasterio.plot
import tabulate
from geopandas import GeoDataFrame
from httpx import BasicAuth
from pystac_client import Client

from csda_client import CsdaClient

LONGMONT_CO_USA = {"type": "Point", "coordinates": [-105.1019, 40.1672]}

import getpass from pathlib import Path from tempfile import TemporaryDirectory import contextily import cql2 import humanize import rasterio import rasterio.plot import tabulate from geopandas import GeoDataFrame from httpx import BasicAuth from pystac_client import Client from csda_client import CsdaClient LONGMONT_CO_USA = {"type": "Point", "coordinates": [-105.1019, 40.1672]}

Collection search¶

CSDA includes data from several vendors. Each vendor maps onto a single STAC Collection, and we can use our STAC API to discover which vendors are available.

Why are there warnings?¶

The CSDA STAC catalog currently contains some invalid STAC Collections. We're working on updating those, and when we do, the warnings will go away.

In [7]:

stac_client = Client.open("https://csdap.earthdata.nasa.gov/stac/")
rows = []
for collection in stac_client.collection_search().collections_as_dicts():
    rows.append([collection["id"], collection.get("title")])

tabulate.tabulate(rows, headers=["collection", "title"], tablefmt="html")

stac_client = Client.open("https://csdap.earthdata.nasa.gov/stac/") rows = [] for collection in stac_client.collection_search().collections_as_dicts(): rows.append([collection["id"], collection.get("title")]) tabulate.tabulate(rows, headers=["collection", "title"], tablefmt="html")

/Users/gadomski/Code/nasa-impact/csda-client/.venv/lib/python3.10/site-packages/pystac_client/collection_search.py:121: PystacClientWarning: Unable to parse extent from collection=ghgsat
  warnings.warn(
/Users/gadomski/Code/nasa-impact/csda-client/.venv/lib/python3.10/site-packages/pystac_client/collection_search.py:121: PystacClientWarning: Unable to parse extent from collection=geooptics
  warnings.warn(
/Users/gadomski/Code/nasa-impact/csda-client/.venv/lib/python3.10/site-packages/pystac_client/collection_search.py:121: PystacClientWarning: Unable to parse extent from collection=capellaspace
  warnings.warn(
/Users/gadomski/Code/nasa-impact/csda-client/.venv/lib/python3.10/site-packages/pystac_client/collection_search.py:121: PystacClientWarning: Unable to parse extent from collection=planet
  warnings.warn(
/Users/gadomski/Code/nasa-impact/csda-client/.venv/lib/python3.10/site-packages/pystac_client/collection_search.py:121: PystacClientWarning: Unable to parse extent from collection=spire
  warnings.warn(
/Users/gadomski/Code/nasa-impact/csda-client/.venv/lib/python3.10/site-packages/pystac_client/collection_search.py:121: PystacClientWarning: Unable to parse extent from collection=airbus
  warnings.warn(
/Users/gadomski/Code/nasa-impact/csda-client/.venv/lib/python3.10/site-packages/pystac_client/collection_search.py:121: PystacClientWarning: Unable to parse extent from collection=earthdem
  warnings.warn(
/Users/gadomski/Code/nasa-impact/csda-client/.venv/lib/python3.10/site-packages/pystac_client/collection_search.py:121: PystacClientWarning: Unable to parse extent from collection=blacksky
  warnings.warn(

Out[7]:

collection	title
ghgsat
geooptics	GeoOptics
capellaspace	CapellaSpace
desis	DESIS
planet	Planet
spire	Spire
umbra	Umbra
iceye	Iceye
airbus	Airbus
earthdem	EarthDEM
blacksky	Blacksky
pgc-earthdem	EarthDEM

Finding Planet data¶

One of the CSDA vendors, Planet, collects imagery that is easy to visualize and interpret, so let's use their data for this notebook. We can use a STAC API search query to narrow our request. In this example, we'll find STAC items that:

• are in the planet collection
• Have a cloud cover of less than or equal to 25%
• Were collected in 2021
• Intersect Longmont, CO, USA

STAC search is extremely powerful — you can search by other attributes, control sorting, and use datetime intervals. Learn more in the pystac-client docs.

