Urban built-up area extraction using triangle threshold algorithm and Naive Bayes classification model with multidata fusion

Accurate identification of urban built-up areas is crucial for monitoring urbanization and promoting sustainable development. To overcome the limitations of single-data-source methods in capturing human activities, this study proposes a novel approach for urban built-up area extraction, based on mac...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 40175 - 13
Hauptverfasser:	Li, Yuan, Zhong, Xia, Liu, Hua, Liao, Ming, Lu, Yue-feng, Liu, Bo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 17.11.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/705 704/172 704/844 Accuracy Algorithms Built-up area extraction Multidata Fusion Cities Datasets Deep learning Distribution patterns Humanities and Social Sciences Land use planning Landsat Light Machine learning multidisciplinary Naive-Bayes classification model Nighttime Overflow Population distribution Remote sensing Science Science (multidisciplinary) Sensors Support vector machines Sustainable development Triangular threshold segmentation algorithm Urban areas Urban environments Urbanization Yangtze River Triangular threshold segmentation algorithm Naive-Bayes classification model Built-up area extraction Multidata Fusion
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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