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Article type: Research Article
Authors: Rong, Guo; * | Xi, Wang
Affiliations: Guangdong Baiyun University, Faculty of Architectural Engineering, Guangzhou, Guangdong, China
Correspondence: [*] Corresponding author. Guo Rong, E-mail: guo.rong98741qq@hotmail.com.
Abstract: The recognition and regulation of buildings are essential aspects of urban management to prevent illegal constructions and maintain public safety and resources. Traditional machine learning methods for building recognition often suffer from low accuracy and weak generalization capabilities due to their reliance on manually designed features. Traditional machine learning methods for building recognition often suffer from low accuracy and weak generalization capabilities due to their reliance on manually designed features. Therefore, the study of automatic, accurate building identification method is very necessary. Based on this, Introducing advanced algorithms like Faster R-CNN and DRNet signifies a significant step towards automating accurate building identification. The utilization of Faster R-CNN as a basic training model combined with DRNet demonstrates promising results in accurately recognizing buildings. The experimental analysis highlights the potential of the proposed method, achieving an impressive 82.1% mean Average Precision (mAP) for landmark buildings. Accurate prediction of building coordinates further strengthens the effectiveness of the proposed approach. Comparative analysis showcases the superiority of the proposed model in recognizing buildings not only in normal images but also in complex environmental settings. The successful implementation of advanced algorithms in building recognition contributes to more efficient urban management and development. Continued research in automatic building identification methods is crucial for addressing challenges in urban planning and management, ensuring sustainable city development.
Keywords: Deep learning, Faster R-CNN, building identification, classification algorithm, building extraction, urbanization
DOI: 10.3233/JIFS-241838
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
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