Building extraction from remote sensing images with deep learning in a supervised manner

Kaiqiang Chen, Kun Fu, Xin Gao, Menglong Yan, Xian Sun, Huan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations


Building extraction from remote sensing images is a longstanding topic in land use analysis and applications of remote sensing. Variations in shape and appearance of buildings, occlusions and other unpredictable factors increase the hardness of automatic building extraction. Numerous methods have been proposed during the last several decays, but most of these works are task oriented and lack of generalization. This paper applys deep learning to building extraction in a supervised manner. A deep deconvolution neural network with 27 Convolution/Deconvolution weight layers is designed to realize building extraction in pixel level. As such a deep network is prone to overfitting, a data augment method that suits pixel-wise prediction tasks in remote sensing is suggested. Moreover, an overall training and inferencing architecture is proposed. Our methods are finally applied to building extraction tasks and get competitive results with other methods published.
Original languageEnglish (US)
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)9781509049516
StatePublished - Dec 1 2017
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-21


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