Building Footprint Extraction

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2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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RBMatch: Dual-Level Class Rebalancing for Semi-Supervised Building Footprint Extraction
Oct 6, 2026Akil Ahmad Taki, Shaikh Anowarul FattahClass-Imbalanced Learning Remote Sensing Image Segmentation
Department of EEE, Bangladesh University of Engineering and Technology(BUET) · Department of EEE, University of Asia Pacific
LoDEOT: Low-Dimensional and Efficient Offset Tokens for Building Footprint Extraction from Off-Nadir Imagery
Oct 5, 2026Kai Li, Zigan Zhou, Zhenyang Li +7Building Footprint Extraction Remote Sensing
City University of Hong Kong · University of the Chinese Academy of Sciences · University of Hong Kong +2
UniBuild: Unified Building Mapping From Multi-Source Optical Remote Sensing Imagery With Detail Decoding and Geometry Regularization
Sep 29, 2026Wei Huang, Chenying Liu, Yilei Shi +1Remote Sensing Image Segmentation Building Footprint Extraction
Chair of Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany
A comparative assessment of global building and settlement datasets across geographic and settlement contexts
Sep 23, 2026Rufai Omowunmi Balogun, Caroline Margaux Gevaert, Capucine Riom +5Benchmark Construction Building Footprint Extraction
The World Bank, Global Facility for Disaster Reduction and Recovery · Center for Geospatial Analytics, Clark University, Worcester, MA, USA · University of Twente, Enschede, Twente, The Netherlands +2
Click2Poly: A VLM for vector mapping buildings and walls
Aug 11, 2026Nicolas Girard, Jawher Ben Abdallah, Arno Gobbin +2Vision-Language Models Building Footprint Extraction
LuxCarta
GeoAI-based post-segmentation quality validation of building footprints via spatial feature engineering
Aug 10, 2026Shah Imran Ahsan Chowdhury, Kazi Jihadur Rashid, Rajsree Das Tuli +2Building Footprint Extraction Decision Tree Learning
Department of Computer Science and Engineering Jahangirnagar University Dhaka 1342, Bangladesh · Department of Geography Florida State University Tallahassee, FL 32306, USA · Department of Geography and Environmental Sustainability The University of Oklahoma Norman, OK 73019, USA +1
Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types
Jul 22, 2026Michał Romaszewski, Kamil Drejer, Katarzyna Kołodziej +7Remote Sensing Image Segmentation Distribution Shift
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland · Space Research Centre, Polish Academy of Sciences Bartycka 18a, 00-716 Warszawa, Poland
Global Building Area Estimation Products: How Accurate Are They?
Jul 22, 2026Saad Lahrichi, Doa'a Allabadi, Kyle Bradbury +1Building Footprint Extraction Remote Sensing
University of Missouri, Department of Electrical Engineering and Computer Science, Columbia, 65201, MO, USA · Duke University, Department of Electrical and Computer Engineering, Durham, 27708, NC, USA
PCFootprint: A Large-Scale Dataset and Benchmark for Vectorized Building Footprint Extraction from Aerial LiDAR Point Clouds
Jun 18, 2026Haoyuan Shen, Kuihao Wang, Ruisheng Wang +1Remote Sensing Image Segmentation Building Footprint Extraction
School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Building and Road Recognition in Dense Urban Informal Settlements: A Dataset and Benchmark
May 28, 2026Hongyu Long, Jiaxuan Liu, Rui CaoRemote Sensing Image Segmentation Building Footprint Extraction
UGOD Thrust, HKUST(GZ)
Morphology-Guided Cross-Task Coupling for Joint Building Height and Footprint Estimation
May 6, 2026Jinzhen Han, JinByeong Lee, Jisung Kim +1Building Footprint Extraction Multi-Task Learning
Department of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, 16419, Republic of Korea · School of Geography, University of Leeds, Leeds, LS2 9JT, United Kingdom
Building Extraction from Remote Sensing Imagery under Hazy and Low-light Conditions: Benchmark and Baseline
Apr 16, 2026Feifei Sang, Wei Lu, Hongruixuan Chen +2Remote Sensing Image Segmentation Building Footprint Extraction
MOE Key Lab of ICSP, Anhui Provincial Key Lab of Multimodal Cognitive Computation, IMIS Lab of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei, China · Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8561, Japan
The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation
Mar 31, 2026Yanjiao Song, Bowen Cai, Timo Balz +4Remote Sensing Image Understanding Building Footprint Extraction
aState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, Hubei, China · bDepartment of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, East Java, Indonesia · cDepartment of Informatics and Information Technology, College of Natural and Applied Sciences (CoNAS), Sokoine University of Agriculture, Morogoro, 3038, Morogoro, Tanzania +2
The P $^3$ Dataset: Pixels, Points and Polygons for Multimodal Building Vectorization
May 21, 2025Raphael Sulzer, Liuyun Duan, Nicolas Girard +1Remote Sensing Image Segmentation Building Footprint Extraction
Université Côte d’Azur, INRIA, Sophia-Antipolis, France · LuxCarta Technology, Mouans-Sartoux, France

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RBMatch: Dual-Level Class Rebalancing for Semi-Supervised Building Footprint Extraction

Akil Ahmad Taki, Shaikh Anowarul Fattah

Department of EEE, Bangladesh University of Engineering and Technology(BUET) · Department of EEE, University of Asia Pacific

Class-Imbalanced Learning Remote Sensing Image Segmentation Building Footprint Extraction Self-Training Semantic Segmentation

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Accurate building footprint extraction from high-resolution remote sensing imagery is essential for urban planning, disaster response, and environmental monitoring. However, obtaining dense pixel-level annotations is costly, motivating the use of semi-supervised learning (SSL) to leverage unlabeled imagery. In remote sensing, severe foreground--background imbalance poses a particular challenge for self-training, as it can bias pseudo-label generation and the resulting unsupervised optimization toward the majority background class. We show that addressing this imbalance at only one stage is insufficient: balancing pseudo-label selection alone does not prevent background bias from re-emerging during unsupervised loss optimization, a failure mode we term \emph{imbalance leak}. To address this issue, we propose \textbf{RBMatch}, a dual-level class-rebalancing framework that jointly regulates pseudo-label generation and unsupervised optimization. RBMatch combines a supervised learning pathway with a self-training module comprising three components: adaptive class-specific thresholding (ACT) for balanced pseudo-label selection, confidence-aware class-balanced reweighting (CACBR) for mitigating class bias in the unsupervised loss, and distribution alignment (DAL) for matching the predicted unlabeled-data distribution to the labeled-data prior. Experiments on the WHU, INRIA, and Massachusetts building footprint datasets across labeled ratios of 1%--10% show that RBMatch consistently achieves the best building IoU and F1-score among the evaluated methods. The improvement is most pronounced on the highly imbalanced Massachusetts dataset, where RBMatch improves IoU by 1.37 points over the strongest baseline at a 1% labeling ratio and is the only method to outperform the fully supervised baseline across all twelve dataset--ratio settings.

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