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Deep Learning-Based Building Footprint Extraction With Missing Annotations
dc.contributor.author | kang, jian | |
dc.contributor.author | Fernandez-Beltran, Ruben | |
dc.contributor.author | Sun, Xian | |
dc.contributor.author | Ni, Jingen | |
dc.contributor.author | Plaza, Antonio | |
dc.date.accessioned | 2021-10-07T11:12:25Z | |
dc.date.available | 2021-10-07T11:12:25Z | |
dc.date.issued | 2021-04-21 | |
dc.identifier.citation | J. Kang, R. Fernandez-Beltran, X. Sun, J. Ni and A. Plaza, "Deep Learning-Based Building Footprint Extraction With Missing Annotations," in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2021.3072589. | ca_CA |
dc.identifier.issn | 1545-598X | |
dc.identifier.issn | 1558-0571 | |
dc.identifier.uri | http://hdl.handle.net/10234/194927 | |
dc.description.abstract | Most state-of-the-art deep learning-based methods for extraction of building footprints are aimed at designing proper convolutional neural network (CNN) architectures or loss functions able to effectively predict building masks from remote sensing (RS) images. To properly train such CNN models, large-scale and pixel-level building annotations are required. One common approach to obtain scalable benchmark data sets for the segmentation of buildings is to register RS images with auxiliary geospatial information data, such as those available from OpenStreetMaps (OSM). However, due to land-cover changes, urban construction, and delayed geospatial information updating, some building annotations may be missing in the corresponding ground-truth building mask layers. This will likely introduce confusion in the training of CNN models for discriminating between background and building pixels. To solve this important issue, we first formulate the problem as a long-tailed classification one. Then, we introduce a new joint loss function based on three terms: 1) logit adjusted cross entropy (LACE) loss, aimed at discriminating between building and background pixels from a long-tailed label distribution; 2) weighted dice loss, aimed at increasing the F₁ scores of the predicted building masks; and 3) boundary (BD) alignment loss, which is optimized for preserving the fine-grained structure of building boundaries. Our experiments, conducted on two benchmark building segmentation data sets, validate the effectiveness of our newly proposed loss with respect to other state-of-the-art losses commonly used for extracting building footprints. The codes of this letter will be publicly available from https://github.com/jiankang1991/GRSL_BFE_MA. | ca_CA |
dc.format.extent | 12 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Institute of Electrical and Electronics Engineers | ca_CA |
dc.publisher | IEEE | ca_CA |
dc.rights | © Copyright 2021 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | buildings | ca_CA |
dc.subject | annotations | ca_CA |
dc.subject | data mining | ca_CA |
dc.subject | benchmark testing | ca_CA |
dc.subject | training | ca_CA |
dc.subject | feature extraction | ca_CA |
dc.subject | image segmentation | ca_CA |
dc.subject | building extraction | ca_CA |
dc.subject | deep learning | ca_CA |
dc.subject | missing labels | ca_CA |
dc.subject | remote sensing (RS) | ca_CA |
dc.subject | semantic segmentation | ca_CA |
dc.title | Deep Learning-Based Building Footprint Extraction With Missing Annotations | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1109/LGRS.2021.3072589 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859 | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
project.funder.name | Ministerio de Ciencia, Innovación y Universidades (Spain) | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
project.funder.name | FEDER-Junta de Extremadura | ca_CA |
project.funder.name | European Union’s Horizon 2020 Research | ca_CA |
oaire.awardNumber | RTI2018-098651-B-C54 | ca_CA |
oaire.awardNumber | GV/2020/167 | ca_CA |
oaire.awardNumber | GR18060 | ca_CA |
oaire.awardNumber | 734541 | ca_CA |
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