A Query-Based Network for Rural Homestead Extraction from VHR Remote Sensing Images

Abstract

It is very significant for rural planning to accurately count the number and area of rural homesteads by means of automation. The development of deep learning makes it possible to achieve this goal. At present, many effective works have been conducted to extract building objects from VHR images using semantic segmentation technology, but they do not extract instance objects and do not work for densely distributed and overlapping rural homesteads. Most of the existing mainstream instance segmentation frameworks are based on the top-down structure. The model is complex and requires a large number of manually set thresholds. In order to solve the above difficult problems, we designed a simple query-based instance segmentation framework, QueryFormer, which includes an encoder and a decoder. A multi-scale deformable attention mechanism is incorporated into the encoder, resulting in significant computational savings, while also achieving effective results. In the decoder, we designed multiple groups, and used a Many-to-One label assignment method to make the image feature region be queried faster. Experiments show that our method achieves better performance (52.8AP) than the other most advanced models (+0.8AP) in the task of extracting rural homesteads in dense regions. This study shows that query-based instance segmentation framework has strong application potential in remote sensing images.

MAD-UNet: A Multi-Region UAV Remote Sensing Network for Rural Building Extraction

“…Compared to urban areas, rural buildings in different regions exhibit diverse backgrounds, and building structures and layouts can vary significantly due to differences in geographical landscapes and cultural practices. This variation presents challenges in automatic building extraction [ 23 , 24 ] and may lead to issues such as omissions, incorrect extractions, and unclear boundaries between buildings [ 25 ]. To address these issues and challenges, this study proposes an improved network based on U-Net, the Multi-attention-Detail U-shaped Network (MAD-UNet), which integrates multi-scale contextual information with detail feature enhancement through the design of Multi-scale Fusion Modules and Detail Feature Extraction Modules (DFEM).…”

Advancing Rural Building Extraction via Diverse Dataset Construction and Model Innovation with Attention and Context Learning

“…Accurate extraction of urban buildings is significant for urban planning, disaster assessment, building area estimation, 3D urban modeling, and so on [1][2][3][4]. Compared with urban buildings, rural buildings have their own characteristics: rural buildings have smaller scales, lower floors, scattered distributions in villages, and higher dispersion degrees; second, building materials, designs and construction times vary greatly, leading to larger internal differences [5]. Therefore, extracting rural buildings is more challenging, and related research is relatively scarce.…”

Influence of the Kinship Networks on Farmers’ Willingness to Revitalize Idle Houses

“…The first aspect is the study of kinship networks theory [31][32][33][34]. Scholars at home and abroad attach great importance social networks, but kinship networks, as a manifestation of these social networks, are yet to have a unified concept [35][36][37].…”