What's the deal with the cql2 stuff?¶

cql2 is an Open Geospatial Consortium standard for querying spatial data. Our STAC API only understands cql2-json, but cql2-json can be a little awkward to write. cql2-text is much more intuitive, so we write that and then convert it to cql2-json using this helper package. This should become automatic via pystac-client soon.

In [8]:

item_search = stac_client.search(
    collections=["planet"],
    intersects=LONGMONT_CO_USA,
    filter=cql2.parse_text("eo:cloud_cover<25").to_json(),
    datetime="2021",
)
item_collection = item_search.item_collection()
print(len(item_collection))

item_search = stac_client.search( collections=["planet"], intersects=LONGMONT_CO_USA, filter=cql2.parse_text("eo:cloud_cover<25").to_json(), datetime="2021", ) item_collection = item_search.item_collection() print(len(item_collection))

18

Let's visualize the items, first as a GeoDataFrame.

In [9]:

data_frame = GeoDataFrame.from_features(item_collection.to_dict(), crs="EPSG:4326")
data_frame.head(5)

data_frame = GeoDataFrame.from_features(item_collection.to_dict(), crs="EPSG:4326") data_frame.head(5)

Out[9]:

	geometry	eo:gsd	eo:epsg	pl:rows	datetime	eo:bands	eo:azimuth	pl:columns	pl:updated	pl:origin_x	...	pl:anomalous_pixels	pl:pixel_resolution	pl:publishing_stage	pl:quality_category	pl:snow_ice_percent	pl:heavy_haze_percent	pl:light_haze_percent	pl:clear_confidence_percent	pl:visible_confidence_percent	pl:ps4b_geometry
0	POLYGON ((-105.34272 40.22107, -105.3952 40.03...	4.0	32613.0	9524.0	2021-12-30T16:54:34.641952Z	[]	101.0	13191.0	2021-12-31T03:59:48Z	466281.0	...	0	3	finalized	test	0	0	0	97	72	NaN
1	POLYGON ((-105.46945 40.24533, -105.5246 40.05...	4.0	32613.0	9595.0	2021-12-20T17:00:46.336281Z	[]	278.6	13244.0	2021-12-21T04:28:04Z	455256.0	...	0	3	finalized	standard	0	0	0	96	74	NaN
2	POLYGON ((-105.49219 40.32051, -105.54659 40.1...	4.1	32613.0	9715.0	2021-12-01T17:44:27.408844Z	[]	111.8	13382.0	2021-12-02T10:03:46Z	453429.0	...	0	3	finalized	standard	0	0	0	97	59	NaN
3	POLYGON ((-105.22817 40.32841, -105.28215 40.1...	4.1	NaN	NaN	2021-11-28T16:54:46.363155Z	[]	100.9	NaN	2022-10-27T11:45:48Z	NaN	...	0	3	finalized	standard	0	0	0	98	77	{'type': 'Polygon', 'coordinates': [[[-105.228...
4	POLYGON ((-105.18108 40.18135, -105.23515 39.9...	4.0	NaN	NaN	2021-11-25T17:11:14.321045Z	[]	276.5	NaN	2022-10-30T06:51:29Z	NaN	...	0	3	finalized	standard	0	0	0	99	99	NaN

5 rows × 36 columns

Then, on a map (it's pretty noisy).

In [26]:

axes = data_frame.to_crs(epsg=3857).boundary.plot()
contextily.add_basemap(axes, source=contextily.providers.CartoDB.Voyager)
axes.set_axis_off()

axes = data_frame.to_crs(epsg=3857).boundary.plot() contextily.add_basemap(axes, source=contextily.providers.CartoDB.Voyager) axes.set_axis_off()

If you happen to know your item (granule) id, you can fetch that item directly and see it's assets dictionary.

