Remote Sensing and LiDAR Applications

Latest papers

• 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…

• LoDA: A Level of Detection Aware Method and a Multimodal Sensing Benchmark for Object Level Change Detection

  Haitian Wang, Xinyu Wang, Sheldon Fung, Xian Zhang, Zichen Geng · 7 August 2026

  High-definition 3D LiDAR maps are important for autonomous driving and smart-city services, which require reliable detection of object-level changes in multi-temporal urban LiDAR to keep digital maps aligned with the physical world. Existing approaches from raster height differencing to depth image …

• Group-wise Supervision with Focal-Dice Loss for Long-Tailed Indoor Semantic Occupancy Prediction

  Qi Zheng, Zihuang Su, Xiao Pan · 3 August 2026

  Recently, 3D semantic occupancy prediction has garnered increasing attention for understanding the indoor scene. However, unlike structured outdoor environments, indoor scenes feature a high diversity of object categories that exhibit a severe long-tailed distribution, which has become a core bottle…

• ObliCity: A Benchmark and Baseline for Roof-to-Ground Projection Displacement Correction

  Kai Li, Yupeng Deng, Ligao Deng, Zhihao Xi, Chenhao Wang, Jierui Zhang, Yingrui Ji, Yu Meng, Xiangyu Zhao · 29 July 2026

  Oblique-view urban remote sensing imagery inevitably exhibits geometric projection displacements between building roofs and footprints, leading to significant distortions in spatial structure. Existing approaches either ignore these deformations or handle them implicitly within segmentation-based fr…

• A2D2: Audi Autonomous Driving Dataset

  Jakob Geyer, Yohannes Kassahun, Mentar Mahmudi, Xavier Ricou, Rupesh Durgesh, Andrew S. Chung, Lorenz Hauswald, Viet Hoang Pham, Maximilian M\"uhlegg, Sebastian Dorn, Tiffany Fernandez, Martin J\"anicke, Sudesh Mirashi, Chiragkumar Savani, Martin Sturm, Oleksandr Vorobiov, Martin Oelker, Sebastian Garreis, Peter Schuberth · 29 July 2026

  Research in machine learning, mobile robotics, and autonomous driving is accelerated by the availability of high quality annotated data. To this end, we release the Audi Autonomous Driving Dataset (A2D2). Our dataset consists of simultaneously recorded images and 3D point clouds, together with 3D bo…

• A Framework for Individual Tree Growth Reconstruction Using Multi-Platform Laser Scanning

  Daniella Tavi, Valtteri Soininen, Lassi Ruoppa, Jesse Muhojoki, Juha Hyyppä · 27 July 2026

  Accurate tree-level forest monitoring using laser scanning data requires reliable tree delineation, consistent tree correspondence across multitemporal point clouds, and accurate estimation of tree attributes and their change. Reconstructing tree growth in boreal forests is challenging due to the sc…

• DTIF: Robust Loop Closure Detection via Delaunay Triangle Topology in Complex Forests

  Xin Zhao, Jianping Li, Qin Zou, Fuxun Liang, Zhen Dong, Bisheng Yang · 24 July 2026

  Accurate forest inventory and large-scale mapping are essential for ecosystem monitoring and sustainable forest management. Multiple low-cost edge platforms enable efficient large-area data acquisition, but merging independently constructed local maps in GNSS-denied understory environments still req…

• Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types

  Michał Romaszewski, Kamil Drejer, Katarzyna Kołodziej, Anna Zawadzka, Stanisław Lewiński, Przemysław Głomb, Marek Ruciński, Michal Krupiński, Krzysztof Gryguc Przemysław Sekuła, Szymon Sala · 23 July 2026

  Sentinel-2 imagery offers open access, global coverage, and frequent revisit times, making it attractive for practical building mapping at scale; however, its native 10m resolution makes building vs non-building classification challenging, particularly for small or sub-pixel buildings, and performan…

• Global Building Area Estimation Products: How Accurate Are They?

  Saad Lahrichi, Doa'a Allabadi, Kyle Bradbury, Jordan Malof · 23 July 2026

  Geo-spatial rasters of building footprint area are useful for a variety of tasks, such as monitoring urbanization, improving energy efficiency, and tracking greenhouse gas emissions. There are now multiple global building raster datasets, however there lacks an independent, comprehensive, and fair a…

• Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation

  Ritu Yadav, Andrea Nascetti, Yifang Ban · 14 July 2026

  Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical. Yet most deep learning models yield only determini…

• 3D Reconstruction of deciduous Trees using low-cost UAV- and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies

  Stephan Nebiker, Micha Tschanz, Nando Amport, Frederik Baumgarten · 10 July 2026

  Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous me…

• WildCity: A Real-World City-Scale Testbed for Rendering, Simulation, and Spatial Intelligence

  Xiangyu Han, Mengyu Yang, Jiaqi Li, Bowen Chang, Ziyu Chen, Hexu Zhao, Rahul Kumar Agrawal, Anthony Rodriguez, Fiona Hua, Marco Pavone, Chen Feng, Yiming Li · 9 July 2026

  Humans can navigate an unfamiliar city and gradually form a coherent spatial mental map spanning tens of square kilometers. Can AI build spatial representations at a comparable scale? Although recent foundation models have advanced scene reconstruction and embodied intelligence, scaling to entire ci…

• Self-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation

  Heeju Mun, Tackang Yang, Yunsoo Nam, Changhyun Choi · 9 July 2026

  The accuracy of existing leaf-wood segmentation methods for tree point clouds varies across forest types and sites. Self-supervised learning (SSL) on point clouds has improved the generalization of deep learning models for forestry point cloud tasks, including biomass regression and individual tree …

• SharpSplat: Edge-Regularized 3D Gaussian Splatting for High Fidelity Urban Building Reconstruction from UAV images

  Porus Vaid, Shivam Chopra, Vaibhav Kumar · 7 July 2026

  Reconstructing high-fidelity 3D building models from UAV imagery is essential for large-scale digital twin development. However, existing 3D Gaussian Splatting (3DGS) techniques often struggle with building facades, failing to capture sharp geometric transitions. To address this, we propose a semant…

• SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests

  David-Alexandre Duclos, William Guimont-Martin, Gabriel Jeanson, Arthur Larochelle-Tremblay, Martine Lapointe, Th\'eo Defosse, Fr\'ed\'eric Moore, Philippe Nolet, Fran\c{c}ois Pomerleau, Philippe Gigu\`ere · 7 July 2026

  Interest in forestry automation is growing alongside rapid advances in deep learning. In particular, tree detection and taxonomic classification are seen as core tasks required for automating field surveys and forestry equipment. These operations must often be performed in under-canopy settings, whi…

• Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation

  Nils Griese, Christoph Kleinn, Nils N\"olke · 7 July 2026

  Conventional modeling approaches for LiDAR-based above-ground biomass (AGB) estimation rely on discrete plot-level inventory aggregates. This methodology introduces boundary-effect uncertainties that may severely degrade model performance within small field plots. To solve this limitation, we evalua…