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 (coming soon), 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
      • Examples (Jupyter Notebook)
    • Sentinel-1
      • Metadata
      • Images
      • Examples (Jupyter Notebook)
    • Neon
      • Metadata
      • Images
      • Examples (Jupyter Notebook)
    • Sentinel 2 (Coming Soon)
  • Known Issues

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

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

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.

Examples (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'],
  'count': 1}}

Sentinel-1

Metadata

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.

Examples (Jupyter Notebook)

import numpy as np
import earthview as ev

# data = ev.load_dataset("sentinel_1", shards=[88])  # shard is optional
data = ev.load_parquet("dataset/sentinel_1/train-00088-of-01763.parquet")

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]]],
  'count': 6}}

Neon

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

Metadata

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.

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.

Examples (Jupyter Notebook)

import numpy as np
import earthview as ev

data = ev.load_dataset("neon", shards=[100])  # shard is optional
# data = ev.load_parquet("neon", batch_size = 10)

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': 'EPSG:32617',
  'siteID': 'OSBS',
  'timestamp': ['2018-01-01T00:00:00',
   '2019-01-01T00:00:00',
   '2021-01-01T00:00:00'],
  'count': 3}}

Sentinel 2 (Coming Soon)

Check the EarthView Dataset Viewer and it's code to get an idea on how to use it.

Known Issues

• The time component of timestamps is not correct.
• Bounds for some images is not correct. We are evaluating what has happened.

We intend to update the dataset to fix issues when possible