An Intelligent Building Recognition Method in Remote Sensing Images Based on Cascade R-CNN

Building recognition and detection in remote sensing images are of great significance for urban planning, spatial database updating, and the construction of urban geographic information systems. For remote sensing images with complex background information, variations in the size of building objects make automatic building detection and recognition challenging, thereby affecting the recognition accuracy of deep learning models. At the same time, the lack of a standardized workflow for converting detection results into vector data formats makes it difficult to directly transform building detection results into usable GIS-compatible vector data. Based on the Cascade R-CNN model, an intelligent building recognition model for remote sensing images and a vectorization workflow for the recognition results are proposed. To address the issue of building recognition accuracy in remote sensing images, an intelligent building recognition model comprising ResNet101, a Feature Pyramid Network (FPN), a Region Proposal Network (RPN), and a cascade detector is proposed, which enhances the recognition precision and localization capability of building objects in multi-scale remote sensing images. To address the efficiency issue of vectorizing detection results, a procedural conversion method for building detection results in remote sensing images is proposed, which converts raster recognition results into GIS-compatible vector files through data verification, information extraction, boundary construction, polygon generation, and format conversion. Experiments show that the intelligent recognition model achieves a recall of 0.958, a miss rate of 0.042, a precision of 0.963, and an F1-score of 0.960. In addition, mAP@0.5, mAP@0.5:0.95, and mean IoU reach 0.954, 0.793, and 0.742, respectively, indicating good performance in building detection and localization. Compared with manual vectorization, the automated workflow reduces the processing time for 57 raster files from 25.4 min to 3.1 min, corresponding to an 87.8% reduction in processing time. These results indicate that the proposed method improves building recognition accuracy while enhancing the efficiency of converting recognition results into GIS vector data, showing application potential for urban spatial information extraction.