Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna

Camera traps can be used to address large-scale questions in community
ecology by providing systematic data on an array of wide-ranging species.
We deployed 225 camera traps across 1,125 km2 in Serengeti National Park,
Tanzania, to evaluate spatial and temporal inter-species dynamics. The
cameras have operated continuously since 2010 and had accumulated 99,241
camera-trap days and produced 1.2 million sets of pictures by 2013.
Members of the general public classified the images via the
citizen-science website www.snapshotserengeti.org. Multiple users viewed
each image and recorded the species, number of individuals, associated
behaviours, and presence of young. Over 28,000 registered users
contributed 10.8 million classifications. We applied a simple algorithm to
aggregate these individual classifications into a final ‘consensus’
dataset, yielding a final classification for each image and a measure of
agreement among individual answers. The consensus classifications and raw
imagery provide an unparalleled opportunity to investigate multi-species
dynamics in an intact ecosystem and a valuable resource for
machine-learning and computer-vision research.

相机陷阱（camera traps）可通过采集多类广布物种的系统性观测数据，解答群落生态学中的诸多大尺度研究问题。本研究于坦桑尼亚塞伦盖蒂国家公园1125平方千米的研究区域内布设了225台相机陷阱，以探究物种间的时空动态规律。该套观测设备自2010年起持续运行，至2013年累计完成99241个相机陷阱日的有效观测，并采集了120万组影像素材。普通公众可通过公民科学平台www.snapshotserengeti.org完成影像分类标注工作：每张影像均由多名用户独立标注，并记录其中的物种、个体数量、关联行为以及幼崽存在情况。累计共有超过28000名注册用户参与标注，累计完成1080万次标注操作。本研究通过简易算法将所有独立标注结果进行聚合，生成最终的"共识"数据集，为每张影像提供最终分类结果，并量化不同标注者间的一致性程度。该共识标注数据集与原始影像素材，不仅为探究完整原生生态系统中的多物种种群动态提供了前所未有的研究契机，同时也可为机器学习与计算机视觉领域的相关研究提供宝贵的实验资源。

搜集汇总

背景与挑战

背景概述

该数据集包含在塞伦盖蒂国家公园部署的225个相机陷阱自2010年起持续采集的高频图像，覆盖40种哺乳动物，积累了120万组图片。通过公民科学平台由超过28,000名用户进行标注，并生成共识分类数据，为研究多物种生态动态和机器学习提供了宝贵资源。