本文提出Depth Any Canopy，通过微调深度基础模型Depth Anything v2实现高效树冠高度估计，以极低计算成本超越现有方法。
摘要：
估算全球树冠高度对于森林保护和气候变化应用至关重要。然而，使用LiDAR捕获高分辨率的地面真实树冠高度成本高昂，且无法在全球范围内获得。一种有效的替代方案是训练一个树冠高度估计器，使其能够处理单视角遥感影像。这种方法的主要障碍在于，这些方法需要大量的训练数据才能在全局范围内以及不常见的边缘情况下进行良好泛化。最近的单目深度估计基础模型即使在复杂场景中也表现出强大的零样本性能。在本文中，我们利用这些模型学习到的表示，将其迁移到遥感领域以测量树冠高度。我们的研究结果表明，我们提出的Depth Any Canopy（对Depth Anything v2模型进行微调以用于树冠高度估计的结果）提供了一种高性能且高效的解决方案，仅使用一小部分计算资源和参数，就超越了当前最先进的方法，具有优越或相当的性能。此外，我们的方法在计算上的成本不到1.30美元，估计碳足迹为0.14千克二氧化碳。代码、实验结果和模型检查点可在https://github.com/DarthReca/depth-any-canopy公开获取。
英文摘要：
Estimating global tree canopy height is crucial for forest conservation and climate change applications. However, capturing high-resolution ground truth canopy height using LiDAR is expensive and not available globally. An efficient alternative is to train a canopy height estimator to operate on single-view remotely sensed imagery. The primary obstacle to this approach is that these methods require significant training data to generalize well globally and across uncommon edge cases. Recent monocular depth estimation foundation models have show strong zero-shot performance even for complex scenes. In this paper we leverage the representations learned by these models to transfer to the remote sensing domain for measuring canopy height. Our findings suggest that our proposed Depth Any Canopy, the result of fine-tuning the Depth Anything v2 model for canopy height estimation, provides a performant and efficient solution, surpassing the current state-of-the-art with superior or comparable performance using only a fraction of the computational resources and parameters. Furthermore, our approach requires less than \$1.30 in compute and results in an estimated carbon footprint of 0.14 kgCO2. Code, experimental results, and model checkpoints are openly available at https://github.com/DarthReca/depth-any-canopy.
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）
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）
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）
A Novel Large Vision Foundation Model (LVFM)-based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management
The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）
Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2]. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago.
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）
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））
UAV3DCrop: Benchmarking 3D Reconstruction in Repeated Multi-Angle UAV Crop Surveys
University of Minnesota, Twin Cities（明尼苏达大学双城校区）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Pittsburgh（匹兹堡大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Boston University（波士顿大学）；Peking University（北京大学）
A Deep Learning Approach to Estimate Canopy Height and Uncertainty by Integrating Seasonal Optical, SAR and Limited GEDI LiDAR Data over Northern Forests
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…
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）
Adapting a Foundation Model for Lunar Surface Height Estimation
University of Technology of Troyes（特鲁瓦技术大学）；Hochschule Darmstadt（达姆施塔特应用科学大学）；European Space Agency（欧洲空间局）；Technische Hochschule Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用科学大学）
ETH Zürich（苏黎世联邦理工学院）；Yale University（耶鲁大学）；University of Zurich（苏黎世大学）
The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change, and prevent biodiversity loss. Here, we present the first global, wall-to-wall canopy height map at 10 m ground sampling distance for the year 2020. No single data source meets these requirements: dedicated space missions like GEDI deliver sparse height data, with unprecedented coverage, whereas optical sate…
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）
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
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）
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；Nanjing Forestry University（南京林业大学）
Forest land plays a vital role in global climate, ecosystems, farming and human living environments. Therefore, forest biomass estimation methods are necessary to monitor changes in the forest structure and function, which are key data in natural resources research. Although accurate forest biomass measurements are important in forest inventory and assessments, high-density measurements that involve airborne light detection and ranging (LiDAR) at a low flight height in large mountainous areas are highly expensive. The objective of this study was to quantify the aboveground biomass (AGB) of a p…
SatelliteCalculator: A Multi-Task Vision Foundation Model for Quantitative Remote Sensing Inversion
Faculty of Computer Science and Information Technology, Universiti Malaya（马来亚大学计算机科学与信息技术学院）；Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）
The increasing demand for commodities is leading to changes in land use worldwide. In the tropics, deforestation, which causes high carbon emissions and threatens biodiversity, is often linked to agricultural expansion. While the need for deforestation-free global supply chains is widely recognized, making progress in practice remains a challenge. Here, we propose an automated approach that aims to support conservation and sustainable land use planning decisions by mapping tropical landscapes at large scale and high spatial resolution following the High Carbon Stock (HCS) approach. A deep lear…
Tomographic SAR Reconstruction for Forest Height Estimation
University of Cambridge（剑桥大学）；United Nations Office for Outer Space Affairs（联合国外层空间事务厅）；UK Department for Energy Security and Net Zero（英国能源安全与净零部）；INSEC TEC；Drexel University（卓克索大学）
本文提出直接由二维 SLC SAR 图像利用深度学习估计森林冠层高度，并评估 SLC 数量影响；结果表明绕过完整层析处理会使误差增加 16%–21%。
FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data
Nanjing Agricultural University（南京农业大学）；Huazhong University of Science and Technology（华中科技大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Northwest A&F University（西北农林科技大学）；The University of Queensland（昆士兰大学）；Commonwealth Scientific and Industrial Research Organization（澳大利亚联邦科学与工业研究组织）；The University of Tokyo（东京大学）；INRAE（法国国家农业食品与环境研究院）；Arvalis（阿瓦利斯植物研究院）；ETH Zurich（苏黎世联邦理工学院）；International Center for Agricultural Research in the Dry Areas (ICARDA)（国际干旱地区农业研究中心）；University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；Chinese Academy of Agricultural Sciences（中国农业科学院）；Hiphen SAS（Hiphen公司）
GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Indian Institute of Tropical Meteorology（印度热带气象研究所）；CIEMAT（西班牙国家研究中心（能源、环境与技术研究中心））；Purdue University（普渡大学）；National Center for Atmospheric Research（美国国家大气研究中心）
The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University（信息工程测绘遥感国家重点实验室（LIESMARS），武汉大学）；Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember（工程技术学院测绘工程系，印度尼西亚技术学院十月份十号学院）；Department of Informatics and Information Technology, College of Natural and Applied Sciences (CoNAS), Sokoine University of Agriculture（信息与信息技术系，索科因农业学院自然与应用科学学院）；Geomatics Department, Tshwane University of Technology（测绘系，塔什韦恩技术大学）；Department of Geography, Environmental Management and Energy Studies, Faculty of Science, University of Johannesburg（地理、环境管理与能源研究系，科学学院，约翰内斯堡大学）
