Utilizing Bounding Box Annotations for Weakly Supervised Building Extraction From Remote-Sensing Images

Zheng, Daoyuan; Li, Shengwen; Fang, Fang; Zhang, Jiahui; Feng, Yuting; Wan, Bo; Liu, Yuanyuan

doi:10.1109/tgrs.2023.3271986

Cited by 5 publications

(1 citation statement)

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“…Weakly-supervised methods construct models through learning with weak supervision. In addition to pixel-level labels, weakly-supervised approaches still need weaker labels, including point labels [79], bounding boxes [80] [81], and image-level labels [82] [83]. However, weaklysupervised methods neglect the opportunity to leverage extensive unlabeled data.…”

Section: A Building Footprint Generationmentioning

confidence: 99%

A Review of Building Extraction From Remote Sensing Imagery: Geometrical Structures and Semantic Attributes

Li,

Mou,

Sun

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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In the remote sensing community, extracting buildings from remote sensing imagery has triggered great interest. While many studies have been conducted, a comprehensive review of these approaches that are applied to optical and synthetic aperture radar (SAR) imagery is still lacking. Therefore, we provide an in-depth review of both early efforts and recent advances, which are aimed at extracting geometrical structures or semantic attributes of buildings, including building footprint generation, building facade segmentation, roof segment and superstructure segmentation, building height retrieval, building type classification, building change detection, and annotation data correction. Furthermore, a list of corresponding benchmark datasets is given. Finally, challenges and outlooks of existing approaches as well as promising applications are discussed to enhance comprehension within this realm of research.

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