EarthView dataset

Overview

The EarthView Dataset is a comprehensive collection of multispectral earth imagery. The dataset is divided into four distinct subsets sourced from Satellogic, Sentinel-1, Sentinel-2, and NEON imagers, each providing unique data.

Dataset Viewer

Check the EarthView Dataset Viewer and it's code for examples on how to access the images and navigate the dataset.

• EarthView dataset
  • Overview
  • Dataset Viewer
  • Data Sources
  • Available Subsets
  • Data Format
    • Satellogic
      • Metadata
      • Images
      • Example (Jupyter Notebook)
      • Example (iterate)
    • Sentinel-1
      • Metadata
      • Images
      • Example (Jupyter Notebook)
    • Neon
      • Metadata
      • Images
      • Example (Jupyter Notebook)
    • Sentinel-2
      • Metadata
      • Images
      • Example (Jupyter Notebook)
  • Known Issues
  • Citation

Data Sources

Each subset (AKA configuration) in the EarthView dataset includes samples representing specific patches of the Earth. Each source (satellite type) has different characteristics, so the details for the samples in each of the subsets are subtly different. We provide a very simple library to access the images in the subsets.

Available Subsets

Name	Samples	Unique locations	Products	Image Resolution
Satellogic	~6 million	~3 million	RGB	1m
			NIR (Near Infrared)	1m
Neon	~1 million	~0.3 million	RGB	0.1m
			Canopy Height Model (Lidar)	1m
			Hyperspectral (369 bands)	1m
Sentinel-1	~5.2 million	~1 million	SAR (mapped to RGB)	10m (from 20m)
Sentinel-2	~10 million	~1 million	RGB	10m
			NIR	10m
			NIR / Red Edge / SWIR	20m
			Scene Classification Layer	20m
			Coastal-Aerosol / Water Vapour / Cirrus	40 (from 60m)

Data Format

Each subset has some peculiarities and a specific data format in the dataset. Each item (sample) in the dataset is a dictionary with a metadata field and one or more entries for the different image products available, such as rgb, chm, 1m, 10m (see below). All of the image fields are 4D arrays where dimensions are REVISITS, BANDS, HEIGHT, WIDTH.

We encourage you to use the supplied earthview library to simplify accessing the dataset, metadata and images. (For now, download the single python file and place in the same directory as your scripts, or in Python's path)

Bellow you'll find details for each subset

Satellogic

Metadata

key	description
bounds	[x_min, y_min, x_max, y_max] (bottom-left corner and top-right corner coordinates in (easting, northing) format, WGS 84 / UTM).
	[178191.0, 8248444.0, 178575.0, 8248828.0]
crs	EPSG code
	['EPSG:32723']
timestamp	list of timestamps corresponding to the capture dates (only date is valid)
	['2022-07-21T00:00:00']
	['2022-07-21T00:00:00', '2022-07-25T00:00:00']
count	Number of re-visits for the location (not present in the dataset, generated by items_to_images())
	2

Images

Satellogic images are captured with Satellogic's MarkIV satellite fleet. The payload produces RGB and NIR images at 1m native resolution (no PAN sharpening). Each sample in the dataset has 1 or 2 re-visits per location.

key	Product	Image Resolution	Size (pixels)	Bands	Re-samples	Data type
rgb	RGB	1m	384 x 384	3	1 or 2	uint8
1m	NIR	1m	384 x 384	1	1 or 2	uint8

Note: Despite data type being stored in uint64, values range between 0 and 255 (uint8).

