Abstract: The exploitation of mineral resources,while promoting regional economic development,has also caused some damage on land surface and has an impact on the ecological environment. The remote sensing technology for timely ob- taining the change information on land cover and ecological environment in mining areas can be used in practical ap- plications such as guiding ecological protection and restoration of mining areas. Aiming at the need to extract a large number of artificially designed image features in the traditional change detection method,an improved UNet siamese network is proposed. The convolution layer is used to replace the pooling layer in the UNet structure,and the siamese structure with dual channels,the feature pyramid module and central surround module was added. Firstly,the preprocessed remote sensing images of the two periods are cropped by central surround and the multi-scale information of the image is acquired,and the images of the central area and the surround area are respectively input into the encoder part of the network structure. The different information between the two periods is extracted by the weight shared siamese structure,then,the features on the same feature layer are subtracted,and the different images on different convolutional layers are obtained and the feature fusion is performed. The merged image is sent to the feature pyramid module to ob- tain image multi-scale context information. Finally,the corresponding features of the encoder and the decoder are fused by skip connection,and the end-to-end prediction is performed to obtain the change binary image of the mining area remote sensing image in the two periods. The results show that the improved change detection network method can au- tomatically extract the low-level features and high-level semantic features of the image compared with the traditional methods,which avoids the cumbersome manual extraction of image features. In addition,from the detection results,the improved change detection method has a significant improvement in overall accuracy and Kappa coefficient compared with the comparison method,and also reduces the commission error and omission error of the detection result.