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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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 40175 - 13
Hlavní autori:	Li, Yuan, Zhong, Xia, Liu, Hua, Liao, Ming, Lu, Yue-feng, Liu, Bo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 17.11.2025 Nature Publishing Group Nature Portfolio
Predmet:	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
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Abstract	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.
AbstractList	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. Abstract 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. 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.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.
ArticleNumber	40175
Author	Li, Yuan Liu, Hua Liu, Bo Lu, Yue-feng Liao, Ming Zhong, Xia
Author_xml	– sequence: 1 givenname: Yuan surname: Li fullname: Li, Yuan organization: School of Surveying and Geoinformation Engineering, East China University of Technology – sequence: 2 givenname: Xia surname: Zhong fullname: Zhong, Xia organization: School of Surveying and Geoinformation Engineering, East China University of Technology – sequence: 3 givenname: Hua surname: Liu fullname: Liu, Hua organization: School of Surveying and Geoinformation Engineering, East China University of Technology – sequence: 4 givenname: Ming surname: Liao fullname: Liao, Ming organization: Jiangxi Provincial Natural Resources Cause Development Center – sequence: 5 givenname: Yue-feng surname: Lu fullname: Lu, Yue-feng organization: School of Civil Engineering and Geomatics, Shandong University of Technology – sequence: 6 givenname: Bo surname: Liu fullname: Liu, Bo email: liubo@ecut.edu.cn organization: School of Surveying and Geoinformation Engineering, East China University of Technology
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Cites_doi	10.1016/j.jksus.2022.101887 10.1117/12.714998 10.1201/9781315140919 10.3390/s20102918 10.3390/rs13091801 10.1007/978-3-540-31865-1_25 10.3390/land11081212 10.1016/0031-3203(86)90030-0 10.5194/isprs-archives-XLII-3-79-2018 10.1016/j.rse.2021.112515 10.1002/9781118358887.ch1 10.1080/014311697218485 10.1007/s11042-022-13644-y 10.3390/ijerph191912198 10.1080/15481603.2014.939539 10.3390/rs9030236 10.1109/LGRS.2018.2830797 10.1016/j.scib.2021.01.012 10.1109/ACCESS.2019.2903127 10.11834/jrs.20211018 10.1038/s41598-024-55214-2 10.5194/essd-2021-7 10.1371/journal.pone.0198189 10.3390/rs12030541 10.3390/rs15123021 10.1038/s41598-024-84925-9 10.3390/rs11212516 10.1016/j.jclepro.2021.129488 10.1111/1467-9671.00058
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Snippet	Accurate identification of urban built-up areas is crucial for monitoring urbanization and promoting sustainable development. To overcome the limitations of... Abstract Accurate identification of urban built-up areas is crucial for monitoring urbanization and promoting sustainable development. To overcome the...
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SubjectTerms	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
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Title	Urban built-up area extraction using triangle threshold algorithm and Naive Bayes classification model with multidata fusion
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