Example (Jupyter Notebook)

import numpy as np import earthview as ev data = ev.load_dataset("satellogic", shards=[10]) # shard is optional sample = next(iter(data)) print(sample.keys()) print(np.array(sample['rgb']).shape) # RGB Data print(np.array(sample['1m']).shape) # NIR Data

dict_keys(['1m', 'rgb', 'metadata']) (1, 3, 384, 384) (1, 1, 384, 384)

sample = ev.item_to_images("satellogic", sample) display(sample) display(*sample["rgb"]) display(*sample["1m"])

{'1m': [<PIL.Image.Image image mode=L size=384x384>], 'rgb': [<PIL.Image.Image image mode=RGB size=384x384>], 'metadata': {'bounds': [[178191.0, 8248444.0, 178575.0, 8248828.0]], 'crs': ['EPSG:32723'], 'timestamp': ['2022-08-13T00:00:00']}}

Example (iterate)

from itertools import islice import earthview as ev data = ev.load_dataset("satellogic", shards=[10]) # shard is optional datai = iter(data) for sample in islice(datai, 10): sample = ev.item_to_images("satellogic", sample) print(sample["metadata"]["bounds"]) sample["rgb"][0].show()

Sentinel-1

Metadata

key	description
type	'Polygon' Indicates the coordinates are the vertices of a Polygon
coordinates	Five coordinates of a closed Polygon.
	[[[434520.0, 8715520.0], [438360.0, 8715520.0], [438360.0, 8711680.0], [434520.0, 8711680.0], [434520.0, 8715520.0]]]
crs	EPSG code
	'epsg:32736'
count	Number of re-visits for the location (not present in the dataset, generated by items_to_images())
	6

Images

Sentinel-1 carries a Synthetic Aperture Radar (SAR) payload. The data (imagery) produced has two channels, for vertical and horizontal polarization. The data in the dataset contains just the two channels, the images returned by item_to_images() implements a standard mapping to return RGB images. (see the example below). Samples in the dataset contain varied numbers of re-visits per location.

key	Product	Image Resolution	Size (pixels)	Bands	Re-samples	Data type
10m	RGB	10m	384 x 384	3	1 or more	uint8

Note: Despite data type being stored in uint64, values range between 0 and 255 (uint8).

Example (Jupyter Notebook)

import numpy as np import earthview as ev data = ev.load_dataset("sentinel_1", shards=[88]) # shard is optional sample = next(iter(data)) print(sample.keys()) print(np.array(sample['rgb']).shape) # RGB Data print(np.array(sample['10m']).shape) # NIR Data

dict_keys(['10m', 'metadata']) (6, 2, 384, 384)

sample = ev.item_to_images("sentinel_1", sample) display(sample) display(*sample["10m"][:2])

{'10m': [<PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>], 'metadata': {'type': 'Polygon', 'crs': 'epsg:32736', 'coordinates': [[[434520.0, 8715520.0], [438360.0, 8715520.0], [438360.0, 8711680.0], [434520.0, 8711680.0], [434520.0, 8715520.0]]]}}

Neon

In the dataset, the subset/configuration for NEON is called default, but when using the earthview library you should call it neon.

Metadata

key	description
bounds	[x_min, y_min, x_max, y_max] (bottom-left corner and top-right corner coordinates in (easting, northing).
	[-82.04138011662944, 29.634596313943526, -82.04071312100113, 29.635179014437973]
epsg	EPSG code
	'EPSG:32617' (this is an error, coordinates are actually 'ESPG:4326', the library (earthview) solves it)
timestamp	list of timestamps corresponding to the capture dates (only date is valid)
	['2018-01-01T00:00:00', '2019-01-01T00:00:00', '2021-01-01T00:00:00']
siteID	'OSBS' for Ordway-Swisher Biological Station
count	Number of re-visits for the location (not present in the dataset, generated by items_to_images())
	3

Images

The NEON subset is composed of very high resolution RGB images at 0.1m, 1m hyperspectral data (369 bands), and 1m Canopy Height Model out of a LIDAR sensor. Every sample in the dataset contains 3 re-visits.

key	Product	Image Resolution	Size (pixels)	Bands	Re-samples	Data type
rgb	RGB	0.1m	640 x 640	3	3	uint8
chm	Canopy Height Model	1m	64 x 64	1	3	uint8
1m	Hyperspectral	1m	64 x 64	369	3	uint8

When using item_to_images() the Hyperspectral images are mapped, from the 369 bands to RGB using a meaningless mapping. Please, don't use it for anything else than an example. Note that images were stored in uint64, although values range between 0 and 255 (uint8).

