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

Yuan Li¹, Xia Zhong¹, Hua Liu¹

¹School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang, 330013, China.

Scientific Reports

|November 17, 2025

Summary

This study introduces a new machine learning method using POI, nighttime light, and satellite data to accurately map urban built-up areas. The approach improves urbanization monitoring and sustainable development planning.

Keywords:

Built-up area extraction Multidata Fusion Naive-Bayes classification model Triangular threshold segmentation algorithm

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Area of Science:

Remote Sensing
Urban Planning
Geographic Information Systems (GIS)

Background:

Accurate urban built-up area identification is vital for sustainable development and urbanization monitoring.
Single-data-source methods struggle to capture complex human activities in urban environments.
Limitations exist in current remote sensing techniques for precise urban mapping.

Purpose of the Study:

To develop a novel, high-precision urban built-up area extraction method.
To overcome limitations of single-source data by employing multi-source data fusion.
To enhance the accuracy of urban mapping in complex environments.

Main Methods:

Constructed the POI-Adjusted Luojia-1 Urban Index (PALUI) using POI and Luojia-1 nighttime light data.
Segmented remote sensing indices (NDVI, MNDWI, NDBI) via triangle threshold algorithm.
Employed a Naive Bayes classifier for multi-source data fusion and built-up area extraction.

Main Results:

The proposed method achieved a precision of 0.89 and recall of 0.76 in Nanchang City.
Outperformed existing methods including SVM, Random Forest, U-net, and YOLO11.
The PALUI index mitigated nighttime light overflow, and fusion improved edge accuracy.

Conclusions:

The multi-source fusion approach provides a reliable solution for high-precision urban mapping.
This method effectively quantifies population distribution and differentiates urban features.
Offers improved capabilities for urbanization monitoring and sustainable development strategies.