# Building Extraction from Remote Sensing Imagery under Hazy and Low-light Conditions: Benchmark and Baseline

arXiv: https://arxiv.org/abs/2604.15088 · HTML: https://mlsanity.com/paper/2604.15088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 67 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 22 of 22)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [Global Structure-Aware Diffusion Process for Low-Light Image Enhancement](https://mlsanity.com/paper/2310.17577/) (2023)
- [DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention](https://mlsanity.com/paper/2301.04805/) (2023)
- [OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping](https://mlsanity.com/paper/2210.10732/) (2022)
- [Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2304.07039/) (2023)
- [LSKNet: A Foundation Lightweight Backbone for Remote Sensing](https://mlsanity.com/paper/2403.11735/) (2024)
- [MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery](https://mlsanity.com/paper/1910.12060/) (2019)
- [Building extraction with vision transformer](https://mlsanity.com/paper/2111.15637/) (2021)
- [Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision](https://mlsanity.com/paper/2112.04719/) (2021)
- [Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation](https://mlsanity.com/paper/2312.01220/) (2023)
- [RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models](https://mlsanity.com/paper/2507.06231/) (2025)
- [Disentangle then Parse:Night-time Semantic Segmentation with Illumination Disentanglement](https://mlsanity.com/paper/2307.09362/) (2023)
- [RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model](https://mlsanity.com/paper/2403.07564/) (2024)
- [Real-World Remote Sensing Image Dehazing: Benchmark and Baseline](https://mlsanity.com/paper/2503.17966/) (2025)
- [Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network](https://mlsanity.com/paper/2307.12309/) (2023)
- [Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2504.02647/) (2025)
- [LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery](https://mlsanity.com/paper/2406.16502/) (2024)
- [LEGNet: A Lightweight Edge-Gaussian Network for Low-Quality Remote Sensing Image Object Detection](https://mlsanity.com/paper/2503.14012/) (2025)
- [HiT: Building Mapping with Hierarchical Transformers](https://mlsanity.com/paper/2309.09643/) (2023)
- [SACANet: scene-aware class attention network for semantic segmentation of remote sensing images](https://mlsanity.com/paper/2304.11424/) (2023)

## Abstract (arXiv)

Building extraction from optical Remote Sensing (RS) imagery suffers from performance degradation under real-world hazy and low-light conditions. However, existing optical methods and benchmarks focus primarily on ideal clear-weather conditions. While SAR offers all-weather sensing, its side-looking geometry causes geometric distortions. To address these challenges, we introduce HaLoBuilding, the first optical benchmark specifically designed for building extraction under hazy and low-light conditions. By leveraging a same-scene multitemporal pairing strategy, we ensure pixel-level label alignment and high fidelity even under extreme degradation. Building upon this benchmark, we propose HaLoBuild-Net, a novel end-to-end framework for building extraction in adverse RS scenarios. At its core, we develop a Spatial-Frequency Focus Module (SFFM) to effectively mitigate meteorological interference on building features by coupling large receptive field attention with frequency-aware channel reweighting guided by stable low-frequency anchors. Additionally, a Global Multi-scale Guidance Module (GMGM) provides global semantic constraints to anchor building topologies, while a Mutual-Guided Fusion Module (MGFM) implements bidirectional semantic-spatial calibration to suppress shallow noise and sharpen weather-induced blurred boundaries. Extensive experiments demonstrate that HaLoBuild-Net significantly outperforms state-of-the-art methods and conventional cascaded restoration-segmentation paradigms on the HaLoBuilding dataset, while maintaining robust generalization on WHU, INRIA, and LoveDA datasets. The source code and datasets are publicly available at: https://github.com/AeroVILab-AHU/HaLoBuilding.