Example (Jupyter Notebook)

import numpy as np import earthview as ev data = ev.load_dataset("neon", shards=[100]) # shard is optional sample = next(iter(data)) print(sample.keys()) print(np.array(sample['rgb']).shape) # RGB Data print(np.array(sample['chm']).shape) # Canopy Height print(np.array(sample['1m']).shape) # Hyperspectral

dict_keys(['1m', 'chm', 'rgb', 'metadata']) (3, 3, 640, 640) (3, 1, 64, 64) (3, 369, 64, 64)

sample = ev.item_to_images("neon", sample) display(sample) display(sample["rgb"][0]) display(sample["1m"][0]) display(sample["chm"][0])

{'1m': [<PIL.Image.Image image mode=RGB size=64x64>, <PIL.Image.Image image mode=RGB size=64x64>, <PIL.Image.Image image mode=RGB size=64x64>], 'chm': [<PIL.Image.Image image mode=L size=64x64>, <PIL.Image.Image image mode=L size=64x64>, <PIL.Image.Image image mode=L size=64x64>], 'rgb': [<PIL.Image.Image image mode=RGB size=640x640>, <PIL.Image.Image image mode=RGB size=640x640>, <PIL.Image.Image image mode=RGB size=640x640>], 'metadata': {'bounds': [-82.04138011662944, 29.634596313943526, -82.04071312100113, 29.635179014437973], 'epsg': 'ESPG:4326', 'siteID': 'OSBS', 'timestamp': ['2018-01-01T00:00:00', '2019-01-01T00:00:00', '2021-01-01T00:00:00']}}

Sentinel 2

Metadata

key	description
s3Path	List of Sentinel 2 tiles. These are available in AWS Open Data Registry
	['tiles/18/N/VF/2021/12/9/0',..., 'tiles/18/N/VF/2021/11/14/0']
solarAngles	Solar angles in degrees
	['{"azimuth": 140.394, "zenith": 30.3633}',..., '{"azimuth": 136.448, "zenith": 25.8697}']
tileGeometry	'Polygon' geometry
	['{"type": "Polygon", "crs": "epsg:32618", "coordinates": [[[430680.0, 4020.0], [434520.0, 4020.0], [434520.0, 180.0], [430680.0, 180.0], [430680.0, 4020.0]]]}']
timestamp	timestamps
	['2021-12-09T15:32:59.458Z',..., '2021-11-14T15:32:59.654Z']
viewIncidenceAngles	View incidence angles in degrees
	['{"azimuth": {"B02": 288.805, "B03": 291.359, "B04": 293.693, "B08": 290.083, "B05": 294.94, "B06": 296.189, "B07": 297.42, "B8A": 298.651, "B11": 295.947, "B12": 298.873, "B01": 299.813, "B09": 301.042}, "zenith": {"B02": 6.28651, "B03": 6.32378, "B04": 6.3693, "B08": 6.30356, "B05": 6.3982, "B06": 6.43049, "B07": 6.46563, "B8A": 6.50413, "B11": 6.42396, "B12": 6.51144, "B01": 6.54365, "B09": 6.58895}}', '{"azimuth": {"B02": 287.726, "B03": 290.233, "B04": 292.531, "B08": 288.98, "B05": 293.761, "B06": 294.998, "B07": 296.215, "B8A": 297.428, "B11": 294.722, "B12": 297.612, "B01": 298.585, "B09": 299.806}, "zenith": {"B02": 6.39968, "B03": 6.43145, "B04": 6.47169, "B08": 6.41401, "B05": 6.49769, "B06": 6.52711, "B07": 6.55927, "B8A": 6.59462, "B11": 6.52027, "B12": 6.60026, "B01": 6.6314, "B09": 6.67363}}',...,'{"azimuth": {"B02": 287.734, "B03": 290.267, "B04": 292.586, "B08": 289.001, "B05": 293.828, "B06": 295.078, "B07": 296.307, "B8A": 297.531, "B11": 294.8, "B12": 297.717, "B01": 298.699, "B09": 299.93}, "zenith": {"B02": 6.34038, "B03": 6.37228, "B04": 6.41274, "B08": 6.35475, "B05": 6.43893, "B06": 6.46856, "B07": 6.50099, "B8A": 6.53659, "B11": 6.46168, "B12": 6.54227, "B01": 6.57366, "B09": 6.61622}}']}
count	Number of re-visits for the location (not present in the dataset, generated by items_to_images())
	10

