# Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images

arXiv: https://arxiv.org/abs/2102.11262 · HTML: https://mlsanity.com/paper/2102.11262/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 57 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1811.12833/) (2018)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [Gated-SCNN: Gated Shape CNNs for Semantic Segmentation](https://mlsanity.com/paper/1907.05740/) (2019)
- [PolyTransform: Deep Polygon Transformer for Instance Segmentation](https://mlsanity.com/paper/1912.02801/) (2019)
- [XingGAN for Person Image Generation](https://mlsanity.com/paper/2007.09278/) (2020)
- [Perceptual Generative Adversarial Networks for Small Object Detection](https://mlsanity.com/paper/1706.05274/) (2017)
- [Semantic Correlation Promoted Shape-Variant Context for Segmentation](https://mlsanity.com/paper/1909.02651/) (2019)
- [Adversarial Cross-Domain Action Recognition with Co-Attention](https://mlsanity.com/paper/1912.10405/) (2019)
- [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection](https://mlsanity.com/paper/1704.03414/) (2017)
- [ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors](https://mlsanity.com/paper/1904.03239/) (2019)
- [MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery](https://mlsanity.com/paper/1910.12060/) (2019)
- [GestureGAN for Hand Gesture-to-Gesture Translation in the Wild](https://mlsanity.com/paper/1808.04859/) (2018)
- [Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF)](https://mlsanity.com/paper/2002.04600/) (2020)
- [DiResNet: Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images](https://mlsanity.com/paper/2005.07232/) (2020)
- [Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States](https://mlsanity.com/paper/1805.08946/) (2018)
- [Overcoming Missing and Incomplete Modalities with Generative Adversarial Networks for Building Footprint Segmentation](https://mlsanity.com/paper/1808.03195/) (2018)
- [Building Footprint Generation Using Improved Generative Adversarial Networks](https://mlsanity.com/paper/1810.11224/) (2018)

## Built on by (most cited first, 12 of 12)

- [Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2108.06103/) (2021)
- [Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2212.05245/) (2022)
- [Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2106.15754/) (2021)
- [Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning](https://mlsanity.com/paper/2212.01261/) (2022)
- [LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing](https://mlsanity.com/paper/2404.13659/) (2024)
- [S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Images](https://mlsanity.com/paper/2502.12604/) (2025)
- [Label Noise Robust Image Representation Learning based on Supervised Variational Autoencoders in Remote Sensing](https://mlsanity.com/paper/2306.08575/) (2023)
- [Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data](https://mlsanity.com/paper/2405.05133/) (2024)
- [A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2506.19406/) (2025)
- [Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectives](https://mlsanity.com/paper/2408.01607/) (2024)
- [FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2603.07468/) (2026)
- [UniBuild: Unified Building Mapping From Multi-Source Optical Remote Sensing Imagery With Detail Decoding and Geometry Regularization](https://mlsanity.com/paper/2609.37031/) (2026)

## 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