Deep Learning-Based Building Footprint Extraction With Missing Annotations
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Other documents of the author: kang, jian; Fernandez-Beltran, Ruben; Sun, Xian; Ni, Jingen; Plaza, Antonio
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Title
Deep Learning-Based Building Footprint Extraction With Missing AnnotationsDate
2021-04-21Publisher
Institute of Electrical and Electronics Engineers; IEEEISSN
1545-598X; 1558-0571Bibliographic 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.Type
info:eu-repo/semantics/articlePublisher version
https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859Version
info:eu-repo/semantics/acceptedVersionSubject
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 ... [+]
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. [-]
Funder Name
Ministerio de Ciencia, Innovación y Universidades (Spain) | Generalitat Valenciana | FEDER-Junta de Extremadura | European Union’s Horizon 2020 Research
Project code
RTI2018-098651-B-C54 | GV/2020/167 | GR18060 | 734541
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