Create README.md

README.md

@@ -0,0 +1,83 @@
+---
+license: apache-2.0
+---
+
+# OPTIMUS Dataset
+
+This dataset contains approximately 600K image time series of 40-50 Sentinel-2 satellite images captured between January 2016 and December 2023.
+
+It also includes 300 time series that are labeled with binary "change" or "no change" labels.
+
+It is used to train and evaluate OPTIMUS (TODO - paper link).
+
+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.
+
+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.
+
+The dataset is released under Apache License 2.0.
+
+
+## Dataset Details
+
+### Images
+
+The bulk of the dataset is stored in tar files in the "images" directory.
+Once extracted, these images follow this directory structure:
+
+```
+2016-01/
+  tci/
+    1234_5678.png
+    2345_6789.png
+    ...
+2016-03/
+  tci/
+    1234_5678.png
+    ...
+2016-05/
+  ...
+...
+2023-11/
+```
+
+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.
+
+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).
+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.
+
+For example, here are the corners of 1234_5678.png:
+
+```python
+import math
+
+def mercator_to_geo(p, zoom=13, pixels=512):
+    n = 2**zoom
+    x = p[0] / pixels
+    y = p[1] / pixels
+    x = x * 360.0 / n - 180
+    y = math.atan(math.sinh(math.pi * (1 - 2.0 * y / n)))
+    y = y * 180 / math.pi
+    return (x, y)
+
+for offset in [(0, 0), (0, 1), (1, 0), (1, 1)]:
+  print(mercator_to_geo((1234 + offset[0], 5678 + offset[1]), pixels=1))
+```
+
+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.
+
+### Other Files
+
+Besides the images, there are additional files:
+
+- `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.
+- `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.
+- `forest_loss_dataset.tar` contains additional image time series that contain forest loss.
+- 
+
+## Authors
+
+- Raymond Yu (University of Washington)
+- Paul Han (University of Washington)
+- Josh Myers-Dean (Allen Institute of AI)
+- Piper Wolters (Allen Institute of AI)
+- Favyen Bastani (Allen Institute of AI)