Images

Sentinel-2 carries a Multi-Spectral Instrument (MSI) payload. The data (imagery) produced contains 13 spectral bands across the visible, near-infrared (NIR), and shortwave infrared (SWIR) spectra. The data in the dataset includes these 13 bands. Samples in the dataset contain varied numbers of re-visits per location.

key	Product	Image Resolution	Size (pixels)	Bands	Re-samples	Data type
10m	NIR	10m	384 x 384	1	multiple	uint8
20m	20m bands	20m	192 x 192	6	multiple	uint8
40m	60m bands	10m	96 x 96	2	multiple	uint8
rgb	RGB	10m	384 x 384	3	multiple	uint8
scl	SCL	20m	192 x 192	1	multiple	uint8

Note: Despite data type being stored in uint64, values range between 0 and 255 (uint8).

Example (Jupyter Notebook)

import numpy as np import earthview as ev data = ev.load_dataset("sentinel_2", shards=[88]) # shard is optional sample = next(iter(data)) print(sample.keys()) print(np.array(sample['10m']).shape) print(np.array(sample['20m']).shape) print(np.array(sample['40m']).shape) print(np.array(sample['rgb']).shape) print(np.array(sample['scl']).shape)

dict_keys(['10m', '20m', '40m', 'rgb', 'scl', 'metadata']) (10, 1, 384, 384) (10, 6, 192, 192) (10, 2, 96, 96) (10, 3, 384, 384) (10, 1, 192, 192)

sample = ev.item_to_images("sentinel_2", sample) display(sample) display(sample["10m"][0]) display(sample["rgb"][0])

