Remote Sensing and LiDAR Applications

Neueste Paper

• A Two-Stage Cascade for Near-Real-Time Forest Anomaly Detection from Sentinel-1 SAR Time Series

  Pann Thinzar Seint, Subas Chhatkuli, Bryan Atwood · 5. Oktober 2026

  Tropical forest monitoring is essential for global climate stability and biodiversity preservation. To address the urgent need for rapid, reliable detection of forest loss which is essential for timely intervention against illegal logging, supply chain transparency, land-use governance and carbon ma…

• ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation

  Zhihao Zhan, Le Tao, Yifei Tian, Xin Liu, Jie Yuan · 5. Oktober 2026

  Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structures, severe occlusions, density variations, and ambiguous instance boundaries. Recent query-based forest segmentation methods have shown promise for uni…

• Visibility on Terrains

  Laura Toma · 2. Oktober 2026

  This chapter surveys terrain visibility models, algorithms, and applications. It introduces basic definitions, from line-of-sight, single-viewpoint viewsheds to cumulative and total viewsheds, on both Triangulated Irregular Network (TIN) and regular grid terrains. Reviews exact and approximate algor…

• Pixel-Level Transformers in Remote Sensing: A Canopy Height Case Study

  Sven Ligensa, Jan Pauls, Karsten Schr\"odter, Ibrahim Fayad, Fabian Gieseke · 30. September 2026

  Predicting canopy height from medium-resolution satellite imagery is a common and scalable approach for assessing the condition of the world's forests, which play a crucial role in climate change mitigation. While Transformer-based architectures have shown strong performance in many domains, their s…

• Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning

  Antoine Lorentz, St\'ephane May, Valentine Bellet, Dawa Derksen, Bastien Nespoulous · 28. September 2026

  Large-scale Digital Surface Models (DSMs) can be produced cost-effectively from satellite images via stereo-photogrammetry. However, the resulting 3D maps are often contaminated by noise, outliers, and voids. On the other hand, aerial LiDAR provides high-accuracy elevation measurements at a substant…

• Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision

  Zekai Shi, Meng Zhang, Haokun Zhang, Bo Zhang · 25. September 2026

  High-resolution digital elevation models (DEMs) support Earth observation applications, but paired training references are often available only at coarser output resolutions. Reconstructing finer terrain grids therefore requires both effective transfer beyond the supervised scale and control of dens…

• Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series

  Foivos I. Diakogiannis, Zheng-Shu Zhou, Jeff Wang, Gonzalo Mata, Dave Henry, Roger Lawes, Amy Parker, Peter Caccetta, Suzanne Furby, Rodrigo Ibata, Ondrej Hlinka, Jonathan Richetti, Kathryn Batchelor, Chris Herrmann, Andrew Toovey, John Taylor · 24. September 2026

  Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing methodologies often face significant challenges, particularly in their reliance on extensive manual efforts for cloud-free data curation and limited adaptabili…

• Geospatial embeddings detect old-growth forests but buffered spatial validation narrows their advantage over Sentinel features

  Thomas Ratsakatika (Department of Geography, University of Cambridge, Cambridge, UK), Mihai Zotta (Fundatia Conservation Carpathia, Brasov, Romania), Srinivasan Keshav (Department of Computer Science and Technology, University of Cambridge, Cambridge, UK), Emily R. Lines (Department of Geography, University of Cambridge, Cambridge, UK) · 24. September 2026

  Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, protecting them requires mapping that is accurate for individual forest parcels yet deployable continent-wide. Geospatial foundation model (GFM) embeddi…

• Learning-Based 3D Reconstruction of Power Networks from Aerial Point Clouds

  Rishabh Jain, Anuja Saini, Vishal Jain · 22. September 2026

  This paper presents an end-to-end framework for reconstructing overhead power utility network topology and extracting span-level physical metadata from large-scale aerial LiDAR. The pipeline begins with semantic segmentation of the input point cloud using an improved KPConv-based model, in which dat…

• Toward a foundation model for forest point clouds

  Yuanwen Yue, Stefano Puliti, Damien Robert, Atakan Topalo\u{g}lu, Binbin Xiang, Maciej Wielgosz, Jan Dirk Wegner, Rasmus Astrup, Christian Rupprecht, Konrad Schindler · 22. September 2026

  Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current models are typically specialized to a single task, sensor, and forest type, making adaptation expensive in terms of annotations, computation, and expertis…

• M3GA-Wild: A Large-Scale Dataset and Benchmark for Multi-Modal Multi-session Ground-to-Aerial Place Recognition in Forests

  Ethan Griffiths, Maryam Haghighat, Simon Denman, Clinton Fookes, Milad Ramezani · 22. September 2026

  We present M3GA-Wild, the first benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests. M3GA-Wild unifies and extends existing forest localisation datasets, providing a holistic benchmark with synchronised RGB imagery and LiDAR from ground traversals spanning 36 km, a…

• The Role of Radiometric Features in Cross-Site Leaf-Wood Segmentation of LiDAR Point Clouds

  Roman Kaharlytskyi, Derek T. Robinson, Roberto Guglielmi · 21. September 2026

  Leaf-wood segmentation of individual trees from LiDAR point clouds is essential for quantitative structure models (QSMs) used in non-destructive biomass estimation. Existing segmentation methods typically exclude radiometric features (e.g., intensity, return number) to maximize cross-sensor compatib…

