We are losing biodiversity at an unprecedented scale and in many cases, we do not even know the basic data for the species. Traditional methods for wildlife monitoring are inadequate. Development of new computer vision tools enables the use of images as the source of information about wildlife. Social media is the rich source of wildlife images, which come with a huge bias, thus thwarting traditional population size estimate approaches. Here, we present a new framework to take into account the social media bias when using this data source to provide wildlife population size estimates. We show that, surprisingly, this is a learnable and potentially solvable problem.
Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation
Microsoft AI for Good（微软AI公益部门）；Center for Research and Formation in Artificial Intelligence, Universidad de los Andes（安第斯大学人工智能研究与形成中心）；Massachusetts Institute of Technology（麻省理工学院）
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）
Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
Microsoft AI for Good Lab, Redmond（微软人工智能公益实验室）；University of Southern California, Los Angeles（南加州大学）；Microsoft Research, Redmond（微软研究院）；Nanyang Technological University, Singapore（南洋理工大学）；Universidad de Los Andes, Colombia（洛斯安第斯大学）
Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence
University of Minnesota（明尼苏达大学）；Universidad de los Andes（安第斯大学）；Western EcoSystems Technology, ULC（西部生态系统技术有限责任公司）；Liverpool John Moores University（利物浦约翰摩尔斯大学）
How many images do I need? Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring
School of Science and Technology, University of New England（新英格兰大学科学与技术学院）；NSW Department of Primary Industries（新南威尔士州初级产业部）；School of Environmental and Rural Science, University of New England（新英格兰大学环境与农村科学学院）
Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Freiburg（弗莱堡大学）；Beihang University（北京航空航天大学）；HUST（华中科技大学）；University of Oulu（奥卢大学）；University of Alberta（阿尔伯塔大学）；Tsinghua University（清华大学）；UCLA（加州大学洛杉矶分校）；City University of Hong Kong（香港城市大学）；East China Normal University（华东师范大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
University of Wyoming（怀俄明大学）；Auburn University（奥本大学）；Harvard University（哈佛大学）；University of Oxford（牛津大学）；University of Minnesota（明尼苏达大学）；Uber AI Labs（优步人工智能实验室）
Detection of trade in products derived from threatened species using machine learning and a smartphone application
Helsinki Lab of Interdisciplinary Conservation Science, Department of Geosciences and Geography, FI-00014, University of Helsinki, Finland（赫尔辛基跨学科保护科学实验室，地理科学与地理系，芬兰赫尔辛基大学）；School of Life Sciences, University of KwaZulu-Natal, Durban 4041, South Africa（生命科学学院，南非夸祖鲁-纳塔尔大学）