In [27]:

item = next(
    stac_client.search(
        collections=["planet"], ids=["PSScene-20250706_160525_67_24c6"]
    ).items()
)
item

item = next( stac_client.search( collections=["planet"], ids=["PSScene-20250706_160525_67_24c6"] ).items() ) item

Out[27]:

• type "Feature"
• stac_version "1.1.0"
• stac_extensions[] 1 items

  • 0 "https://stac-extensions.github.io/file/v2.1.0/schema.json"
• id "PSScene-20250706_160525_67_24c6"
• geometry

  • type "Polygon"
  • coordinates[] 1 items

    • 0[] 5 items

      • 0[] 2 items

        • 0 -71.51031041215329

        • 1 44.08847606785535

      • 1[] 2 items

        • 0 -71.56277741631092

        • 1 43.92181467531235

      • 2[] 2 items

        • 0 -71.17689639541125

        • 1 43.85813988165753

      • 3[] 2 items

        • 0 -71.12367813028888

        • 1 44.02473950851437

      • 4[] 2 items

        • 0 -71.51031041215329

        • 1 44.08847606785535
• bbox[] 4 items

  • 0 -71.56277741631092

  • 1 43.85813988165753

  • 2 -71.12367813028888

  • 3 44.08847606785535
• properties

  • eo:gsd 3.6
  • datetime "2025-07-06T16:05:25.677171Z"
  • eo:bands[] 0 items
  • eo:azimuth 111.2
  • pl:updated "2025-07-07T04:02:58Z"
  • pl:provider "planetscope"
  • pl:strip_id "8175328"
  • eo:off_nadir 1
  • pl:item_type "PSScene"
  • pl:published "2025-07-06T23:43:35Z"
  • eo:instrument "PSB.SD"
  • eo:cloud_cover 12
  • eo:sun_azimuth 153.2
  • pl:satellite_id "24c6"
  • eo:sun_elevation 66.8
  • pl:clear_percent 50
  • pl:cloud_percent 12
  • pl:ground_control True
  • pl:shadow_percent 3
  • pl:visible_percent 88
  • pl:anomalous_pixels 0
  • pl:pixel_resolution 3
  • pl:publishing_stage "standard"
  • pl:quality_category "test"
  • pl:snow_ice_percent 0
  • pl:heavy_haze_percent 0
  • pl:light_haze_percent 35
  • pl:clear_confidence_percent 84
  • pl:visible_confidence_percent 77
• links[] 4 items

  • 0

    • rel "collection"
    • href "https://csdap.earthdata.nasa.gov/stac/collections/planet"
    • type "application/json"

  • 1

    • rel "parent"
    • href "https://csdap.earthdata.nasa.gov/stac/collections/planet"
    • type "application/json"

  • 2

    • rel "root"
    • href "https://csdap.earthdata.nasa.gov/stac/"
    • type "application/json"
    • title "stac-fastapi"

  • 3

    • rel "self"
    • href "https://csdap.earthdata.nasa.gov/stac/collections/planet/items/PSScene-20250706_160525_67_24c6"
    • type "application/geo+json"
• assets

  • thumbnail

    • href "https://ss-ingest-dev-thumbnails115905a6-1qhb7abvyj8pc.s3.amazonaws.com/planet/PSScene-20250706_160525_67_24c6/thumb"
    • title "Thumbnail"
    • status "active"
    • roles[] 1 items

      • 0 "thumbnail"
  • basic_udm2

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/basic_udm2/20250706_160525_67_24c6_1A_udm2.tif"
    • type "image/tiff"
    • title "basic_udm2"
    • size 5765066
    • md5_digest "d75527248c9e7c830c8a931d9892a3b4"
    • sha256_digest "ec574ffeea4f98f977f7153908c05e6515bd569417b1a36ea165b68583d0ab10"
    • file:size 5765066
    • roles[] 1 items

      • 0 "data"
  • ortho_udm2

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/ortho_udm2/20250706_160525_67_24c6_3B_udm2.tif"
    • type "image/tiff"
    • title "ortho_udm2"
    • size 9302700
    • md5_digest "4bfda8206b0939fffbdcd634abdaab4d"
    • sha256_digest "fb161d5d1b857db6bd6f084eced4cc9fd0e1c048924a85fa4ec0a7126e7af952"
    • file:size 9302700
    • roles[] 1 items

      • 0 "data"
  • ortho_visual

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/ortho_visual/20250706_160525_67_24c6_3B_Visual.tif"
    • type "image/tiff"
    • title "ortho_visual"
    • size 179183410
    • md5_digest "92c15958bc53c8525586281147432c96"
    • sha256_digest "7965668a82729982d2e76b3b219fc7c6d25ad234c97ad03d1f61b9f90c6a27a1"
    • file:size 179183410
    • roles[] 1 items

