This work leverages neural radiance fields and remote sensing for forestry applications. Here, we show neural radiance fields offer a wide range of possibilities to improve upon existing remote sensing methods in forest monitoring. We present experiments that demonstrate their potential to: (1) express fine features of forest 3D structure, (2) fuse available remote sensing modalities and (3), improve upon 3D structure derived forest metrics. Altogether, these properties make neural fields an attractive computational tool with great potential to further advance the scalability and accuracy of forest monitoring programs.
Evaluating the point cloud of individual trees generated from images based on Neural Radiance fields (NeRF) method
Fuzhou University（福州大学）；National Engineering Research Center of Geospatial Information Technology（国家地理空间信息技术工程研究中心）；Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education（空间数据挖掘与信息共享教育部重点实验室）；The Academy of Digital China (Fujian)（数字中国研究院（福建））
Neural Tree Reconstruction for the Open Forest Observatory
Berkeley AI Research, University of California, Berkeley（加州大学伯克利分校伯克利人工智能研究）；Department of Plant Sciences, University of California, Davis（加州大学戴维斯分校植物科学系）
Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring
Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford, UK（牛津大学机器人研究所、工程科学系、牛津大学、牛津、英国）；Group of Automation, Robotics and Computer Vision, University of Alicante, Alicante, Spain（自动化、机器人与计算机视觉小组、阿尔瓦登特大学、阿尔瓦登特、西班牙）；Department of Biology, University of Oxford, Oxford, UK（生物学系、牛津大学、牛津、英国）
Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and extraction of individual tree parameters
Fuzhou University（福州大学）；National Engineering Research Center of Geospatial Information Technology（国家地理空间信息技术工程研究中心）；Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education（教育部空间数据挖掘与信息共享重点实验室）；The Academy of Digital China (Fujian)（数字中国研究院（福建））
Mapping Semantic Segmentation to Point Clouds Using Structure from Motion for Forest Analysis
Universidad de Buenos Aires（布宜诺斯艾利斯大学）；Facultad de Ciencias Exactas y Naturales（精确科学与自然学院）；CONICET-Universidad de Buenos Aires（CONICET-布宜诺斯艾利斯大学）；Instituto de Ciencias de la Computación (ICC)（计算科学研究所）
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）
Minkowski-MambaNet: A Point Cloud Framework with Selective State Space Models for Forest Biomass Quantification
School of Computer Science and Technology, the Key Laboratory of Signal Detection and Processing, and the Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center, Xinjiang University（计算机科学与技术学院、信号检测与处理重点实验室以及新疆多模态智能处理与信息安全工程技术研究中心，新疆大学）；National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）；Xinjiang Guanghui New Energy Co., Ltd（新疆广汇新能源有限公司）；Hongyousoft Co., Ltd（宏远软件有限公司）
DTIF: Robust Loop Closure Detection via Delaunay Triangle Topology in Complex Forests
School of Computer Science, Wuhan University（武汉大学计算机科学学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Urban Design, Wuhan University（武汉大学城市设计学院）；LIESMARS, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）
Towards 3D Scene Understanding of Gas Plumes in LWIR Hyperspectral Images Using Neural Radiance Fields
Utah State University, Department of Mathematics & Statistics（犹他州立大学数学与统计学系）；Los Alamos National Laboratory, Intelligence and Space Research Division（洛斯阿拉莫斯国家实验室情报与空间研究部）
OpenForest: A data catalogue for machine learning in forest monitoring
McGill University（麦吉尔大学）；Mila, Quebec AI Institute（魁北克人工智能研究所（Mila））；Leipzig University（莱比锡大学）；German Centre for Integrative Biodiversity Research (iDiv)（德国综合生物多样性研究中心）；Université de Montréal（蒙特利尔大学）
FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring
National Observatory of Athens（雅典国家天文台）；National Technical University of Athens（雅典国家技术大学）；Harokopio University of Athens（哈罗科皮奥大学）；McGill University（麦吉尔大学）
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Spatial Informatics Group, LLC.（空间信息集团有限责任公司）；ADNET systems, INC.（ADNET系统公司）；NASA Langley Research Center（美国国家航空航天局兰利研究中心）；University at Albany（奥尔巴尼大学）
Tropical forests represent the home of many species on the planet for flora and fauna, retaining billions of tons of carbon footprint, promoting clouds and rain formation, implying a crucial role in the global ecosystem, besides representing the home to countless indigenous peoples. Unfortunately, millions of hectares of tropical forests are lost every year due to deforestation or degradation. To mitigate that fact, monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. These monitoring/detection programs gener…
Global environment monitoring is a task that requires additional attention in the contemporary rapid climate change environment. This includes monitoring the rate of deforestation and areas affected by flooding. Satellite imaging has greatly helped monitor the earth, and deep learning techniques have helped to automate this monitoring process. This paper proposes a solution for observing the area covered by the forest and water. To achieve this task UNet model has been proposed, which is an image segmentation model. The model achieved a validation accuracy of 82.55% and 82.92% for the segmenta…
