Content area
It is confirmed that high-spatial-resolution remote sensing images can achieve high-precision estimation of rural homestead utilization rate and calculation of vacancy rate via semantic segmentation methods. A high-precision extraction algorithm framework suitable for rural homesteads in regularly shaped areas is proposed.
It provides a feasible technical approach for the rapid and accurate acquisition of rural homestead spatial information, breaking through the limitation of low efficiency in traditional manual surveys. The proposed algorithm framework can offer key technical support and data references for rural planning, homestead management, and optimal allocation of land resources. In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. To this end, we implement the RS3Mamba+ deep learning model, which introduces the Mamba state space model (SSM) into its auxiliary branching—leveraging Mamba’s sequence modeling advantage to efficiently capture long-range spatial correlations of rural compounds, a critical capability for analyzing sparse rural buildings. This Mamba-assisted branch, combined with multi-directional selective scanning (SS2D) and the enhanced STEM network framework (replacing single 7 × 7 convolution with two-stage 3 × 3 convolutions to reduce information loss), works synergistically with a ResNet-based main branch for local feature extraction. We further introduce a multiscale attention feature fusion mechanism that optimizes feature extraction and fusion, enhances edge contour extraction accuracy in courtyards, and improves the recognition and differentiation of courtyards from regions with complex textures. The feature information of courtyard utilization status is finally extracted using empirical methods. A typical rural area in Weifang City, Shandong Province, is selected as the experimental sample area. Results show that the extraction accuracy reaches an average intersection over union (mIoU) of 79.64% and a Kappa coefficient of 0.7889, improving the F1 score by at least 8.12% and mIoU by 4.83% compared with models such as DeepLabv3+ and Transformer. The algorithm’s efficacy in mitigating false alarms triggered by shadows and intricate textures is particularly salient, underscoring its potential as a potent instrument for the extraction of rural vacancy rates.
Details
Accuracy;
Texture recognition;
Deep learning;
Image resolution;
Algorithms;
Satellite imagery;
Spatial dependencies;
State space models;
Rural areas;
Remote sensing;
Data processing;
Unmanned aerial vehicles;
Image processing;
Semantic segmentation;
Machine learning;
Spatial data;
Image segmentation;
False alarms;
Courtyards;
Neural networks;
Optimization;
High resolution;
Classification;
Information processing;
Utilization;
Land resources;
Semantics;
Towns
; Ge Yunjian 2 1 School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; [email protected] (X.F.); [email protected] (S.X.)
2 School of Geography, Nanjing University of Information Science and Technology, Nanjing 210044, China; [email protected]