Abstract: With the improvement of the spatial resolution of remote sensing images, the imaging features of ground objects have become increasingly complex. As a result, the change detection methods of remote sensing images based on texture expression and local semantics are difficult to meet the demand. To improve the change detection accuracy of high-resolution remote sensing images, this study constructed a large-scale remote sensing-based human activity change detection dataset (HRHCD-1.0) with a high resolution of 0.8~2 m. Moreover, this study designed an attention-based Siamese change detection network with a strong capability to extract contextual semantic features by introducing spatial attention and channel attention mechanisms. In the model comparative experiment, the attention-based Siamese change detection network proposed in this study increased the mean intersection over union on the validation set by 24% and showed more complete detection results compared to the models using non-attention mechanisms, effectively alleviating the problems of poor boundary, local omission, and holes of models using non-attention mechanisms. The post-processing method allows for small polygon removal, hole filling, and graphic smoothing of the detection results, improving the processing graphic effects of polygons. Furthermore, the increase in the sample size in the training of change detection significantly improves the application accuracy and generalization ability of the attention-based Siamese change detection network proposed in this study.