      • 0 "data"
  • json_metadata

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/json_metadata/20250706_160525_67_24c6_metadata.json"
    • type "application/json"
    • title "json_metadata"
    • size 968
    • md5_digest "4a8f154efbc725b6defb28df8208487a"
    • sha256_digest "da42ee32a0e1f72f00127b5ad49508bdec36be8f4e0332883a5ace20bc022cf7"
    • file:size 968
    • roles[] 1 items

      • 0 "metadata"
  • basic_analytic_4b

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/basic_analytic_4b/20250706_160525_67_24c6_1B_AnalyticMS.tif"
    • type "image/tiff"
    • title "basic_analytic_4b"
    • size 367674388
    • md5_digest "e2125283bd0ee052bac22556bf23cccb"
    • sha256_digest "0c208a1b5cff64ccfaf9efadb1ad524250e912bd774a29e7a6dc3bfcfa5a6bd9"
    • file:size 367674388
    • roles[] 1 items

      • 0 "data"
  • basic_analytic_8b

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/basic_analytic_8b/20250706_160525_67_24c6_1B_AnalyticMS_8b.tif"
    • type "image/tiff"
    • title "basic_analytic_8b"
    • size 736624587
    • md5_digest "d9cc9ef43da344eae331fe5f9c707231"
    • sha256_digest "24213ab985da685fd6bb75a71ef800721cf21d4dea9058fbf414b5b86cb15afb"
    • file:size 736624587
    • roles[] 1 items

      • 0 "data"
  • ortho_analytic_4b

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/ortho_analytic_4b/20250706_160525_67_24c6_3B_AnalyticMS.tif"
    • type "image/tiff"
    • title "ortho_analytic_4b"
    • size 518181162
    • md5_digest "f71ff41d20ec02773e7dc7b39d1f5faf"
    • sha256_digest "cceafa53d129db6a6bf830ec4888fa49a434502b0cf92485d94e9d4104465aa6"
    • file:size 518181162
    • roles[] 1 items

      • 0 "data"
  • ortho_analytic_8b

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/ortho_analytic_8b/20250706_160525_67_24c6_3B_AnalyticMS_8b.tif"
    • type "image/tiff"
    • title "ortho_analytic_8b"
    • size 1029523389
    • md5_digest "cbe5197d05b89928660ae49ae815aae9"
    • sha256_digest "d57c75052733ce884701a1b74a05d6f9524156cc6eb649b886513d01d7996287"
    • file:size 1029523389
    • roles[] 1 items

      • 0 "data"
  • ortho_analytic_4b_sr

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/ortho_analytic_4b_sr/20250706_160525_67_24c6_3B_AnalyticMS_SR.tif"
    • type "image/tiff"
    • title "ortho_analytic_4b_sr"
    • size 437116673
    • md5_digest "bb734ff0f9f871b4df49f393b4e88d8f"
    • sha256_digest "3d5ce5ab0d6b36b316a616ca5b501666e7a61e7fb6b129158588c6fc755dd524"
    • file:size 437116673
    • roles[] 1 items

      • 0 "data"
  • ortho_analytic_8b_sr

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/ortho_analytic_8b_sr/20250706_160525_67_24c6_3B_AnalyticMS_SR_8b.tif"
    • type "image/tiff"
    • title "ortho_analytic_8b_sr"
    • size 866996013
    • md5_digest "0625a9a579ee6f0f23b440d94ab6da01"
    • sha256_digest "d8807569ffbc9b9b58d2a91e1216bf382a6224a01e658caa6c55aa78d11fab65"
    • file:size 866996013
    • roles[] 1 items

      • 0 "data"
  • basic_analytic_4b_rpc

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/basic_analytic_4b_rpc/20250706_160525_67_24c6_1B_AnalyticMS_RPC.TXT"
    • type "text/plain"
    • title "basic_analytic_4b_rpc"
    • size 3327
    • md5_digest "d1cc47badc61e297083d6f5c38e5fec9"
    • sha256_digest "de1853f78c4c258130e8c29de3449db6c3d99fb211c70bef6950288984824ea4"
    • file:size 3327
    • roles[] 1 items

