Deep convolution neural network (DCNN) technology has achieved great success in extracting buildings from aerial images. However, the current mainstream algorithms are not satisfactory in feature extraction and classification of homesteads, especially in complex rural scenarios. This study proposes a deep convolutional neural network for rural homestead extraction consisting of a detail branch, a semantic branch, and a boundary branch, namely Multi-Branch Network (MBNet). Meanwhile, a multi-task joint loss function is designed to constrain the consistency of bounds and masks with their respective labels. Specifically, MBNet guarantees the details of prediction through serial 4× down-sampled high-resolution feature maps and adds a mixed-scale spatial attention module at the tail of the semantic branch to obtain multi-scale affinity features. At the same time, the low-resolution semantic feature maps and interaction between high-resolution detail feature maps are maintained. Finally, the result of semantic segmentation is refined by the point-to-point module (PTPM) through the generated boundary. Experiments on UAV high-resolution imagery in rural areas show that our method achieves better performance than other state-of-the-art models, which helps to refine the extraction of rural homesteads. This study demonstrates that MBNet is a potential candidate for building an automatic rural homestead management system.

The current research on the change of homesteads has rarely combined spatial information, and no expansion research has been found with a scale as detailed as the level of the homestead plots. Understanding the changing rules of homesteads expansion and its influencing factors are important to homestead reform, optimization of village layout, and cultivated land protection. In order to explore the direction, level, and spatial pattern of homesteads expansion from 2010 to 2020 and the influencing factors, by taking six villages in Deqing County as an example, this research proposed a tract division scheme based on multidistance spatial clustering and intersecting area index and used homesteads expansion index and geographical detector. The results showed that (1) 83.3% of the villages had a decreasing trend of homesteads expansion, and half of the villages’ homesteads remained unchanged in their distribution direction. (2) The average order of factors influencing the expansion of homesteads was: distance from road > altitude > slope > policy restriction > distance from water area > slope aspect. This study helps to understand the spatio-temporal features of homesteads expansion in small-scale areas, provides an empirical reference for deepening homesteads reform and policy formulation, and also provides new methods for quantitative research on homesteads expansion.