Dataset Card for NOLDO-S12 Dataset

NoLDO-S12 is a multi-modal dataset for remote sensing image segmentation from Sentinel-1&2 images, which contains two splits: SSL4EO-S12@NoL with noisy labels for pretraining, and two downstream datasets, SSL4EO-S12@DW and SSL4EO-S12@OSM, with exact labels for transfer learning.

Dataset Details

• Curated by: Chenying Liu, Conrad M Albrecht, Yi Wang, Xiao Xiang Zhu
• License: MIT
• Repository: More details at https://github.com/zhu-xlab/CromSS
• Paper [arXiv]: https://arxiv.org/abs/2405.01217
• Citation:

  @ARTICLE{liu-cromss,
    author={Liu, Chenying and Albrecht, Conrad M and Wang, Yi and Zhu, Xiao Xiang},
    journal={IEEE Transactions on Geoscience and Remote Sensing}, 
    title={CromSS: Cross-modal pretraining with noisy labels for remote sensing image segmentation}, 
    year={2025},
    volume={},
    number={},
    pages={in press}}
  

• Contents:

  Type	File	Description
  Data	ssl4eo_s12_nol.zip	SSL4EO-S12@NoL pretraining dataset with noisy labels
  	ssl4eo_s12_dw.zip	SSL4EO-S12@DW downstream dataset with 9-class exact labels from the Google DW project
  	ssl4eo_s12_osm.zip	SSL4EO-S12@OSM downstream dataset with 13-class exact labels from OSM
  weights	weights-cromss-13B-midFusion-epoch=199.ckpt	pretrained with CromSS and middle fusion using S1 and 13-band S2
  	weights-cromss-13B-lateFusion-epoch=199.ckpt	pretrained with CromSS and late fusion using S1 and 13-band S2
  	weights-cromss-9B-midFusion-epoch=199.ckpt	pretrained with CromSS and middle fusion using S1 and 9-band S2
  	weights-cromss-9B-lateFusion-epoch=199.ckpt	pretrained with CromSS and late fusion using S1 and 9-band S2

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• SSL4EO-S12@NoL

SSL4EO-S12@NoL paired the large-scale, multi-modal, and multi-temporal self-supervised SSL4EO-S12 dataset with the 9-class noisy labels (NoL) sourced from the Google Dynamic World (DW) project on Google Earth Engine (GEE). To keep the dataset's multi-temporal characteristics, we only retain the S1-S2-noisy label triples from the locations where all 4 timestamps of S1-S2 pairs have corresponding DW labels, resulting in about 41% (103,793 out of the 251,079 locations) noisily labeled data of the SSL4EO-S12 dataset. SSL4EO-S12@NoL well reflects real-world use cases where noisy labels remain more difficult to obtain than bare S1-S2 image pairs.

The ssl4eo_s12_nol.zip contains the 103,793 DW noisy mask quadruples paired for the SSL4EO-S12 dataset. The paired location IDs are recorded in dw_complete_ids.csv.

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• SSL4EO-S12@DW & SSL4EO-S12@OSM

SSL4EO-S12@DW and SSL4EO-S12@OSM were constructed for RS image segmentation transfer learning experiments with Sentinel-1/2 data. Both are selected on the DW project’s manually annotated training and validation datasets, yet paired with different label sources from DW and OSM.
SSL4EO-S12@DW was constructed from the DW expert labeled training subset of 4,194 tiles with given dimensions of 510×510 pixels and its hold-out validation set of 409 tiles with given dimensions of 512×512. The human labeling process allows some ambiguous areas left unmarked. We spatial-temporally aligned the S1 and S2 data for the training and test tiles with GEE, leading to 3,574 training tiles and 340 test tiles, that is, a total of 656,758,064 training pixels and 60,398,506 test pixels.
SSL4EO-S12@OSM adopts 13-class fine-grained labels derived from OpenStreetMap (OSM) following the work of Schultz et al. We retrieved 2,996 OSM label masks among the 3,914=3,574+340 DW tiles, with the remaining left without OSM labels. After an automatic check with DW labels as reference assisted by some manual inspection, we construct SSL4EO-S12@OSM with 1,375 training tiles and 400 test tiles, that is, a total of 165,993,707 training pixels and 44,535,192 test pixels.

The ssl4eo_s12_dw.zip and ssl4eo_s12_osm.zip contain the training and test splits for the two curated downstream datasets.

The ground-truth mask key for the DW test split is lulc (the second layer).

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Dataset Card Contact

Chenying Liu (chenying.liu@tum.de; chenying.liu023@gmail.com)