{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:37:21Z","timestamp":1766158641691,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T00:00:00Z","timestamp":1615420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Study on the growth and shrinkage of cities in the Pearl River Delta under the new normal","award":["41571118"],"award-info":[{"award-number":["41571118"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping new and old buildings are of great significance for understanding socio-economic development in rural areas. In recent years, deep neural networks have achieved remarkable building segmentation results in high-resolution remote sensing images. However, the scarce training data and the varying geographical environments have posed challenges for scalable building segmentation. This study proposes a novel framework based on Mask R-CNN, named Histogram Thresholding Mask Region-Based Convolutional Neural Network (HTMask R-CNN), to extract new and old rural buildings even when the label is scarce. The framework adopts the result of single-object instance segmentation from the orthodox Mask R-CNN. Further, it classifies the rural buildings into new and old ones based on a dynamic grayscale threshold inferred from the result of a two-object instance segmentation task where training data is scarce. We found that the framework can extract more buildings and achieve a much higher mean Average Precision (mAP) than the orthodox Mask R-CNN model. We tested the novel framework\u2019s performance with increasing training data and found that it converged even when the training samples were limited. This framework\u2019s main contribution is to allow scalable segmentation by using significantly fewer training samples than traditional machine learning practices. That makes mapping China\u2019s new and old rural buildings viable.<\/jats:p>","DOI":"10.3390\/rs13061070","type":"journal-article","created":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T20:17:40Z","timestamp":1615493860000},"page":"1070","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["A Novel Framework Based on Mask R-CNN and Histogram Thresholding for Scalable Segmentation of New and Old Rural Buildings"],"prefix":"10.3390","volume":"13","author":[{"given":"Ying","family":"Li","sequence":"first","affiliation":[{"name":"Department of Urban and Regional Planning, School of Geography and Planning, China Regional Coordinated Development and Rural Construction Institute, Urbanization Institute, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Weipan","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Urban and Regional Planning, School of Geography and Planning, China Regional Coordinated Development and Rural Construction Institute, Urbanization Institute, Sun Yat-sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8976-3634","authenticated-orcid":false,"given":"Haohui","family":"Chen","sequence":"additional","affiliation":[{"name":"Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"}]},{"given":"Junhao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Urban and Regional Planning, School of Geography and Planning, China Regional Coordinated Development and Rural Construction Institute, Urbanization Institute, Sun Yat-sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7190-0853","authenticated-orcid":false,"given":"Xun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Urban and Regional Planning, School of Geography and Planning, China Regional Coordinated Development and Rural Construction Institute, Urbanization Institute, Sun Yat-sen University, Guangzhou 510275, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.ecolind.2018.01.006","article-title":"China\u2019s rural human settlements: Qualitative evaluation, quantitative analysis and policy implications","volume":"105","author":"Zhao","year":"2018","journal-title":"Ecol. 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