Fast extraction of buildings with high accuracy from remote sensing images is an important research of remote sensing intelligent application services. To address the problems of imprecise segmentation of building edge in remote sensing images, holes in large-scale target segmentation, and a large amount of network parameters in the DeepLab model, a lightweight DeepLabv3+ model for building extraction from remote sensing images is proposed. In this method, the lightweight network MobileNetv2 is used to replace Xception, the backbone network of DeepLabv3+, so as to reduce the number of parameters and improve the training speed; The hole rate of hole convolution in ASPP is optimized to improve the effect of multi-scale semantic feature extraction. The improved model has been tested on WHU and Massachusetts data sets. The results show that the IOU and F1 score in WHU dataset are 82.37% and 92.89%, respectively, 2.71 percentage points and 2.14 percentage points higher than those of DeepLabv3+, 2.04 percentage points, and 2.32 percentage points higher than those of DeepLabv3+ in Massachusetts data set. The number of training parameters and training time is reduced, and the accuracy of the building extraction is effectively improved, which can meet the requirements of fast extraction of high-precision buildings.
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