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Pytorch codes ofJoint Spatio-Temporal Modeling for Semantic Change Detection in Remote Sensing Images[paper]

For readers to easily evaluate the accuracy, we provide the trained weights.

SECOND:

1.Drive
2.Baidu (pswd: SCAN)

LandsatSCD:

1.Drive
2.Baidu (pswd: SCAN)

The land-scd dataset needs to be pre-processed to meet the experimental settings in this paper.More details are provided at/datasets/LandsatSCD/read_me.md

For readers' convenience, we also provide the preprocessed data:

Baidu Netdisk (psswd lscd)

Google Drive

If you find this work useful or interesting, please consider citing the following BibTeX entry.

@article{ding2024joint,  title={Joint Spatio-Temporal Modeling for Semantic Change Detection in Remote Sensing Images},  author={Ding, Lei and Zhang, Jing and Guo, Haitao and Zhang, Kai and Liu, Bing and Bruzzone, Lorenzo},  journal={IEEE Transactions on Geoscience and Remote Sensing},   year={2024},  volume={62},  pages={1-14},  doi={10.1109/TGRS.2024.3362795}}

(Note: This repository is under construction, contents are not final.)