Alvaritox/seasonstereo-data

SeasonStereo — Data & Checkpoints

Diachronic stereo matching for multi-date satellite imagery.

This repository hosts the datasets released with the SeasonStereo paper. The training/evaluation code lives in a separate GitHub repository: SeasonStereo. See that repo's README.md for installation, training, and evaluation instructions — this page only documents the data itself.

Links

• Code
• Project page
• Paper: coming soon
• Checkpoint

Contents

Folder	Size	Description
Train-Track3-cropped/	~4.8 GB	Real cropped RGB tiles (Track3), .tif, organized by AOI
Train-Track3-cropped-synthetic/	~19 GB	Seasonal synthetic variants of the tiles above (.png, 4 seasons per tile)
water_segmentation/, tree_segmentation/, building_segmentation/	~1 GB total	Semantic masks + probability maps for the cropped tiles
diachronic-stereo-synthetic/	~11.5 GB (train 1 AOI) + ~50 GB (val/test/other)	Main stereo training/validation/test set: rectified pairs, disparity, masks, homographies

Train-Track3-cropped/ and Train-Track3-cropped-synthetic/

Train-Track3-cropped/
├── Track3-RGB-1/<AOI, e.g. JAX_204>/<AOI>_<tile_id>_RGB.tif
└── Track3-RGB-2/<AOI, e.g. OMA_367>/<AOI>_<tile_id>_RGB.tif

Train-Track3-cropped-synthetic/
├── Track3-RGB-1/<AOI>/<AOI>_<tile_id>_RGB_{SPRING,SUMMER,AUTUMN,WINTER}.png
└── Track3-RGB-2/<AOI>/<AOI>_<tile_id>_RGB_{SPRING,SUMMER,AUTUMN,WINTER}.png

Same AOI/tile layout as the real tiles; each real .tif has four seasonal synthetic .png counterparts generated for training.

Segmentation masks

segmentation_masks/{water,tree,building}_segmentation/
├── masks/<tile>_mask.png        # binary/class segmentation mask
└── probs/<tile>_{water,tree,building}_prob.png   # per-pixel probability map

diachronic-stereo-synthetic/

The main dataset used to train and evaluate the stereo model.

diachronic-stereo-synthetic/
├── train/
│   ├── L/, R/                       # rectified left/right image pairs, .npy, float32, HxWx3
│   ├── disparity/                   # pseudo-GT / reference disparity, .iio (numpy array format)
│   ├── masks/, water_masks/, tree_masks/, building_masks/   # each split into L/ and R/, .npy, uint8, HxW
│   └── homography/                  # rectification homographies, .npz with keys: Hleft, Hright, out_shape
├── val/                              # same structure as train/
├── test/
│   ├── JAX/       # test_jax, test_jax_dsm_and_rpc (stereo pairs + DSM/RPC metadata)
│   ├── OMA/       # test_omaha_synchronic, test_omaha_diachronic, generated_matching_dataset_v2, all_oma_and_jax_dsm_and_rpc
│   └── IARPA/     # test_buenos_aires, test_buenos_aires_dsm_and_rpc
├── synchronic_only/                  # L/, R/, disparity/, homography/ — synchronic-only real pairs
├── disparity_maps/
│   ├── GT/                           # ground-truth disparity (LiDAR-derived)
│   └── monster++/                    # MonSter++ pseudo-label disparity used for supervision
├── experiments/
│   ├── all.csv                       # full training pair list
│   ├── sub1_synchronic.csv           # real synchronic pairs only
│   ├── sub2_synchronic.csv           # real + synthetic synchronic pairs
│   ├── sub3_syndiach.csv             # real/synthetic + synchronic/diachronic pairs
│   └── sub_val.csv                   # validation pairs (105 of 109 original, see note below)
├── intersected_pairs.csv
├── train_aois.csv / val_aois.csv / test_aois.csv
└── description_exps.txt              # original notes on subsets/experiments (see below)

Reading the array files

import numpy as np

# .npy files (L, R, masks, etc.)
arr = np.load("train/L/<file>.npy")

# .iio files are numpy arrays with a non-standard extension — same reader works
with open("train/disparity/<file>.iio", "rb") as f:
    disp = np.lib.format.read_array(f)

# homography .npz
h = np.load("train/homography/<file>.npz")
Hleft, Hright, out_shape = h["Hleft"], h["Hright"], h["out_shape"]

Checkpoint

checkpoints/season-stereo-final.pth — final SeasonStereo model weights (experiment: disparity + photometric + aware smoothness losses, supervised with MonSter++ pseudo-labels). Load with the training/eval code in the SeasonStereo repo.

Local layout for use with the code repo

To use this data directly with the SeasonStereo codebase, place it as:

data/
  diachronic-stereo-synthetic/
  synchronic_only/
  Train-Track3-cropped/
  Train-Track3-cropped-synthetic/
  water_segmentation/
  tree_segmentation/
  building_segmentation/

checkpoints/
  season-stereo-final.pth

Citation

The final citation will be added after publication metadata is available.

@inproceedings{seasonstereo2026,
  title     = {SeasonStereo: Diachronic Stereo Matching for Multi-Date Satellite Imagery},
  author    = {Authors},
  booktitle = {Venue},
  year      = {2026}
}

License

TBD.