Traditional Village Building Extraction Based on Improved Mask R-CNN: A Case Study of Beijing, China

Abstract

As an essential material carrier of cultural heritage, the accurate identification and effective monitoring of buildings in traditional Chinese villages are of great significance to the sustainable development of villages. However, along with rapid urbanization in recent years, many towns have experienced problems such as private construction, hollowing out, and land abuse, destroying the traditional appearance of villages. This study combines deep learning technology and UAV remote sensing to propose a high-precision extraction method for conventional village architecture. Firstly, this study constructs the first sample database of traditional village architecture based on UAV remote sensing orthophotos of eight representative villages in Beijing, combined with fine classification; secondly, in the face of the diversity and complexity of the built environment in traditional villages, we use the Mask R-CNN instance segmentation model as the basis and Path Aggregate Feature Pyramid Network (PAFPN) and Atlas Space Pyramid Pool (ASPP) as the main strategies to enhance the backbone model for multi-scale feature extraction and fusion, using data increment and migration learning as auxiliary means to overcome the shortage of labeled data. The results showed that some categories could achieve more than 91% accuracy, with average precision, recall, F1-score, and Intersection over Union (IoU) values reaching 71.3% (+7.8%), 81.9% (+4.6%), 75.7% (+6.0%), and 69.4% (+8.5%), respectively. The application practice in Hexi village shows that the method has good generalization ability and robustness, and has good application prospects for future traditional village conservation.