The ClearSCD model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery

[Paper]

Introduction

A remote sensing semantic change detection model, Comprehensively leveraged sEmantics and chAnge Relationships Semantics Change Detection model, named ClearSCD.

This new method draws inspiration from the mutual reinforcement of semantic and change information in the multi-task learning model.

Overview of the ClearSCD.

Innovations

The main innovations in ClearSCD are as follows:

1. We introduced a supervised Semantics Augmented Contrastive Learning (SACL) module, utilizing both local and global data features, along with cross-temporal differences.

2. A Bi-temporal Semantic Correlation Capture (BSCC) mechanism is designed, allowing for the refinement of semantics through the output of the Binary Change Detection (BCD) branch.

3. A deep CVAPS module in classification posterior probability space is developed to execute BCD by integrating semantics posterior probabilities instead of high-dimensional features.

Requirements

1. The pytorch version of torchvision>=0.13.1 is recommended to ensure that the torchvision library contains Efficientnet's pretrained weights.
2. Then pip install segmentation-models-pytorch to install a Python library Segmentation Models Pytorch for image segmentation based on PyTorch.

Getting Started

1. Download Hi-UCD series dataset.

2. Deal with the dataset using clip_image.py, deal_hiucd.py, and write_path.py from the folder scripts.
   Note: After running the deal_hiucd.py, the classification codes in Hi-UCD with the land cover class in order minus 1, the unlabeled region as 9 in bi-temporal semantic maps, and unlabeled as 255 in BCD.

3. Run main.py, then you will find the checkpoints in the results folder.

4. Run test.py, then you will obtain the test metric and visual results. Our checkpoint on the Hi-UCD-mini dataset can be downloaded from Google Drive

Dataset

We have released the extended LsSCD dataset, LsSCD-Ex. [Download link]

Note: If you need the original, uncropped large-scale TIFF imagery, please contact us (tangkai@mail.bnu.edu.cn) to obtain the download link.

Citation

If you use the ClearSCD codes or the LsSCD-Ex dataset, please cite our paper:

@article{tang2024clearscd,
title = {The ClearSCD model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery},
author = {Tang, Kai and Xu, Fei and Chen, Xuehong and Dong, Qi and Yuan, Yuheng and Chen, Jin},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {211},
pages = {299-317},
year = {2024},
issn = {0924-2716},
}

@article{tang2026dreamcd,
  title = {DreamCD: A Change-Label-Free Framework for Change Detection via a Weakly Conditional Semantic Diffusion Model in Optical VHR Imagery},
  author = {Tang, Kai and Zheng, Zhuo and Chen, Hongruixuan and Chen, Xuehong and Chen, Jin},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {146},
  pages = {105125},
  year = {2026},
  issn = {1569-8432},
  doi = {10.1016/j.jag.2026.105125},
}