The countryside is the principal area of population agglomeration with a high incidence of global poverty problems. As a shelter for the daily life of rural inhabitants, the rural buildings constitute the element of rural settlements. Moreover, they can directly characterize the level of rural development. Therefore, in the new stage of the Sustainable Development Goals (SDGs), this study selected Laos as the main study area and investigated the effect of different factors on the spatial heterogeneity of rural development based on the rural building spatial database. With the geodetector, the results are summarized as follows: (1) The spatial pattern of rural buildings in different regions of Laos varies significantly, with hot spots areas of rural buildings mainly located in the central and southern regions, while cold spots areas are mainly concentrated in the northern region. (2) Slope, transport infrastructure, and public service are the dominant elements influencing the spatial differentiation of rural buildings in Laos, but spatial heterogeneity existed in different regions of factors. (3) The interaction detector shows that slope ∩ road is the dominant interaction factor influencing the spatial distribution pattern of rural buildings nationwide, and there are marked divergences in the interaction factors. Finally, this study combines the findings to propose corresponding countermeasures for promoting the development and construction of rural areas in Laos.

The research on the distribution of rural buildings is one of the fundamental works of urban–rural development in Vietnam. Adopting a Mask R-CNN deep learning framework and collecting sub-meter remote sensing images, this research used a remote sensing interpretation model of rural buildings trained based on East Asian characteristics of rural buildings and successfully recognized about 2.87 million rural buildings in 34 Vietnamese provincial administrative districts with a total area of rural buildings of 2492 million square meters. The reliability of the identification results was verified by manual detection and quantitative statistics, and a multi-scale database of rural buildings in Vietnam based on individual rural buildings was created. Based on the database, this paper analyzes the distribution characteristics of rural buildings and summarizes characteristics of rural building distribution at the country, regional, and provincial scales. The identification results lay the foundation for the next study of urban–rural relations in Southeast Asia and the construction of a basic database on villages.

The research on the distribution of rural buildings is one of the fundamental works of urban-rural development in Vietnam. Adopting Mask R-CNN deep learning framework and collecting sub-meter remote sensing images, this research used a remote sensing interpretation model of rural buildings trained based on East Asian characteristics of rural buildings and successfully recognized about 2.87 million rural buildings in 34 Vietnamese provincial administrative districts with a total area of rural buildings of 2,492 million square meters. The reliability of the identification results was verified by manual detection and quantitative statistics, and a multi-scale database of rural buildings in Vietnam based on individual rural buildings was created. Based on the database, this paper analyzes the distribution characteristics of rural buildings and summarizes characteristics of rural buildings distribution at the country, regional, and provincial scales. The identification results lay the foundation for the next study of urban-rural relations in Southeast Asia and the construction of a basic database on villages.