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

2026-04-16

AI Summary

This study addresses the significant performance degradation of building extraction from optical remote sensing imagery under hazy and low-light conditions. To this end, the authors introduce HaLoBuilding—the first benchmark dataset specifically designed for such adverse imaging scenarios—and propose HaLoBuild-Net, an end-to-end network featuring three key innovations: spatial-frequency focusing, global multi-scale guidance, and mutual-guided fusion. These modules effectively suppress atmospheric interference and enhance boundary delineation. The approach further leverages multi-temporal image pairs from the same scene to ensure label alignment and incorporates large-receptive-field attention, frequency-domain channel reweighting, and bidirectional semantic-spatial calibration mechanisms. Experiments demonstrate that HaLoBuild-Net substantially outperforms existing methods and cascaded paradigms on HaLoBuilding, while also exhibiting strong generalization across diverse datasets, including WHU, INRIA, and LoveDA.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• building extraction
• hazy conditions
• low-light conditions
• remote sensing imagery
• adverse weather

Innovation

• building extraction
• hazy and low-light conditions
• spatial-frequency fusion
• end-to-end framework
• remote sensing benchmark