      • 0 "data"
  • basic_analytic_4b_xml

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/basic_analytic_4b_xml/20250706_160525_67_24c6_1B_AnalyticMS_metadata.xml"
    • type "text/xml"
    • title "basic_analytic_4b_xml"
    • size 10273
    • md5_digest "cfbd62268f7817d15f6d309487e61132"
    • sha256_digest "66ec44fe1088a72cf91d04b0e1efe645f77c2735dc43235a141dd49ac9211ff9"
    • file:size 10273
    • roles[] 1 items

      • 0 "metadata"
  • basic_analytic_8b_xml

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/basic_analytic_8b_xml/20250706_160525_67_24c6_1B_AnalyticMS_8b_metadata.xml"
    • type "text/xml"
    • title "basic_analytic_8b_xml"
    • size 12153
    • md5_digest "a50547fc08e1f85b3961ddf412c9896d"
    • sha256_digest "c3c2a1494b3181652d562fa271293bbd14e0d6a461e67bf6e90f4ec028d939c8"
    • file:size 12153
    • roles[] 1 items

      • 0 "metadata"
  • ortho_analytic_4b_xml

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/ortho_analytic_4b_xml/20250706_160525_67_24c6_3B_AnalyticMS_metadata.xml"
    • type "text/xml"
    • title "ortho_analytic_4b_xml"
    • size 10288
    • md5_digest "0042fda1590909d48865600a16cdb418"
    • sha256_digest "5972e578f366cb01898086813110b9dc07f5a317193b3672c725a9f4cbfd97c3"
    • file:size 10288
    • roles[] 1 items

      • 0 "metadata"
  • ortho_analytic_8b_xml

    • href "s3://ss-ingest-prod-ingesteddata-uswest2/storage-ss-ingest-prod-ingesteddata-uswest2/planet/PSScene-20250706_160525_67_24c6/assets/ortho_analytic_8b_xml/20250706_160525_67_24c6_3B_AnalyticMS_8b_metadata.xml"
    • type "text/xml"
    • title "ortho_analytic_8b_xml"
    • size 12168
    • md5_digest "f6fa07ea75fea5fda2a879260f73ed28"
    • sha256_digest "479f6cd3a30100ba958944653a8ef5bcbd4abbd13ffbd9e53dc6e0c89cc6157a"
    • file:size 12168
    • roles[] 1 items

      • 0 "metadata"
• collection "planet"

Download assets¶

While the STAC API is open, the CSDA Orders API requires authentication. You'll use your Earthdata login username and password to log in.

In [28]:

username = input("Earthdata username: ")
password = getpass.getpass("Earthdata password: ")

username = input("Earthdata username: ") password = getpass.getpass("Earthdata password: ")

Logging into CSDA creates an access token that encodes your download quotas, among other information.

In [ ]:

client = CsdaClient.open(BasicAuth(username=username, password=password))
client.verify()

client = CsdaClient.open(BasicAuth(username=username, password=password)) client.verify()

Out[ ]:

'Hello gadomski, you have a valid token!'

First, let's see how much quota you have. The example output has very large quotas, because it was run for a developer on the project who has artificially high quotas.

In [ ]:

profile = client.profile(username)
rows = []
for vendor in profile.vendors:
    rows.append([vendor.vendor, vendor.quota, vendor.quota_unit])
tabulate.tabulate(rows, headers=["Vendor", "Quota", "Quota unit"], tablefmt="html")

profile = client.profile(username) rows = [] for vendor in profile.vendors: rows.append([vendor.vendor, vendor.quota, vendor.quota_unit]) tabulate.tabulate(rows, headers=["Vendor", "Quota", "Quota unit"], tablefmt="html")

Out[ ]:

Vendor	Quota	Quota unit
BlackSky	1099511627776	QuotaUnit.filesize
GHGSat	1099511627776	QuotaUnit.filesize
EarthDEM	21990232555520	QuotaUnit.filesize
Airbus U.S.	1099511627776	QuotaUnit.filesize
Spire	1099511627776	QuotaUnit.filesize
Planet	5000000	QuotaUnit.area
Umbra	1099511627776	QuotaUnit.filesize

To create a new order, let's first identify the item ids that we want to order. For cost-saving, we regularly move "older" data (data that was provided to the program a long time ago) to "cold" storage, meaning that it can't be quickly downloaded. For this demonstration, we cheat a bit and use an item that we know isn't in cold storage (for now).

