Abstract
The rapid growth of major cities across the counties demands accurate building extraction techniques. Unmanned Aerial Vehicles (UAV) help in obtaining terrain information that can be used to extract urban features. Recent advancement has led to the capture of aerial images of the earth surface in micro-detail using UAV. These aerial images enable us to perform classification, feature extraction, and height estimation at a finer scale. In this work, aerial images of the university campus were captured using a quadcopter drone equipped with high-resolution camera and satellite navigation system. Approximately 500 images were captured in the study area with necessary overlap and side lap. Captured images were subjected to aerial triangulation, dense image matching, and point cloud generation to produce Digital Surface Models (DSM) and orthophoto. Various machine learning algorithms—random forest (RF), support vector machine (SVM), naïve Bayes (NB) and artificial neural networks (ANN)—have been used to extract building rooftops from derivatives of UAV-captured imageries, and accuracies were compared. Algorithms were trained using both spectral and elevation information to extract building rooftops, and improvements shown due to the addition of elevation data in training the model are observed. The proposed method is aimed at improving building-level information extraction and providing accurate building information to aid authorities for better planning and management.
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Acknowledgements
We are thankful to SERB, India, The Ministry of Science and Technology, Government of India, Ranbir and Chitra Gupta School of Infrastructure Design and Management and Sponsored research in Consultancy cell (SRIC), Indian Institute of Technology, Kharagpur, and Department of Science and Technology, West Bengal, for the financial and infrastructure support.
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Pilinja Subrahmanya, P., Haridas Aithal, B. & Mitra, S. Automatic Extraction of Buildings from UAV-Based Imagery Using Artificial Neural Networks. J Indian Soc Remote Sens 49, 681–687 (2021). https://doi.org/10.1007/s12524-020-01235-z
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DOI: https://doi.org/10.1007/s12524-020-01235-z