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http://dx.doi.org/10.7848/ksgpc.2021.39.3.157

Comparative evaluation of deep learning-based building extraction techniques using aerial images  

Mo, Jun Sang (Dept. of Civil Engineering, Chungbuk National University)
Seong, Seon Kyeong (Dept. of Civil Engineering, Chungbuk National University)
Choi, Jae Wan (Dept. of Civil Engineering, Chungbuk National University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.39, no.3, 2021 , pp. 157-165 More about this Journal
Abstract
Recently, as the spatial resolution of satellite and aerial images has improved, various studies using remotely sensed data with high spatial resolution have been conducted. In particular, since the building extraction is essential for creating digital thematic maps, high accuracy of building extraction result is required. In this manuscript, building extraction models were generated using SegNet, U-Net, FC-DenseNet, and HRNetV2, which are representative semantic segmentation models in deep learning techniques, and then the evaluation of building extraction results was performed. Training dataset for building extraction were generated by using aerial orthophotos including various buildings, and evaluation was conducted in three areas. First, the model performance was evaluated through the region adjacent to the training dataset. In addition, the applicability of the model was evaluated through the region different from the training dataset. As a result, the f1-score of HRNetV2 represented the best values in terms of model performance and applicability. Through this study, the possibility of creating and modifying the building layer in the digital map was confirmed.
Keywords
Deep Learning; Semantic Segmentation; Aerial Photo; Building Extraction;
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