Country-wide, high-resolution monitoring of forest browning with Sentinel-2
ETH Zurich（苏黎世联邦理工学院）；ETH AI Center（ETH人工智能中心）；Swiss Data Science Center（瑞士数据科学中心）；EPFL（洛桑联邦理工学院）；Swiss Federal Institute for Forest, Snow and Landscape Research WSL（瑞士联邦森林、雪与景观研究所）；University of Bern（伯尔尼大学）；Oeschger Centre for Climate Change Research（厄施格气候变化研究中心）
Remote Sensing Technology Institute, German Aerospace Center（德国航空航天中心遥感技术研究所）；Technical University of Munich（慕尼黑工业大学）；TJ Watson Research Center, IBM Research（IBM研究院托马斯·J·沃森研究中心）
We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interaction. Successfully training machine learning models for semantic segmentation typically depends on the availability of high-quality labels. We evaluate the benefit of high-resolution, three-dimensional point cloud data (LiDAR) as source of noisy labels in order to train models for the localization of trees in orthophotos. As proof of concept we sense Hurricane Sandy's impact on urban forests in Coney Island, New York…
Recently, there has been a growing interest in monitoring brain activity for individual recognition system. So far these works are mainly focussing on single channel data or fragment data collected by some advanced brain monitoring modalities. In this study we propose new individual recognition schemes based on spatio-temporal resting state Electroencephalography (EEG) data. Besides, instead of using features derived from artificially-designed procedures, modified deep learning architectures which aim to automatically extract an individual's unique features are developed to conduct classificat…
University of Cambridge（剑桥大学）；Royal Society for Protection of Birds（英国皇家鸟类保护协会）；Jambi University（占碑大学）；PT Restorasi Ekosistem Indonesia（印度尼西亚生态系统恢复公司）
Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crow…
Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition
University of Oxford（牛津大学）；University of Amsterdam（阿姆斯特丹大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Imperial College London（伦敦帝国理工学院）；University of Copenhagen（哥本哈根大学）
Estimating forest carbon stocks from high-resolution remote sensing imagery by reducing domain shift with style transfer
School of Geography and Remote Sensing, Guangzhou University（广州大学地理与遥感学院）；Innovation Center for Remote Sensing Big Data Intelligent Applications, Guangzhou University（广州大学遥感大数据智能应用创新中心）
Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation
Mathematical Modeling Laboratory, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University（九州大学生物资源与生物环境科学研究生院数学建模实验室）；International Cooperation Center, Thai Nguyen University（Thai Nguyen大学国际合作中心）；Centrer of Crop Research for Adaptation to Climate Change, Thai Nguyen University of Agriculture and Forestry（Thai Nguyen农业与林业大学适应气候变化作物研究中心）
Streamlining Forest Wildfire Surveillance: AI-Enhanced UAVs Utilizing the FLAME Aerial Video Dataset for Lightweight and Efficient Monitoring
Renmin University of China（中国人民大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；International Research Institute of Disaster Science (IRIDeS), Tohoku University（东北大学灾害科学国际研究所）
DeepForestVisionV2: Ecology-Driven Taxonomy Expansion for Camera-Trap Monitoring in African Tropical Forests
UMR7206 Eco-Anthropologie, MNHN（UMR7206 生态人类学，法国国家自然历史博物馆）；One Forest Vision initiative（One Forest Vision 倡议）；Sebitoli Chimpanzee Project（塞比托利黑猩猩项目）；Centre National de la Recherche Scientifique et Technologique（国家科学技术研究中心）；Institut de Recherche en Ecologie Tropicale（热带生态研究所）；Tacugama Chimpanzee Sanctuary（塔库加马黑猩猩保护区）；Biotope（Biotope 公司）；CIRAD（法国农业发展国际合作研究中心）；Max Planck Institute for Evolutionary Anthropology（马克斯·普朗克进化人类学研究所）；BOKU University（维也纳自然资源与生命科学大学）；Agence Nationale des Parcs Nationaux du Gabon（加蓬国家公园管理局）；Uganda Wildlife Authority（乌干达野生动物管理局）；Addax Data Science（Addax 数据科学公司）；Utrecht University（乌得勒支大学）
Bialystok University of Technology（比亚韦斯托克理工大学）；Mammal Research Institute, Polish Academy of Sciences（波兰科学院哺乳动物研究所）
Camera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Bialowieza Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open source application for managin…
Low-Cost Tree Crown Dieback Estimation Using Deep Learning-Based Segmentation
University of Cambridge（剑桥大学）；Universidad de Alcalá（阿尔卡拉大学）；Institute of Forest Sciences (INIA-CSIC)（森林科学研究所（INIA-CSIC））；Queen Mary University of London（伦敦玛丽女王大学）；Digital Environment Research Institute（数字环境研究所）
Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery
Bonn-Rhein-Sieg University of Applied Sciences（博恩-莱茵-锡格应用科学大学）；Department of Computer Science（计算机科学系）；Institute for Artificial Intelligence and Autonomous Systems (A2S)（人工智能与自主系统研究所）；Institute of Technology, Resource and Energy-efficient Engineering (TREE)（技术与资源节能工程研究所）
本文针对无人机林地地表分割难题，采用 PEFT 微调 SAM 并改造其掩码解码器实现无提示自动分割，证明适配器方法 mIoU 最高，LoRA 更轻量。
ETH Zurich（苏黎世联邦理工学院）；Norwegian Institute of Bioeconomy Research（挪威生物经济研究所）；University of Zurich（苏黎世大学）
Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-meter resolution, using freely available satellite imagery as input. Our method jointly tra…
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）
