Reliable detection of humans beneath forest canopy remains a difficult remote-sensing challenge due to sparse, structured, and viewpoint-dependent occlusion. This paper presents a multimodal proof-of-concept pipeline that integrates three complementary approaches: (i) experimental evaluation of LiDAR returns through vegetation to assess the feasibility of active sensing, (ii) visible--thermal image fusion using a multi-scale transform and sparse-representation framework to enhance human saliency, and (iii) synthetic-aperture image formation via Airborne Optical Sectioning (AOS) to suppress canopy clutter. A YOLOv5 detector is fine-tuned on the Teledyne FLIR thermal dataset and evaluated on thermal and fused imagery. Results show that the tested terrestrial LiDAR configuration provides limited penetration for object-level detection, while visible--thermal fusion improves target visibility in low-contrast scenes and AOS enhances ground-plane detection in synthetic forest imagery. The fine-tuned YOLOv5 achieves a mean average precision of $\sim$0.83 on the top three FLIR classes. These findings establish an initial baseline for UAV-deployable search-and-rescue and surveillance systems operating in forested environments, and motivate future work on dedicated forest datasets and real-time multimodal integration.
FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series
organization= Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA, CNRS, UVSQ, Université Paris-Saclay , city= Gif-sur-Yvette , country= France；organization= CNRS \& Département d'Informatique, École Normale Supérieure -- PSL , addressline= 45 Rue d'Ulm , postcode= 75005 , city= Paris , country= France；organization= EcoVision Lab, DM3L, University of Zurich , country= Switzerland；organization= LIGM, École des Ponts, CNRS, IPP, UGE；organization= Institute of Earth System Science
SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests
Northern Robotics Laboratory, Université Laval（北方机器人实验室，拉瓦尔大学）；Département des sciences du bois et de la forêt, Université Laval（林业与木材科学系，拉瓦尔大学）；Institut des Sciences de la Forêt tempérée, Université du Québec en Outaouais（温带森林科学研究所，魁北克outsouais大学）
Department of Geosciences and Natural Resource Management, University of Copenhagen（哥本哈根大学地球科学与自然资源管理系）；Department of Mathematical Sciences, University of Copenhagen（哥本哈根大学数学科学系）；Department of Computer Science and Electrical Engineering, University of Rostock（罗斯托克大学计算机科学与电气工程系）；Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）
In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios. We adapt a state of the art template matching feature into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. We train with positive samples only and we treat class distributions at the leaf nodes as latent variables. During testing we infer by iteratively updating these distributions, providing accurate estimation of background clutter and foreground occlusions and, thus,…
Semantic segmentation of forest stands using deep learning
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences（生命科学大学环境科学与自然资源管理学院）；Faculty of Science and Technology, Norwegian University of Life Sciences（生命科学大学科学与技术学院）
School of Geospatial Engineering and Science, Sun Yat-Sen University（中山大学测绘科学与技术学院）；Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University（香港理工大学土地测量及地理资讯学系）；Changjiang River Scientific Research Institute (CRSRI)（长江水利委员会长江科学院）；Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院数字地球重点实验室）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））
Registration of unmanned aerial vehicle laser scanning (ULS) and ground light detection and ranging (LiDAR) point clouds in forests is critical to create a detailed representation of a forest structure and an accurate inversion of forest parameters. However, forest occlusion poses challenges for marker-based registration methods, and some marker-free automated registration methods have low efficiency due to the process of object (e.g., tree, crown) segmentation. Therefore, we use a divide-and-conquer strategy and propose an automated and efficient method to register ULS and ground LiDAR point …
High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model