{'10m': [<PIL.Image.Image image mode=L size=384x384>, <PIL.Image.Image image mode=L size=384x384>, <PIL.Image.Image image mode=L size=384x384>, <PIL.Image.Image image mode=L size=384x384>, <PIL.Image.Image image mode=L size=384x384>, <PIL.Image.Image image mode=L size=384x384>, <PIL.Image.Image image mode=L size=384x384>, <PIL.Image.Image image mode=L size=384x384>, <PIL.Image.Image image mode=L size=384x384>, <PIL.Image.Image image mode=L size=384x384>], '20m': [<PIL.Image.Image image mode=RGB size=192x192>, <PIL.Image.Image image mode=RGB size=192x192>, <PIL.Image.Image image mode=RGB size=192x192>, <PIL.Image.Image image mode=RGB size=192x192>, <PIL.Image.Image image mode=RGB size=192x192>, <PIL.Image.Image image mode=RGB size=192x192>, <PIL.Image.Image image mode=RGB size=192x192>, <PIL.Image.Image image mode=RGB size=192x192>, <PIL.Image.Image image mode=RGB size=192x192>, <PIL.Image.Image image mode=RGB size=192x192>], '40m': array([[[[ 4, 4, 4, ..., 115, 108, 102], [ 4, 4, 4, ..., 102, 95, 90], [ 4, 4, 4, ..., 89, 82, 76], ..., [ 54, 54, 54, ..., 65, 65, 64], [ 54, 55, 56, ..., 63, 62, 62], [ 55, 56, 57, ..., 62, 60, 60]]]], dtype=uint8), 'rgb': [<PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>, <PIL.Image.Image image mode=RGB size=384x384>], 'scl': [<PIL.Image.Image image mode=L size=192x192>, <PIL.Image.Image image mode=L size=192x192>, <PIL.Image.Image image mode=L size=192x192>, <PIL.Image.Image image mode=L size=192x192>, <PIL.Image.Image image mode=L size=192x192>, <PIL.Image.Image image mode=L size=192x192>, <PIL.Image.Image image mode=L size=192x192>, <PIL.Image.Image image mode=L size=192x192>, <PIL.Image.Image image mode=L size=192x192>, <PIL.Image.Image image mode=L size=192x192>], 'metadata': {'s3Path': ['tiles/18/N/VF/2021/12/9/0', 'tiles/18/N/VF/2021/10/25/0', 'tiles/18/N/VF/2021/3/14/0', 'tiles/18/N/VF/2021/6/12/0', 'tiles/18/N/VF/2021/12/14/0', 'tiles/18/N/VF/2021/11/19/0', 'tiles/18/N/VF/2021/12/4/0', 'tiles/18/N/VF/2021/9/5/0', 'tiles/18/N/VF/2020/12/9/0', 'tiles/18/N/VF/2021/11/14/0'], 'solarAngles': [{'azimuth': 140.394, 'zenith': 30.3633}, {'azimuth': 124.852, 'zenith': 22.0045}, {'azimuth': 95.5396, 'zenith': 24.7237}, {'azimuth': 41.6202, 'zenith': 31.7255}, {'azimuth': 140.117, 'zenith': 31.0129}, {'azimuth': 138.058, 'zenith': 26.8774}, {'azimuth': 140.325, 'zenith': 29.6254}, {'azimuth': 73.0239, 'zenith': 22.8998}, {'azimuth': 140.382, 'zenith': 30.3984}, {'azimuth': 136.448, 'zenith': 25.8697}], 'tileGeometry': [{'type': 'Polygon', 'crs': 'epsg:32618', 'coordinates': [[[430680.0, 4020.0], [434520.0, 4020.0], [434520.0, 180.0], [430680.0, 180.0], [430680.0, 4020.0]]]}], 'timestamp': ['2021-12-09T15:32:59.458Z', '2021-10-25T15:33:02.398Z', '2021-03-14T15:33:00.373Z', '2021-06-12T15:33:01.436Z', '2021-12-14T15:32:54.854Z', '2021-11-19T15:33:00.970Z', '2021-12-04T15:32:56.151Z', '2021-09-05T15:32:57.392Z', '2020-12-09T15:32:58.492Z', '2021-11-14T15:32:59.654Z'], 'viewIncidenceAngles': [{'azimuth': {'B02': 288.805, 'B03': 291.359, 'B04': 293.693, 'B08': 290.083, 'B05': 294.94, 'B06': 296.189, 'B07': 297.42, 'B8A': 298.651, 'B11': 295.947, 'B12': 298.873, 'B01': 299.813, 'B09': 301.042}, 'zenith': {'B02': 6.28651, 'B03': 6.32378, 'B04': 6.3693, 'B08': 6.30356, 'B05': 6.3982, 'B06': 6.43049, 'B07': 6.46563, 'B8A': 6.50413, 'B11': 6.42396, 'B12': 6.51144, 'B01': 6.54365, 'B09': 6.58895}}, ... {'azimuth': {'B02': 287.734, 'B03': 290.267, 'B04': 292.586, 'B08': 289.001, 'B05': 293.828, 'B06': 295.078, 'B07': 296.307, 'B8A': 297.531, 'B11': 294.8, 'B12': 297.717, 'B01': 298.699, 'B09': 299.93}, 'zenith': {'B02': 6.34038, 'B03': 6.37228, 'B04': 6.41274, 'B08': 6.35475, 'B05': 6.43893, 'B06': 6.46856, 'B07': 6.50099, 'B8A': 6.53659, 'B11': 6.46168, 'B12': 6.54227, 'B01': 6.57366, 'B09': 6.61622}}], 'count': 10}}

Known Issues

• Satellogic timestamps are missing time information, only dates are provided. You can access full Satellogic sample timestamps in our STAC Catalog. Please visit our Satellogic EarthView website for more information. We additionally provide a notebook showing how to navigate through Satellogic dataset.

Citation

To reference this work in your research or publications, please consider citing:

@inproceedings{earthview2025,
                author={Velázquez, Diego and Rodríguez, Pau and Alonso, Sergio and Gonfaus, Josep M. and González, Jordi and, Richarte, Gerardo and Marín, Javier and Bengio, Yoshua and Lacoste, Alexandre},
                booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)}, 
                title={EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision}, 
                year={2025}}