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
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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
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Zusammenfassung: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 machine learning techniques, and multi-source data fusion (POI, Luojia-1 nighttime light, and Landsat 8 imagery). First, we construct the POI-Adjusted Luojia-1 Urban Index (PALUI) by combining POI and Luojia-1nighttime light data quantify population distribution patterns. Next, we segment remote sensing indices (NDVI, MNDWI, NDBI) using the triangle threshold algorithm to differentiate vegetation, water, and built-up areas. Finally, a Naive Bayes classifier is used to fuse these features for built-up area extraction. Experiments in Nanchang City show that the proposed method achieves a precision of 0.89 and recall of 0.76, outperforming SVM, Random Forest, U-net, and YOLO11. The PALUI index effectively reduces light overflow issues in nighttime data, while the multi-source fusion strategy improves edge accuracy in complex urban environments. This method offers a reliable solution for high-precision urban mapping.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-23986-w