CTrees；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Institute of Environment and Sustainability, University of California, Los Angeles（加州大学洛杉矶分校环境与可持续发展研究所）；Quapá Lab, Faculty of Architecture and Urbanism, University of São Paulo（圣保罗大学建筑与城市规划学院Quapá实验室）；Gamma Remote Sensing Ag（Gamma遥感公司）；USDA Forest Service, International Institute of Tropical Forestry（美国农业部林业局热带林业研究所）；EMBRAPA Satellite Monitoring（巴西农业研究院卫星监测中心）；Remote Sensing Division, National Institute for Space Research—INPE（巴西国家空间研究院遥感部）；Department of Geosciences and Natural Resource Management, University of Copenhagen（哥本哈根大学地球科学与自然资源管理系）；Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ（气候与环境科学实验室（CEA-CNRS-UVSQ））
SelectAnyTree: A Promptable Instance Segmentation Model for 3D Forest LiDAR Point Clouds
Nagoya University（名古屋大学）；RIKEN（理化学研究所）；University of Freiburg（弗莱堡大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Twente（特温特大学）；KC Machine Learning Lab（KC机器学习实验室）；Hanoi University of Science and Technology（河内科技大学）；Ritsumeikan University（立命馆大学）
Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理空间研究 institute FGI，芬兰国家土地测绘局）
Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlappi…
A Deep Learning Approach to Estimate Canopy Height and Uncertainty by Integrating Seasonal Optical, SAR and Limited GEDI LiDAR Data over Northern Forests
Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on RGB Images of Closed Canopy: Validation Using TLS
University of Cambridge（剑桥大学）；Queen Mary University of London（伦敦玛丽女王大学）；Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学数字环境研究所）
AIT Austrian Institute of Technology（奥地利技术研究所）；TU Wien（维也纳工业大学）
Object detection in natural environments is still a very challenging task, even though deep learning has brought a tremendous improvement in performance over the last years. A fundamental problem of object detection based on deep learning is that neither the training data nor the suggested models are intended for the challenge of fragmented occlusion. Fragmented occlusion is much more challenging than ordinary partial occlusion and occurs frequently in natural environments such as forests. A motivating example of fragmented occlusion is object detection through foliage which is an essential re…
Forest canopy height estimation from satellite RGB imagery using large-scale airborne LiDAR-derived training data and monocular depth estimation
Beijing Normal University（北京师范大学）；CSIRO Environment（英联邦科学与工业研究组织环境部）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Wuhan University（武汉大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests however remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density o…
Accurate and reliable kiwifruit detection is one of the biggest challenges in developing a selective fruit harvesting robot. The vision system of an orchard robot faces difficulties such as dynamic lighting conditions and fruit occlusions. This paper presents a semantic segmentation approach with two novel image prepossessing techniques designed to detect kiwifruit under the harsh lighting conditions found in the canopy. The performance of the presented system is evaluated on a 3D real-world image set of kiwifruit under different lighting conditions (typical, glare, and overexposed). Alone the…
ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation
Nagoya University（名古屋大学）；RIKEN Seika（日本理化学研究所Seika研究中心）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Twente（埃因霍温理工大学）；Ritsumeikan University（立命馆大学）
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-SCE32UVSQ, Université Paris Saclay（巴黎萨克雷大学气候与环境科学实验室）；Kayrros SAS（Kayrros SAS公司）；IGN, Laboratoire d’Inventaire Forestier（法国国家地理与森林信息研究所森林清查实验室）；INRAE, UMR TETIS, Université de Montpellier, AgroParisTech（法国国家农业食品与环境研究院、蒙彼利埃大学、巴黎高科农业学院）；Department of Geosciences and Natural Resource Management, University of Copenhagen（哥本哈根大学地球科学与自然资源管理系）；IGN, Service de l'Information Statistique, Forestière et Environnementale（法国国家地理与森林信息研究所统计、森林与环境信息服务处）；CESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS（图卢兹大学生物圈空间研究中心）；ISPA, UMR 1391, INRA Nouvelle-Aquitaine（法国国家农业研究院新阿基坦大区农业与环境相互作用研究所）
Scattered trees outside of dense, closed-canopy forests are very important for carbon sequestration, supporting livelihoods, maintaining ecosystem integrity, and climate change adaptation and mitigation. In contrast to trees inside of closed-canopy forests, not much is known about the spatial extent and distribution of scattered trees at a global scale. Due to the cost of high-resolution satellite imagery, global monitoring systems rely on medium-resolution satellites to monitor land use. Here we present a globally consistent method to identify trees with canopy diameters greater than three me…
ETH Zürich（苏黎世联邦理工学院）；University of Maryland（马里兰大学）；University of Zurich（苏黎世大学）
NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a…
