Recent progress in vision language models (VLMs) has enabled remarkable perception and reasoning capabilities, yet their potential for scientific regression in Earth Observation (EO) remains largely unexplored. Existing EO datasets mainly emphasize semantic understanding tasks such as captioning or classification, lacking benchmarks that align multimodal perception with measurable biophysical variables. To fill this gap, we present REO-Instruct, the first unified benchmark designed for both descriptive and regression tasks in EO. REO-Instruct establishes a cognitively interpretable logic chain in forest ecological scenario (human activity,land-cover classification, ecological patch counting, above-ground biomass (AGB) regression), bridging qualitative understanding and quantitative prediction. The dataset integrates co-registered Sentinel-2 and ALOS-2 imagery with structured textual annotations generated and validated through a hybrid human AI pipeline. Comprehensive evaluation protocols and baseline results across generic VLMs reveal that current models struggle with numeric reasoning, highlighting an essential challenge for scientific VLMs. REO-Instruct offers a standardized foundation for developing and assessing next-generation geospatial models capable of both description and scientific inference. The project page are publicly available at \href{https://github.com/zhu-xlab/REO-Instruct}{REO-Instruct}.
Tracking Phenological Status and Ecological Interactions in a Hawaiian Cloud Forest Understory using Low-Cost Camera Traps and Visual Foundation Models
Computer Science, The Ohio State University, Columbus, Ohio, USA（俄亥俄州立大学计算机科学系）；Computer Science, The University of Puerto Rico Rio Piedras, San Juan, Puerto Rico（波多黎各大学里奥皮埃拉斯分校计算机科学系）；Computer Science, McGill University, Montreal, Quebec, Canada（麦吉尔大学计算机科学系）；Battele Ecology, Neon Domain 20, Hilo, Hawaii（Battele生态研究所）；Ecology, Princeton University, Princeton, New Jersey, USA（普林斯顿大学生态学系）
Ecological Legacies of Pre-Columbian Settlements Evident in Palm Clusters of Neotropical Mountain Forests
Leiden Institute of Advanced Computer Science (LIACS), Leiden University（莱顿大学莱顿高级计算机科学研究所）；Computational Social Sciences and Humanities Laboratory, Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS)（巴塞罗那超级计算中心-国家超级计算中心计算社会科学与人文实验室）；Subdirección de Gestión del Patrimonio, Instituto Colombiano de Antropología e Historia（哥伦比亚人类学与历史研究所遗产管理副局）；Department of Humanities, CASEs group, Universitat Pompeu Fabra（庞培法布拉大学人文系CASEs研究组）；Facultad de Artes, Universidad Nacional de Colombia（哥伦比亚国立大学艺术学院）；Naturalis Biodiversity Center（荷兰自然生物多样性中心）；Division of Earth Observation and Geoinformatics (DIOTG), General Coordination of Earth Sciences (CG-CT), National Institute for Space Research (INPE)（巴西国家空间研究所地球科学总协调部地球观测与地理信息司）；Departmento de Antropología, Universidad del Magdalena（马格达莱纳大学人类学系）；ICREA（加泰罗尼亚研究及高级研究机构）；GAES, The University of the Witwatersrand（威特沃特斯兰德大学GAES）
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）
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）
JOANNEUM RESEARCH（约阿内姆研究公司）；Albert-Ludwigs-University Freiburg（弗赖堡阿尔伯特-路德维希大学）；University of Patras（帕特雷大学）
Purpose of review: This paper presents a review of the current state of the art in remote sensing based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing approaches: classical image-to-image change detection and time series analysis. Recent findings: With the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has…
Sun Yat-sen University（中山大学）；UiT the Arctic University of Norway（挪威北极大学）；University of Cambridge（剑桥大学）
Global forest cover is critical to the provision of certain ecosystem services. With the advent of the google earth engine cloud platform, fine resolution global land cover mapping task could be accomplished in a matter of days instead of years. The amount of global forest cover (GFC) products has been steadily increasing in the last decades. However, it's hard for users to select suitable one due to great differences between these products, and the accuracy of these GFC products has not been verified on global scale. To provide guidelines for users and producers, it is urgent to produce a val…
Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity
University College London（伦敦大学学院）；Zoological Society of London（伦敦动物学会）；Instituto Biotrópicos（Biotrópicos研究所）；University of Kent（肯特大学）；University of Oxford（牛津大学）；Tribhuvan University（特里布万大学）；University of Edinburgh（爱丁堡大学）
deadtrees.earth-aerial: A Multi-Resolution Aerial Image Dataset for Tree Cover and Mortality Detection
Chair of Sensor-based Geoinformatics, University of Freiburg（传感器基于地理信息学系，弗赖堡大学）；EcoVision Lab, DM3L, University of Zurich（生态视觉实验室，苏黎世大学）；Institute for Earth System Science and Remote Sensing, Leipzig University（地球系统科学与遥感研究所，莱比锡大学）；Washington University, St. Louis（斯蒂芬斯敦大学）
Tree-GPT: Modular Large Language Model Expert System for Forest Remote Sensing Image Understanding and Interactive Analysis
Shenzhen University（深圳大学）；Research Institute for Smart Cities（智慧城市研究院）；State Key Laboratory of Subtropical Building and Urban Science（亚热带建筑与城市科学国家重点实验室）；Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities（粤港澳智慧城市联合实验室）
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）
