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.

Habitat degradation has become a critical bottleneck that restricts watershed ecological security and sustainable development. This study aims to reveal the spatiotemporal evolution of habitats in the Chishui River Basin, in order to provide scientific guidance for regional ecological conservation and restoration. Based on land use datasets from 1990 to 2023 in the basin, the InVEST-PLUS model is adopted to quantify habitat quality and conduct exploratory multiscenario simulations of future habitat conditions across the study area. The main results are as follows: (1) The average habitat quality index of the Chishui River Basin tends to decrease, with the area of high-grade habitat decreasing by 50.30%. The basin has undergone four distinct phases: high-intensity degradation, continuous but low-intensity structural change, large-scale ecological restoration, and active bidirectional adjustment. (2) Spatial pattern of habitat quality is strongly heterogeneous. Specifically, high-quality habitats are primarily distributed in the northern region of Chishui River Basin, while degraded habitats are concentrated in the central and southeastern parts of the basin, with an expanding trend. Habitat changes are characterized by leapfrog transfer and path dependence. Notably, low-grade habitats show an extremely high persistence rate of 97.16%. (3) Simulation outputs under diverse scenarios show that ecological protection serves as an effective strategy for improving habitat quality within the basin. Habitat quality of the Chishui River Basin has displayed a phased evolutionary pattern of initial degradation followed by recovery. Strengthening ecological protection will be the core approach to ameliorate watershed habitat quality and safeguard ecological security in the future. The study provides a scientific reference for watershed ecological conservation and restoration, territorial spatial optimization, and the construction of ecological barriers in the upper reaches of the Yangtze River.