mlsanityData to 7 Oct 2026

Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images

arXiv 2102.11262 ↗First posted 22 Feb 2021cs.CVcited by 12

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Abstract (arXiv)

Building extraction in VHR RSIs remains a challenging task due to occlusion and boundary ambiguity problems. Although conventional convolutional neural networks (CNNs) based methods are capable of exploiting local texture and context information, they fail to capture the shape patterns of buildings, which is a necessary constraint in the human recognition. To address this issue, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns that improve the accuracy of building segmentation. In the proposed ASLNet, we introduce the adversarial learning strategy to explicitly model the shape constraints, as well as a CNN shape regularizer to strengthen the embedding of shape features. To assess the geometric accuracy of building segmentation results, we introduced several object-based quality assessment metrics. Experiments on two open benchmark datasets show that the proposed ASLNet improves both the pixel-based accuracy and the object-based quality measurements by a large margin. The code is available at: https://github.com/ggsDing/ASLNet

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 28 of its 57 references that are in the map. First posted on arXiv 22 Feb 2021.

Builds on

28 of 57 references.

References · 28

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16 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 3; 2025: 2; 2026: 2.

12 papers.

  1. 2021Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Imagescited by 43
  2. 2022Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Imagescited by 43
  3. 2021Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Imagescited by 27
  4. 2022Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learningcited by 5
  5. 2024LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensingcited by 4
  6. 2025S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Imagescited by 2
  7. 2023Label Noise Robust Image Representation Learning based on Supervised Variational Autoencoders in Remote Sensingcited by 1
  8. 2024Identifying every building's function in large-scale urban areas with multi-modality remote-sensing datacited by 1
  9. 2025A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentationcited by 1
  10. 2024Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectivescited by 0
  11. 2026FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentationcited by 0
  12. 2026UniBuild: Unified Building Mapping From Multi-Source Optical Remote Sensing Imagery With Detail Decoding and Geometry Regularizationcited by 0