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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
• 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
.
• 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).
Dataset Card Contact
Chenying Liu (chenying.liu@tum.de; chenying.liu023@gmail.com)
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