First Mapping the Canopy Height of Primeval Forests in the Tallest Tree Area of Asia
Beijing Forestry University（北京林业大学）；Delft University of Technology（代尔夫特理工大学）；University of Amsterdam（阿姆斯特丹大学）；Tibet Agricultural & Animal Husbandry University（西藏农牧学院）
Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-SCE32UVSQ, Université Paris Saclay（巴黎萨克雷大学气候与环境科学实验室）；Kayrros SAS（Kayrros SAS公司）；ISPA, UMR 1391, INRA Nouvelle-Aquitaine（法国国家农业食品与环境研究院新阿基坦大区ISPA联合研究单位）；INRAE, UMR TETIS, Université de Montpellier, AgroParisTech（蒙彼利埃大学、巴黎高科农业学院、法国国家农业食品与环境研究院TETIS联合研究单位）；Department of Computer Science, École Normale Supérieure（巴黎高等师范学院计算机科学系）；Jet Propulsion Laboratory (JPL), California Institute of Technology（加州理工学院喷气推进实验室）；Université Paris-Saclay, AgroParisTech, INRAE, UMR 518 MIA Paris-Saclay（巴黎萨克雷大学、巴黎高科农业学院、法国国家农业食品与环境研究院MIA Paris-Saclay联合研究单位）
ECHOSAT: Estimating Canopy Height Over Space And Time
Department of Information Systems, University of Münster（慕尼黑大学信息系统系）；Laboratoire des Sciences du Climat et de l’Environnement (LSCE)（气候与环境科学实验室）；Zuse Institute Berlin (ZIB)（柏林Zuse研究所）；Jet Propulsion Laboratory (JPL), California Institute of Technology（喷气推进实验室（JPL），加州理工学院）；Technische Universität Berlin（柏林技术大学）
Modeling forests using historical data allows for more accurately evolution analysis, thus providing an important basis for other studies. As a recognized and effective tool, remote sensing plays an important role in forestry analysis. We can use it to derive information about the forest, including tree type, coverage and canopy density. There are many forest time series modeling studies using statistic values, but few using remote sensing images. Image prediction digital twin is an implementation of digital twin, which aims to predict future images bases on historical data. In this paper, we …
Occlusion caused by vegetation is an essential problem for remote sensing applications in areas, such as search and rescue, wildfire detection, wildlife observation, surveillance, border control, and others. Airborne Optical Sectioning (AOS) is an optical, wavelength-independent synthetic aperture imaging technique that supports computational occlusion removal in real-time. It can be applied with manned or unmanned aircrafts, such as drones. In this article, we demonstrate a relationship between forest density and field of view (FOV) of applied imaging systems. This finding was made with the h…
Sorbonne Universités, UPMC Univ Paris 06, CNRS, ISIR UMR 7222（索邦大学，巴黎第六大学，法国国家科学研究中心，ISIR UMR 7222）
Fully-Automatic Facial Expression Recognition (FER) from still images is a challenging task as it involves handling large interpersonal morphological differences, and as partial occlusions can occasionally happen. Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the variability may not be fully covered by the training data. In this work, we propose to train Random Forests upon spatially defined local subspaces of the face. The output local predictions form a categorical expression-driven high-level representation that we call Local Expression P…
Through-Foliage Surface-Temperature Reconstruction for Early Wildfire Detection
Department of Computer Science, Johannes Kepler University（计算机科学系，约翰尼斯·开普勒大学）；Fire Brigade St. Agatha（圣阿加塔消防队）；Upper Austria Fire Brigade Headquarter（上奥地利消防队总部）
Enhancing UAV Search under Occlusion using Next Best View Planning
Swarm Exploration Group, Department of Communication Systems, Institute for Communications and Navigation, German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR)（德国航空航天中心（德意志航天中心））
Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin
Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich（julich超级计算中心（JSC），julich研究所）；School of Engineering and Natural Sciences (SENS), University of Iceland（工程与自然科学学院（SENS），冰岛大学）；Global Land Monitoring Group, GFZ Helmholtz Centre for Geosciences（全球土地监测组，geofz赫尔姆霍兹研究中心）
We demonstrate how efficient autonomous drone swarms can be in detecting and tracking occluded targets in densely forested areas, such as lost people during search and rescue missions. Exploration and optimization of local viewing conditions, such as occlusion density and target view obliqueness, provide much faster and much more reliable results than previous, blind sampling strategies that are based on pre-defined waypoints. An adapted real-time particle swarm optimization and a new objective function are presented that are able to deal with dynamic and highly random through-foliage conditio…