In [35]:

item_search = stac_client.search(
    collections=["planet"],
    ids=["PSScene-20250513_160742_95_253a"],
)
item_collection = item_search.item_collection()
assert len(item_collection) == 1
data_frame = GeoDataFrame.from_features(item_collection.to_dict())
data_frame

item_search = stac_client.search( collections=["planet"], ids=["PSScene-20250513_160742_95_253a"], ) item_collection = item_search.item_collection() assert len(item_collection) == 1 data_frame = GeoDataFrame.from_features(item_collection.to_dict()) data_frame

Out[35]:

	geometry	eo:gsd	datetime	eo:bands	eo:azimuth	pl:updated	pl:provider	pl:strip_id	eo:off_nadir	pl:item_type	...	pl:visible_percent	pl:anomalous_pixels	pl:pixel_resolution	pl:publishing_stage	pl:quality_category	pl:snow_ice_percent	pl:heavy_haze_percent	pl:light_haze_percent	pl:clear_confidence_percent	pl:visible_confidence_percent
0	POLYGON ((-72.47785 42.70292, -72.5326 42.5210...	3.9	2025-05-13T16:07:42.957137Z	[]	270.6	2025-05-14T03:55:05Z	planetscope	8063802	1.2	PSScene	...	94	0	3	finalized	standard	0	0	79	80	71

1 rows × 30 columns

The item has several assets.

In [36]:

item = item_collection[0]
rows = []
for key, asset in item.assets.items():
    if file_size := asset.ext.file.size:
        humanized_file_size = humanize.naturalsize(file_size)
    else:
        humanized_file_size = None
    if roles := asset.roles:
        humanized_roles = humanize.natural_list(roles)
    else:
        humanized_roles = roles
    rows.append([key, humanized_roles, asset.media_type, humanized_file_size])
tabulate.tabulate(
    rows, headers=["Key", "Roles", "Type", "File size", "Roles"], tablefmt="html"
)

item = item_collection[0] rows = [] for key, asset in item.assets.items(): if file_size := asset.ext.file.size: humanized_file_size = humanize.naturalsize(file_size) else: humanized_file_size = None if roles := asset.roles: humanized_roles = humanize.natural_list(roles) else: humanized_roles = roles rows.append([key, humanized_roles, asset.media_type, humanized_file_size]) tabulate.tabulate( rows, headers=["Key", "Roles", "Type", "File size", "Roles"], tablefmt="html" )

Out[36]:

Key	Roles	Type	File size
thumbnail	thumbnail
basic_udm2	data	image/tiff	4.6 MB
ortho_udm2	data	image/tiff	8.2 MB
ortho_visual	data	image/tiff	200.3 MB
json_metadata	metadata	application/json	975 Bytes
basic_analytic_4b	data	image/tiff	352.6 MB
basic_analytic_8b	data	image/tiff	714.4 MB
ortho_analytic_4b	data	image/tiff	586.8 MB
ortho_analytic_8b	data	image/tiff	1.2 GB
ortho_analytic_4b_sr	data	image/tiff	494.2 MB
ortho_analytic_8b_sr	data	image/tiff	989.4 MB
basic_analytic_4b_rpc	data	text/plain	3.3 kB
basic_analytic_4b_xml	metadata	text/xml	10.3 kB
basic_analytic_8b_xml	metadata	text/xml	12.2 kB
ortho_analytic_4b_xml	metadata	text/xml	10.3 kB
ortho_analytic_8b_xml	metadata	text/xml	12.2 kB

Let's download the ortho visual asset.

In [37]:

with TemporaryDirectory() as temporary_directory:
    local_path = Path(temporary_directory) / "ortho_visual.tif"
    client.download_item(item, "ortho_visual", local_path)
    with rasterio.open(local_path) as dataset:
        rasterio.plot.show(dataset)

with TemporaryDirectory() as temporary_directory: local_path = Path(temporary_directory) / "ortho_visual.tif" client.download_item(item, "ortho_visual", local_path) with rasterio.open(local_path) as dataset: rasterio.plot.show(dataset)