• Instance Segmentation and Fine-grained Classification for Urban Buildings with Adaptive Region Dividing and Spatially-Supervised Contrastive Learning

  Weiyuan Zhang, Qi Zhang, Hui Huang · 18. September 2026

  Accurate instance-level and functional understanding of urban buildings in large-scale point clouds is essential for digital city modeling and urban analysis. However, the extensive spatial coverage of urban scenes leads most existing methods to rely on predefined blocks for training and evaluation,…

• RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation

  Siyuan You, Guozheng Xu, Pengwei Zhou, Qiwen Jin, Jian Yao, Li Li · 16. September 2026

  Roof plane segmentation is one of the key procedures for reconstructing three-dimensional (3D) building models at levels of detail (LoD) 2 and 3 from airborne light detection and ranging (LiDAR) point clouds. The majority of current approaches for roof plane segmentation rely on the manually designe…

• KODAMA: Multimodal Digital Twin Reconstruction for Urban RF Propagation Modelling

  Maximiliano Wardle, A. Ryo Koblitz · 10. September 2026

  3D reconstruction typically strives for geometric fidelity or visual plausibility. Radio frequency digital twins (RFDT) are instead judged by whether communication channels behave in them as they do in the real world. RFDTs promise site-specific channel prediction but current practice forces a choic…

• Tree species mapping in Denmark: A comparison of spectral-temporal features with geospatial foundation model embeddings

  Alkiviadis Koukos, Spyros Kondylatos, Thomas Nord-Larsen, Lotte Nyborg, Christian T{\o}ttrup, Kenneth Grogan · 4. September 2026

  We map tree species across Denmark using National Forest Inventory plots and EO data, while evaluating the potential of foundation models for large-scale forest characterization. We compare two alternative input representations for tree species classification: (i) manually engineered spectral-tempor…

• Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery

  M\'elisande Teng, Arthur Ouaknine, Etienne Lalibert\'e, Yoshua Bengio, David Rolnick, Hugo Larochelle · 31. August 2026

  Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for ma…

• Bootstrapping a 4D LiDAR Annotation Tool from Video Foundation Models

  Jihun Kim, Hyun-Kurl Jang, Hyemin Yang, Jinnyeong Yang, Hyeokjun Kweon, Kuk-Jin Yoon · 27. August 2026

  Progress in 4D LiDAR segmentation is bottlenecked by data. Assigning temporally consistent labels across sparse point cloud sequences is costly and hard to scale, and every new task or domain tends to demand fresh dense annotation. This motivates a simple question of whether high-quality LiDAR train…

• Spatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors

  Guilherme Iablonovski, Pierre-Louis Frison, Tatiana Silva da Silva · 19. August 2026

  Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains difficult to obtain at city scale in the Global South where airborne LiDAR coverage is rare and commercial very high-resolution imagery i…

• Rapid Debris-Volume Estimation from Post-Hurricane Aerial Imagery

  Kooshan Amini, Jamie Ellen Padgett, Guha Balakrishnan · 19. August 2026

  Hurricane debris removal is planned, contracted, and federally reimbursed on the basis of volume estimates, yet operational practice still relies on parametric forecasts with 41-90% documented over-estimation or on truck-load tallies that arrive only after hauling begins. We present DebrisHeightNet,…

• Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing

  Yixi Cai, Rundong Li, Yuhan Xie, Qingwen Zhang, Patric Jensfelt, Fu Zhang · 17. August 2026

  LiDAR bundle adjustment is widely utilized in mapping to construct globally consistent point cloud maps. In this paper, we propose the first fully parallel computing framework to accelerate LiDAR bundle adjustment for large-scale mapping, incorporating three key techniques. First, we design an adapt…

• Transferable Above-Ground Biomass (AGB) Estimation Model from Multi-Sensor Data with Sparse Field Calibration

  Pann Thinzar Seint, Bryan Atwood, Subas Chhatkuli · 13. August 2026

  Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable. While field inventories provide high localized accuracy, they are spatially sparse; conversely, spaceborne LiDAR from the Global Ecosystem Dynamics …

• GeoAI-based post-segmentation quality validation of building footprints via spatial feature engineering

  Shah Imran Ahsan Chowdhury, Kazi Jihadur Rashid, Rajsree Das Tuli, Rahul Saha, Bulbul Ahammad · 11. August 2026

  Deep learning-based building footprint extraction from high-resolution imagery often produces topologically inconsistent vectors unfit for direct GIS database ingestion. To address this, we present a multidomain GeoAI quality control framework that automates error detection to systematically purify …

• Unsupervised Point Cloud Registration with Self-Distillation

  Christian L\"owens, Thorben Funke, Andr\'e Wagner, Alexandru Paul Condurache · 11. August 2026

  Rigid point cloud registration is a fundamental problem and highly relevant in robotics and autonomous driving. Nowadays deep learning methods can be trained to match a pair of point clouds, given the transformation between them. However, this training is often not scalable due to the high cost of c…

• Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery

  Laura Bader, Muhammad Ammar Ahmed, Xiao Xiang Zhu, G\"oran Kauermann · 10. August 2026

  Accurate estimation of forest height from satellite imagery is essential for applications such as carbon accounting, biodiversity monitoring, and ecosystem management. While recent deep learning approaches provide accurate predictions, they typically do not quantify predictive uncertainty. This limi…