--- license: mit task_categories: - image-segmentation size_categories: - 100K **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:** ```Bibtex @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}} ``` ---------------------------------------------------------------------------------------------------------------- ## • 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. ---------------------------------------------------------------------------------------------------------------- ## Dataset Card Contact Chenying Liu (chenying.liu@tum.de; chenying.liu023@gmail.com)