We introduce a unique semantic segmentation dataset of 6,096 high-resolution aerial images capturing indigenous and invasive grass species in Bega Valley, New South Wales, Australia, designed to address the underrepresented domain of ecological data in the computer vision community. This dataset presents a challenging task due to the overlap and distribution of grass species, which is critical for advancing models in ecological and agronomical applications. Our study features a homotopy-based multi-objective fine-tuning approach that balances segmentation accuracy and contextual consistency, applicable to various models. By integrating DiceCELoss for pixel-wise classification and a smoothness loss for spatial coherence, this method evolves during training to enhance robustness against noisy data. Performance baselines are established through a case study on the Segment Anything Model (SAM), demonstrating its effectiveness. Our annotation methodology, emphasizing pen size, zoom control, and memory management, ensures high-quality dataset creation. The dataset and code will be made publicly available, aiming to drive research in computer vision, machine learning, and ecological studies, advancing environmental monitoring and sustainable development.
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）
A continental-scale dataset of ground beetles with high-resolution images and validated morphological trait measurements
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Virginia Tech（弗吉尼亚理工大学）；The University of Maine（缅因大学）；University of Florida（佛罗里达大学）；The Ohio State University（俄亥俄州立大学）；Vector Institute（向量研究所）；University of Guelph（圭尔夫大学）；National Ecological Observatory Network（国家生态观测网络）
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability
Leibniz-Institut für Agrartechnik und Bioökonomie e.V. (ATB)（莱布尼茨农业工程与生物经济研究所）；Technische Universität Berlin（柏林工业大学）；Understandable Machine Intelligence Lab (UMI Lab)（可理解机器智能实验室）；Universität Potsdam（波茨坦大学）
Enhancing Maritime Situational Awareness through End-to-End Onboard Raw Data Analysis
European Space Agency（欧洲空间局）；University of Naples Federico II（那不勒斯费德里科二世大学）；Ben-Gurion University of the Negev（本-古里安大学内盖夫分校）；University of Campania “L. Vanvitelli”（坎帕尼亚大学“L.万维泰利”分校）；Advanced Concepts and Studies Office, European Space Agency（欧洲空间局先进概念与研究办公室）
Photorealistic Inpainting for Perturbation-based Explanations in Ecological Monitoring
Duke University（杜克大学）；University of Agder（阿格德大学）；University of Cambridge（剑桥大学）；U.S. National Park Service Department of Interior（美国国家公园管理局）；Alaska Spatial Science（阿拉斯加空间科学）
Explainable AI for Biodiversity Monitoring and Ecological Image Analysis
Pratt School of Engineering, Duke University（杜克大学工程学院）；British Antarctic Survey（英国南极调查局）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；Duke University Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University Marine Laboratory（杜克大学海洋科学与保护分会，尼古拉斯环境学院，杜克大学海洋实验室）
Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya
Mila（米拉）；University of Texas at El Paso（得克萨斯大学埃尔帕索分校）；Integrated Centre for Mountain Development（山地发展综合中心）；Microsoft AI for Good Research Lab（微软AI公益研究实验室）；University of Wisconsin - Madison（威斯康星大学麦迪逊分校）
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）
Deep Learning Object Detection Methods for Ecological Camera Trap Data
University of Guelph（圭尔夫大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Canadian Institute for Advanced Research（加拿大高级研究所）
用于计算机视觉任务的深度学习方法有望实现相机陷阱图像数据分析的自动化。生态相机陷阱是监测生态系统动物种群的常用方法，因为它们提供对环境的持续洞察而不会侵入。然而，相机陷阱图像的分析是昂贵的，劳动密集型的，并且耗时。用于目标检测的深度学习领域的最新进展显示出自动分析相机陷阱图像的前景。在这里，我们通过训练和比较两个深度学习目标检测分类器Faster R-CNN和YOLO v2.0来展示它们的能力，以使用Reconyx Camera Trap和自标记的Gold Standard Snapshot Serengeti数据集识别，量化和定位相机陷阱图像中的动物物种。当在大型标记数据集上训练时，对象识别方法已经显示出成功。我们展示了他们的使用，在现实规模的生态数据集的背景下，通过测试，如果目标检测方法是适用于生态研究的情况下，利用迁移学习。更快的R-CNN优于YOLO v2.0，在两个数据集上的平均准确率分别为93.0%和76.7%。我们的研究结果显示，在标记相机陷阱图像的繁重任务的自动化方面迈出了有希望的步伐，这可用于提高我们对全球生态系统种群动态的理解。
YH-MINER: Multimodal Intelligent System for Natural Ecological Reef Metric Extraction
South China Sea Institute of Oceanology, Chinese Academy of Sciences（中国科学院南海海洋研究所）；National Astronomical Observatories of the Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；International Department of Affiliated High School of South China Normal University（华南师范大学附属中学国际部）；Alibaba Cloud（阿里云）
From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations
Leibniz Centre for Agricultural Landscape Research (ZALF)（莱比锡农业景观研究中心（ZALF））；Brandenburg University of Technology Cottbus–Senftenberg（勃兰登堡技术大学科特博斯-森芬根堡）
WildLIFT: Lifting monocular drone video to 3D for species-agnostic wildlife monitoring
Fondazione Bruno Kessler（布雷诺·科塞勒基金会）；University of Muenster（明斯特大学）；Max Planck Institute of Animal Behaviour（动物行为马克斯·普朗克研究所）；University of Konstanz（康斯坦茨大学）
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）
PLAS-Net: Pixel-Level Area Segmentation for UAV-Based Beach Litter Monitoring
Graduate School of Frontier Sciences, The University of Tokyo, Japan（前沿科学研究生院，东京大学，日本）；Department of Urban Informatics, Shenzhen University, China（城市信息化系，深圳大学，中国）
CamAgent: An LLM-Agent Framework for Multi-Species Camera-Trap Workflows
Beijing Normal University（北京师范大学）；National Forestry and Grassland Administration Key Laboratory for Conservation Ecology of Northeast Tiger and Leopard（国家林业和草原局东北虎豹保护生态学重点实验室）
Seeing biodiversity: perspectives in machine learning for wildlife conservation
Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；Centre for the Advanced Study of Collective Behaviour, University of Konstanz（康斯坦茨大学集体行为高级研究中心）；University of Konstanz（康斯坦茨大学）；Institute for Applied Mathematics and Information Technologies, IMATI-CNR（意大利国家研究委员会应用数学与信息技术研究所）；University of Münster（明斯特大学）；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Wageningen University（瓦赫宁根大学）；University of Bristol（布里斯托尔大学）；North Carolina State University（北卡罗来纳州立大学）；North Carolina Museum of Natural Sciences（北卡罗来纳自然科学博物馆）；Cornell Lab of Ornithology, Cornell University（康奈尔大学鸟类学实验室）；Cornell University（康奈尔大学）
Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；ICISEE, Shanghai Jiao Tong University（上海交通大学ICISEE）；School of Mathematical Sciences, Institute of Natural Sciences, MOE-LSC, CMA-Shanghai, Shanghai Jiao Tong University（上海交通大学数学科学学院）；King Abdullah University of Science and Technology（卡塔尔国王 Abdullah 科学与技术大学）
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）
