Protecting and restoring forest ecosystems is critical for biodiversity conservation and carbon sequestration. Forest monitoring on a global scale is essential for prioritizing and assessing conservation efforts. Satellite-based remote sensing is the only viable solution for providing global coverage, but to date, large-scale forest monitoring is limited to single modalities and single time points. In this paper, we present a dataset consisting of data from five public satellites for recognizing forest plantations and planted tree species across the globe. Each satellite modality consists of a multi-year time series. The dataset, named \PlantD, includes over 2M examples of 64 tree label classes (46 genera and 40 species), distributed among 41 countries. This dataset is released to foster research in forest monitoring using multimodal, multi-scale, multi-temporal data sources. Additionally, we present initial baseline results and evaluate modality fusion and data augmentation approaches for this dataset.
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
Technical University of Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；University of Warwick（华威大学）；New York University（纽约大学）；WWF Switzerland（世界自然基金会瑞士分会）
Decadal sink-source shifts of forest aboveground carbon since 1988
Munich Climate Center and Earth System Modelling Group（慕尼黑气候中心和地球系统建模组）；Department of Aerospace and Geodesy, TUM School of Engineering and Design（航空航天与大地测量系，技术大学慕尼黑工程与设计学院）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；School of Systems Science and Institute of Nonequilibrium Systems（系统科学学院和非平衡系统研究所）；Beijing Normal University（北京师范大学）
DeepSat - A Learning framework for Satellite Imagery
Louisiana State University（路易斯安那州立大学）；Bay Area Environmental Research Institute（湾区环境研究所）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；NASA Advanced Supercomputing Division（美国国家航空航天局高级计算部门）
From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry
School of Earth Sciences, The Ohio State University（地球科学学院，俄亥俄州立大学）
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
Max-Planck-Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；German Aerospace Center (DLR)（德国航空航天中心）；University of Jena（耶拿大学）；Michael-Stifel-Center Jena for Data-driven and Simulation Science（耶拿米夏埃尔·施蒂费尔数据驱动与仿真科学中心）；Technische Universität Berlin（柏林工业大学）
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）
Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for Czechia
Charles University（查理大学）；European Space Agency (ESA/ESRIN)（欧洲空间局（欧洲空间局地球观测中心））；AI4EARTH
Mapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021
University of California, Los Angeles（加州大学洛杉矶分校）；California Institute of Technology（加州理工学院）；NASA-Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
监测树木覆盖率的变化以快速评估毁林情况被认为是任何减少碳排放的气候减缓政策的关键组成部分。在这里，我们使用巴西马托格罗索州（MT）5米空间分辨率的Planet NICFI卫星图像和U-net深度学习模型绘制了2015年至2022年之间的热带树木覆盖和森林砍伐。2015年该州的树木覆盖面积为556510. 8 km $^2$（占蒙大拿州的58. 1%），2021年底减少至141598. 5 km $^2$（占总面积的14. 8%）。在2016年12月达到最小毁林面积6632.05 km $^2$后，两年一次的毁林面积仅在2016年12月至2019年12月期间略有增加。一年后，森林砍伐面积几乎翻了一番，从2019年12月的9944. 5公里$^2$增加到2021年12月的19817. 8公里$^2$。高分辨率数据产品与巴西官方森林砍伐地图（67.2%）相对一致，但与全球森林变化产品的森林覆盖损失估计数存在很大差异，主要原因是全球森林变化数据中观察到大面积的火灾退化。来自NICFI星球的高分辨率图像与深度学习技术相结合，可以显著改善热带地区森林砍伐程度的测绘。
A Satellite Band Selection Framework for Amazon Forest Deforestation Detection Task
Institute of Science and Technology Universidade Federal de São Paulo（圣保罗联邦大学科学与技术学院）；Escola de Artes, Ciências e Humanidades University of Sao Paulo（圣保罗大学艺术、科学与人文学院）
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery
Statistics Department, UC Berkeley（加州大学伯克利分校统计系）；Electrical Engineering & Computer Science Department, UC Berkeley（加州大学伯克利分校电气工程与计算机科学系）；Global Policy Laboratory, Goldman School of Public Policy, UC Berkeley（加州大学伯克利分校高盛公共政策学院全球政策实验室）；Center for the Environment and Data Science Initiative, Harvard University（哈佛大学环境中心与数据科学计划）；Bren School of Environmental Science & Management, UC Santa Barbara（加州大学圣塔芭芭拉分校布伦环境科学与管理学院）；Rhodium Group（荣鼎集团）
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）
A Deep Learning Approach to Estimate Canopy Height and Uncertainty by Integrating Seasonal Optical, SAR and Limited GEDI LiDAR Data over Northern Forests
Comparing remote sensing-based forest biomass mapping approaches using new forest inventory plots in contrasting forests in northeastern and southwestern China
School of GeoSciences, University of Edinburgh（爱丁堡大学地球科学学院）；Space Intelligence Ltd（空间智能有限公司）；Finnish Geospatial Research Institute（芬兰地理空间研究所）；Institute for Logic, Language and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Department of Forest Science, University of Helsinki（赫尔辛基大学森林科学系）；Jihua Lab（季华实验室）
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）
From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model
University of Minnesota（明尼苏达大学）；University of Minnesota Informatics Institute（明尼苏达大学信息学研究所）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）
