README

April 22, 2025 · View on GitHub

Cropland-SCD

The pytorch implementation for MeGNet in paper "A Memory Guided Network and A Novel Dataset for Cropland Semantic Change Detection" on IEEE Transactions on Geoscience and Remote Sensing.

The CropSCD dataset contains 4,141 pairs of high-resolution samples, each with a size of 512×512 and a resolution between 0.5-2 meters. All images of the samples were randomly collected from rural areas of Guangdong Province, China, while their corresponding labels were precisely annotated through expert visual interpretation. The dataset contains a total of eight distinct change classes, which include Water, Forest, Plantation, Grassland, Impervious Surface, Greenhouse, Road, and Bare Soil.

Download the CropSCD Dataset: OneDrive | Baidu
Download the HRSCD Dataset

Citation

Please cite our paper if you use this code in your work:

@ARTICLE{10579791,
  author={Liu, Mengxi and Lin, Simin and Zhong, Yutong and Shi, Qian and Li, Jiaqi},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={A Memory Guided Network and A Novel Dataset for Cropland Semantic Change Detection}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  keywords={Semantics;Feature extraction;Task analysis;Transformers;Land surface;Soil;Plantations;Remote sensing;semantic change detection;deep learning;memory;Transformer},
  doi={10.1109/TGRS.2024.3421654}}

README

Cropland-SCD

Requirements

Datasets

CropLand Senmantic Change Dection (CropSCD) Dataset

Citation