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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. [ABSTRACT FROM AUTHOR]

The loss of biodiversity from urbanized areas is a major environmental problem challenging policy-makers throughout the world. Solutions to this problem are urgently required in China. We carried out a case study of wintering long-eared owls ( Asio otus) and their main prey to illustrate the negative effects of urbanization combined with ineffective conservation of biodiversity in Beijing. Field monitoring of owl numbers at two roosting sites from 2004 to 2012 showed that the owl population had fallen rapidly in metropolitan Beijing. Analysis of pellet contents identified only seven individuals of two species of shrew. The majority of mammalian prey comprised four bat and seven rodent species, making up 29.3% and 29.5% of the prey items, respectively. Prey composition varied significantly among years at the two sample sites. At the urban site the consumption of bats and rodents declined gradually over time, while predation on birds increased. In contrast, at the suburban site the prey composition showed an overall decrease in the number of bats, a sharp increase and a subsequent decrease in bird prey, and the number of rodent prey fell to a low point. Rapid development of real estate and inadequate greenfield management in city parks resulted in negative effects on the bird and small mammal habitat of urban areas in Beijing. We suggest that measures to conserve biodiversity should be integrated into future urban planning to maintain China's rich biodiversity while also achieving sustainable economic development. [ABSTRACT FROM AUTHOR]