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Open AccessArticle

A Machine Learning-Based Classification System for Urban Built-Up Areas Using Multiple Classifiers and Data Sources

1
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 91; https://doi.org/10.3390/rs12010091
Received: 14 November 2019 / Revised: 14 December 2019 / Accepted: 18 December 2019 / Published: 25 December 2019
(This article belongs to the Special Issue Data Mining and Machine Learning in Urban Applications)
Information about urban built-up areas is important for urban planning and management. However, obtaining accurate information about urban built-up areas is a challenge. This study developed a general-purpose built-up area intelligent classification (BAIC) system that supports various types of data and classifiers. All of the steps in the BAIC were implemented using Python modules including Numpy, Pandas, matplotlib, and scikit-learn. We used the BAIC to conduct a classification experiment that involved seven types of input data; namely, Point of Interest (POI), Road Network (RN), nighttime light (NTL), a combination of POI and RN data (POI_RN), a combination of POI and NTL data (POI_NTL), a combination of RN and NTL data (RN_NTL), and a combination of POI, RN, and NTL data (POI_RN_NTL), and five classifiers, namely, Logistic Regression (LR), Decision Tree (DT), Random Forests (RF), Gradient Boosted Decision Trees (GBDT), and AdaBoost. The results show the following: (1) among the 35 combinations of the five classifiers and seven types of input data, the overall accuracy (OA) ranged from 76 to 89%, F1 values ranged from 0.73 to 0.86, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.83 to 0.95. The largest F1 value and OA were obtained using the POI_RN_NTL data and AdaBoost, while the largest AUC was obtained using POI_RN_NTL and POI_NTL data against AdaBoost, LR, and RF; and (2) the advantages of the BAIC include its support for multi-source input data, its objective accuracy assessment, and its robust classifiers. The BAIC can quickly and efficiently realize the automatic classification of urban built-up areas at a reasonably low cost and can be readily applied to other urban areas in the world where any kind of POI, RN, or NTL data coverage is available. The results of this study are expected to provide timely and effective reference information for urban planning and urban management departments, and could also potentially be used to develop large-scale maps of urban built-up areas in the future. View Full-Text
Keywords: urban built-up area; POI; NTL; road network; machine learning urban built-up area; POI; NTL; road network; machine learning
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MDPI and ACS Style

Sun, L.; Tang, L.; Shao, G.; Qiu, Q.; Lan, T.; Shao, J. A Machine Learning-Based Classification System for Urban Built-Up Areas Using Multiple Classifiers and Data Sources. Remote Sens. 2020, 12, 91.

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