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RegGAN: An End-to-End Network for Building Footprint Generation with Boundary Regularization

Accurate and reliable building footprint maps are of great interest in many applications, e.g., urban monitoring, 3D building modeling, and geographical database updating. When compared to traditional methods, the deep-learning-based semantic segmentation networks have largely boosted the performanc...

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Main Authors: Qingyu Li, Stefano Zorzi, Yilei Shi, Friedrich Fraundorfer, Xiao Xiang Zhu
Format: Artigo
Language:Inglês
Published: MDPI AG 2022-04-01
Series:Remote Sensing
Subjects:
building footprint
semantic segmentation
generative adversarial network
regularization
Online Access:https://www.mdpi.com/2072-4292/14/8/1835
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