Pléiades Display (Download)¶

Pléiades 1A/1B are very-high resolution twin satellites offering 0.5m resolution products. The constellation is highly reactive and has been designed for daily revisit anywhere on Earth, with stereo and tri-stereo capacity.

In the example, we use catalog search to find cloud-cover free images and plot their quicklooks. Then the workflow, the area of interest and the workflow parameters are defined. After running the job, the results are downloaded and visualized. For more information, refer to the block's UP42 Marketplace page and Documentation.

In [1]:

import up42

import up42

In [7]:

# Authenticate with UP42.
up42.authenticate(project_id="your-project-ID", project_api_key="your-project-API-key")

# Authenticate with UP42. up42.authenticate(project_id="your-project-ID", project_api_key="your-project-API-key")

2022-07-02 14:55:15,951 - Authentication with UP42 successful!

Use Catalog Search to find available images¶

In [3]:

aoi = {"type": "FeatureCollection","features": [{"type": "Feature","properties": {},
      "geometry": {"type": "Polygon","coordinates": [[[13.373408317565918,52.51568549156065],
                                                      [13.381991386413574,52.51568549156065],
                                                      [13.381991386413574,52.519302392686626],
                                                      [13.373408317565918,52.519302392686626],
                                                      [13.373408317565918,52.51568549156065]]]}}]}

aoi = {"type": "FeatureCollection","features": [{"type": "Feature","properties": {}, "geometry": {"type": "Polygon","coordinates": [[[13.373408317565918,52.51568549156065], [13.381991386413574,52.51568549156065], [13.381991386413574,52.519302392686626], [13.373408317565918,52.519302392686626], [13.373408317565918,52.51568549156065]]]}}]}

In [5]:

# Define the search parameters and run the search.
catalog = up42.initialize_catalog()
search_paramaters = catalog.construct_parameters(geometry=aoi, 
                                                 start_date="2020-04-23",
                                                 end_date="2020-04-28",
                                                 collections=["phr"],
                                                 max_cloudcover=100,
                                                 sortby="acquisitionDate",
                                                 limit=3)

search_results = catalog.search(search_paramaters)
search_results

# Define the search parameters and run the search. catalog = up42.initialize_catalog() search_paramaters = catalog.construct_parameters(geometry=aoi, start_date="2020-04-23", end_date="2020-04-28", collections=["phr"], max_cloudcover=100, sortby="acquisitionDate", limit=3) search_results = catalog.search(search_paramaters) search_results

2022-07-02 14:53:40,211 - Searching catalog with search_parameters: {'datetime': '2020-04-23T00:00:00Z/2020-04-28T23:59:59Z', 'intersects': {'type': 'Polygon', 'coordinates': (((13.373408317565918, 52.51568549156065), (13.381991386413574, 52.51568549156065), (13.381991386413574, 52.519302392686626), (13.373408317565918, 52.519302392686626), (13.373408317565918, 52.51568549156065)),)}, 'limit': 3, 'collections': ['phr'], 'query': {'cloudCoverage': {'lte': 100}, 'up42:usageType': {'in': ['DATA', 'ANALYTICS']}}, 'sortby': [{'field': 'properties.acquisitionDate', 'direction': 'asc'}]}
2022-07-02 14:53:40,949 - 3 results returned.

Out[5]:

	geometry	id	acquisitionDate	constellation	collection	providerName	blockNames	cloudCoverage	up42:usageType	providerProperties	sceneId	resolution	deliveryTime	producer
0	POLYGON ((13.21858 52.45587, 13.34190 52.45724...	3c89938c-e669-4525-959b-62ba46c904e2	2020-04-23T10:19:52Z	phr	phr	oneatlas	[]	0.000000	[DATA]	{'qualified': False, 'sourceIdentifier': 'DS_P...	3c89938c-e669-4525-959b-62ba46c904e2	0.7	HOURS	airbus
1	POLYGON ((13.33197 52.33049, 13.46304 52.32930...	804c50f2-4093-4b85-ab84-e328b8fc47f1	2020-04-28T10:31:23Z	phr	phr	oneatlas	[]	0.013443	[DATA]	{'qualified': False, 'sourceIdentifier': 'DS_P...	804c50f2-4093-4b85-ab84-e328b8fc47f1	0.7	HOURS	airbus
2	POLYGON ((13.20620 52.45359, 13.33872 52.45525...	f53ee788-e20d-4fb0-94d1-a7f9e96a5ba4	2020-04-28T10:31:35Z	phr	phr	oneatlas	[]	0.000000	[DATA]	{'qualified': False, 'sourceIdentifier': 'DS_P...	f53ee788-e20d-4fb0-94d1-a7f9e96a5ba4	0.7	HOURS	airbus

In [85]:

# Download & visualise the full-scene preview images.
catalog.download_quicklooks(image_ids=search_results.id.to_list(), sensor="pleiades")
catalog.plot_quicklooks(figsize=(18,5), titles=search_results.sceneId.to_list())

# Download & visualise the full-scene preview images. catalog.download_quicklooks(image_ids=search_results.id.to_list(), sensor="pleiades") catalog.plot_quicklooks(figsize=(18,5), titles=search_results.sceneId.to_list())

2021-04-08 14:57:48,773 - Getting quicklooks from provider oneatlas for image_ids: ['3c89938c-e669-4525-959b-62ba46c904e2', '07e5eaba-7830-4aee-8c27-6b69fa1da89f', '804c50f2-4093-4b85-ab84-e328b8fc47f1']
2021-04-08 14:57:48,776 - Download directory: /Users/christoph.rieke/repos/up42-py/examples/data-block-examples/project_75d25f7a-426d-495f-8cfa-e54a57d2da74/catalog
100%|██████████| 3/3 [00:03<00:00,  1.23s/it]