Traditional villages are important carriers of cultural heritage, and the quantitative study of their spatial characteristics is an important approach to their preservation. However, the rapid extraction, statistics, and estimation of the rich spatial characteristic indicators in these villages have become bottlenecks in traditional village research. This paper employs UAV (unmanned aerial vehicle) and handheld laser scanners to acquire three-dimensional point cloud data and construct a spatial feature three-dimensional calculation workflow of “field data collection—data processing—data analysis and application”, which enables the rapid acquisition, processing, and analysis of three-dimensional village data. Typical case studies are conducted in Liukeng Village, China, focusing on the quantification of village spatial characteristics at three levels: topography, streets and alleys, and individual buildings, as well as comparative studies of multiple villages across different regions. The quantification of three-dimensional data reveals the regularity of village spatial characteristics and uncovers the spatial wisdom embedded in the site selection and spatial structure of traditional villages. This paper establishes a complete technical route for the quantitative analysis of villages, deepens public understanding of the diverse value of traditional villages, and provides technical support for research and practice in related fields.

Urban disaster risks are escalating, and university campus emergency shelters (UCESs) are key to alleviating the supply–demand imbalance in emergency shelter services (ESSs) within high-density central urban areas. However, existing studies lacked the measurement of UCES service effectiveness from a regional supply–demand perspective, limiting the ability to guide planning practices. Therefore, we focused on the capacity of UCESs to improve regional supply–demand relationships and developed a service effectiveness evaluation framework for UCESs in the central urban area of Tianjin under an earthquake scenario. We identified emergency shelter spaces within the campuses and developed a campus–city collaborative shelter capacity model to determine their service supply capacity. Then we quantified regional service demand driven by seismic risk. Finally, we quantified the service effectiveness of each UCES by constructing a service effectiveness evaluation model. Results showed that (1) the total shelter capacity and service coverage of 13 UCESs accounted for approximately 32.1% of the central district’s population and 67.5% of its land area, indicating their strong potential to provide large-scale ESSs. (2) Average seismic risk values ranged from 0.200 to 0.260, exhibiting the characteristic of being higher in the south and lower in the north. (3) Service effectiveness was classified into three levels—higher (1.150–1.257), medium (0.957–0.988), and lower (0.842–0.932)—corresponding to planning interventions that can be implemented based on them. This study aims to reveal differences between different UCESs to improve regional supply–demand relationships by evaluating their service effectiveness and supporting refined emergency management and planning decisions.

Dimensional data are critical for the assessment, conservation, and restoration of architectural heritage. Traditional manual measurement methods are time-consuming and labor-intensive, particularly for large, complex, and difficult-to-access structures such as the hollow watchtowers of the Ming Great Wall. This study proposes a new method that combines low-altitude UAV photogrammetry with point cloud semantic segmentation algorithms, using watchtowers along the Ming Great Wall as case studies. First, images were collected using low-altitude UAV, and point cloud data are generated via photogrammetry to capture information of the difficult-to-reach watchtowers located on mountain ridges. Second, a semantic segmentation algorithm was applied to classify different components of the watchtower, and dimensional data were automatically calculated by fitting geometric models. Finally, experimental results showed that this automated method significantly outperforms traditional techniques in both efficiency and accuracy. The overall accuracy of the point cloud semantic segmentation algorithm reaches 90.80, and the error in automatically calculating the length and width of the watchtowers is less than 10%. Through the dimensional analysis of 601 watchtowers, the study identifies dimensional differences among watchtowers under three military jurisdictions during the Ming Dynasty. In summary, this study develops an automatic dimensional analysis method for watchtowers of the Ming Great Wall, improving the efficiency of heritage surveys. The method also shows potential for extension to other large-scale architectural heritage, offering a valuable tool for rapid analysis and digital archiving.