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Performance evaluation of three deep learning models in building footprint extraction from aerial and satellite images
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Nima Ahmadian *   , Amin Sedaghat , Nazila Mohammadi |
| University of Tabriz |
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Abstract: (2634 Views) |
Buildings as one of the important man-made objects have various applications and need to be observed with aerial and satellite images. Deep learning models have often been used to automatically extract building footprints from aerial and satellite images. It is essential to evaluate and compare the features of different deep learning models in images with geometric and brightness variations. For this purpose, in this research the performance of three deep learning models called Mask-RCNN (Mask Region-based Convolutional Neural Network), U-Net and MA-FCN (Multi-scale Aggregation Fully Convolutional Network) is evaluated on two aerial and satellite datasets with F1-score and IOU metrics. The results of this research indicate that the model, quantity and quality of training samples and digital surface model affect the performance of these models. Also, using digital surface models alongside the 3 band RGB images is an effective way of improving the building footprint extraction with deep learning models. By using digital surface model, the IOU results of U-Net and MA-FCN models in building footprint extraction are increased 7.46% and 5.7% in satellite dataset and 3.61% and 3.34% in aerial dataset, respectively. U-Net and MA-FCN are more precise in building boundaries since they concatenate feature maps of encoder and decoder parts in producing final segmentation maps. Mask-RCNN is stable to overfitting because of using ResNet in its architecture.
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| Keywords: Deep Learning, Buildings, Digital Surface Model, Satellite Imagery, U-Net |
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Full-Text [PDF 1345 kb]
(893 Downloads)
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Type of Study: Research |
Subject:
RS
Received: 2023/05/1 | Accepted: 2023/06/14 | ePublished ahead of print: 2023/06/21 | Published: 2023/07/9
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