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Title	A Multiscale And Multitask Deep Learning Framework For Automatic Building Extraction
ID_Doc	2916
Authors	Yin J.; Wu F.; Qiu Y.; Li A.; Liu C.; Gong X.
Year	2022
Published	Remote Sensing, 14, 19
DOI	http://dx.doi.org/10.3390/rs14194744
Abstract	Detecting buildings, segmenting building footprints, and extracting building edges from high-resolution remote sensing images are vital in applications such as urban planning, change detection, smart cities, and map-making and updating. The tasks of building detection, footprint segmentation, and edge extraction affect each other to a certain extent. However, most previous works have focused on one of these three tasks and have lacked a multitask learning framework that can simultaneously solve the tasks of building detection, footprint segmentation and edge extraction, making it difficult to obtain smooth and complete buildings. This study proposes a novel multiscale and multitask deep learning framework to consider the dependencies among building detection, footprint segmentation, and edge extraction while completing all three tasks. In addition, a multitask feature fusion module is introduced into the deep learning framework to increase the robustness of feature extraction. A multitask loss function is also introduced to balance the training losses among the various tasks to obtain the best training results. Finally, the proposed method is applied to open-source building datasets and large-scale high-resolution remote sensing images and compared with other advanced building extraction methods. To verify the effectiveness of multitask learning, the performance of multitask learning and single-task training is compared in ablation experiments. The experimental results show that the proposed method has certain advantages over other methods and that multitask learning can effectively improve single-task performance. © 2022 by the authors.
Author Keywords	building extraction; deep learning framework; high-resolution remote sensing image; multilevel and multitask

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