Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning Algorithms

Ethnic minority architecture is a vital carrier of the cultural heritage of ethnic minorities in China, and its quick and accurate extraction from remote sensing images is highly important for promoting the application of remote sensing information in urban management and architectural heritage protection. Taking Buyi architecture in China as an example, this paper proposes a minority architectural heritage identification method that combines low-altitude unmanned aerial vehicle (UAV) remote sensing technology and an improved deep learning algorithm. First, UAV images are used as the data source to provide high-resolution images for research on ethnic architecture recognition and to solve the problems associated with the high costs, time consumption, and destructiveness of traditional methods for ethnic architecture recognition. Second, to address the lack of edge pixel features in the sample images and reduce repeated labeling of the same sample, the ethnic architecture in entire remote sensing images is labeled on the Arcgis platform, and the sliding window method is used to cut the image data and the corresponding label file with a 10% overlap rate. Finally, an attention mechanism SE module is introduced to improve the DeepLabV3+ network model structure and achieve superior ethnic building recognition results. The experimental data fully show that the model’s accuracy reaches as high as 0.9831, with an excellent recall rate of 0.9743. Moreover, the F1 score is stable at a high level of 0.9787, which highlights the excellent performance of the model in terms of comprehensive evaluation indicators. Additionally, the intersection/union ratio (IoU) of the model is 0.9582, which further verifies its high precision in pixel-level recognition tasks. According to an in-depth comparative analysis, the innovative method proposed in this paper solves the problem of insufficient feature extraction of sample edge pixels and substantially reduces interference from complex environmental factors such as roads, building shadows, and vegetation with the recognition results for ethnic architecture. This breakthrough greatly improves the accuracy and robustness of the identification of architecture in low-altitude remote sensing images and provides strong technical support for the protection and intelligent analysis of architectural heritage.