国家自然科学基金（41971311; 42101381; 41901282）和安徽省自然科学基金（2008085QD188）共同资助。
The extraction of bare land using remote sensing images is an important means to monitor the spatial distribution of bare land. Aiming at the problems of unclear boundaries, spatial information loss, small area bare land missing extraction and difficulty in distinguishing buildings with high reflectivity, a deep learning model from remote sensing bare land extraction with improved DenseNet was designed, mainly adopting three methods of DenseBlock, coordinate convolution and dense atrous spatial pyramid pooling to enhance the ability of DenseNet model in acquiring coordinate information, enriching spatial feature information of bare land and sensing global context information, reducing the model for spatial detail feature loss links and improving the accuracy of bare land remote sensing extraction. The experiments showed that the method extracts bare land with 97.66% overall accuracy, the IoU was 68.69%, the comprehensive evaluation index F1 was 81.44%, and the recall rate was 76.62. %, the false alarm rate was 25.68%, which was significantly better than other machine learning methods and deep learning methods. In addition, the model also has good universality for bare ground extraction on multi-source remote sensing images. The OA tested on remote sensing datasets such as GF-1, GF-6 and Sentinel-2 were 95.80%, 93.00%, and 92.55%, respectively; the IoU were 75.18%, 75.13%, and 50.47%, respectively; the F1 were 85.83%, 85.80% and 67.08%, respectively. Therefore, the improved DenseNet model method is more suitable for the extraction of bare land than other methods.