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license: apache-2.0 |
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# OPTIMUS Dataset |
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This dataset contains approximately 600K image time series of 40-50 Sentinel-2 satellite images captured between January 2016 and December 2023. |
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It also includes 300 time series that are labeled with binary "change" or "no change" labels. |
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It is used to train and evaluate OPTIMUS (TODO - paper link). |
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The time series are distributed globally, with half of the time series selected at random locations covered by Sentinel-2, and the other half sampled specifically within urban areas. |
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Each image is 512x512 at roughly 10 m/pixel (the source image is 10 m/pixel but it is re-projected to WebMercator). Within each time series, the images are aligned and so cover the same location at different timestamps. |
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The dataset is released under Apache License 2.0. |
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## Dataset Details |
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### Images |
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The bulk of the dataset is stored in tar files in the "images" directory. |
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Once extracted, these images follow this directory structure: |
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``` |
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2016-01/ |
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tci/ |
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1234_5678.png |
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2345_6789.png |
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... |
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2016-03/ |
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tci/ |
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1234_5678.png |
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... |
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2016-05/ |
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... |
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... |
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2023-11/ |
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``` |
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Here, the top level folders are different timestamps, so one time series consists of the images with the same filename (like `1234_5678.png`) across the different timestamp folders. |
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The filename identifies a position in the WebMercator grid at zoom level 13 (where the world is split into 2^13 tiles vertically and 2^13 tiles horizontally). |
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This matches the grid system used in Satlas; see https://github.com/allenai/satlas/blob/main/SatlasPretrain.md#coordinates for how to get the corner longitude/latitude coordinates from the tile. |
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For example, here are the corners of 1234_5678.png: |
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```python |
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import math |
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def mercator_to_geo(p, zoom=13, pixels=512): |
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n = 2**zoom |
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x = p[0] / pixels |
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y = p[1] / pixels |
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x = x * 360.0 / n - 180 |
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y = math.atan(math.sinh(math.pi * (1 - 2.0 * y / n))) |
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y = y * 180 / math.pi |
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return (x, y) |
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for offset in [(0, 0), (0, 1), (1, 0), (1, 1)]: |
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print(mercator_to_geo((1234 + offset[0], 5678 + offset[1]), pixels=1)) |
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``` |
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Each image is cropped from a Sentinel-2 L1C scene, using B04/B03/B02 only. See https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2 for details about the Sentinel-2 mission. |
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### Other Files |
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Besides the images, there are additional files: |
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- `index.json` identifies which tar files contain which tiles. It is a list of groups of files, and `groups[1234]` corresponds to the files present in 1234.tar. |
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- `2024_dataset_tiles_random.json` and `2024_dataset_tiles_urban.json` differentiate which tiles were selected based on random global sampling, and which were selected based on targeted sampling of urban areas. |
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- `forest_loss_dataset.tar` contains additional image time series that contain forest loss. |
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- |
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## Authors |
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- Raymond Yu (University of Washington) |
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- Paul Han (University of Washington) |
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- Josh Myers-Dean (Allen Institute of AI) |
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- Piper Wolters (Allen Institute of AI) |
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- Favyen Bastani (Allen Institute of AI) |
