Seatizen Atlas:水下和空中海洋图像的协作数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Matteo Contini, Victor Illien, Mohan Julien, Mervyn Ravitchandirane, Victor Russias, Arthur Lazennec, Thomas Chevrier, Cam Ly Rintz, Léanne Carpentier, Pierre Gogendeau, César Leblanc, Serge Bernard, Alexandre Boyer, Justine Talpaert Daudon, Sylvain Poulain, Julien Barde, Alexis Joly, Sylvain Bonhommeau
{"title":"Seatizen Atlas:水下和空中海洋图像的协作数据集。","authors":"Matteo Contini, Victor Illien, Mohan Julien, Mervyn Ravitchandirane, Victor Russias, Arthur Lazennec, Thomas Chevrier, Cam Ly Rintz, Léanne Carpentier, Pierre Gogendeau, César Leblanc, Serge Bernard, Alexandre Boyer, Justine Talpaert Daudon, Sylvain Poulain, Julien Barde, Alexis Joly, Sylvain Bonhommeau","doi":"10.1038/s41597-024-04267-z","DOIUrl":null,"url":null,"abstract":"<p><p>Citizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers. This dataset is regularly updated and contains >1.6 M images from the Southwest Indian Ocean. Most of images are geolocated, and some are annotated with 51 distinct classes (e.g. fauna, and habitats) to train AI models. The quality of these photos taken by action cameras along the trajectories of different platforms, is highly heterogeneous (due to varying speed, depth, turbidity, and perspectives) and well reflects the challenges of underwater image recognition. Data discovery and access rely on DOI assignment while data interoperability and reuse is ensured by complying with widely used community standards. The open-source data workflow is provided to ease contributions from anyone collecting pictures.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"67"},"PeriodicalIF":5.8000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733269/pdf/","citationCount":"0","resultStr":"{\"title\":\"Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery.\",\"authors\":\"Matteo Contini, Victor Illien, Mohan Julien, Mervyn Ravitchandirane, Victor Russias, Arthur Lazennec, Thomas Chevrier, Cam Ly Rintz, Léanne Carpentier, Pierre Gogendeau, César Leblanc, Serge Bernard, Alexandre Boyer, Justine Talpaert Daudon, Sylvain Poulain, Julien Barde, Alexis Joly, Sylvain Bonhommeau\",\"doi\":\"10.1038/s41597-024-04267-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Citizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers. This dataset is regularly updated and contains >1.6 M images from the Southwest Indian Ocean. Most of images are geolocated, and some are annotated with 51 distinct classes (e.g. fauna, and habitats) to train AI models. The quality of these photos taken by action cameras along the trajectories of different platforms, is highly heterogeneous (due to varying speed, depth, turbidity, and perspectives) and well reflects the challenges of underwater image recognition. Data discovery and access rely on DOI assignment while data interoperability and reuse is ensured by complying with widely used community standards. The open-source data workflow is provided to ease contributions from anyone collecting pictures.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"67\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-01-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733269/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-024-04267-z\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04267-z","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

公民科学计划通过提供全球范围和高分辨率的数据,对环境研究产生了全球性的影响。绘制海洋生物多样性地图仍然是公民倡议可以作出贡献的一项关键挑战。在这里,我们描述了一个数据集,该数据集由在热带沿海浅层地区收集的水下和空中图像组成,这些图像是通过由公民或研究人员操作的各种低成本平台收集的。该数据集定期更新,包含来自西南印度洋的160万张图像。大多数图像都是地理定位的,有些图像被标注为51个不同的类别(例如动物群和栖息地),以训练AI模型。这些由运动相机沿着不同平台的轨迹拍摄的照片的质量是高度异构的(由于不同的速度、深度、浊度和视角),很好地反映了水下图像识别的挑战。数据发现和访问依赖于DOI分配,而数据互操作性和重用则通过遵守广泛使用的社区标准来确保。提供开源数据工作流是为了方便任何人收集图片。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery.

Citizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers. This dataset is regularly updated and contains >1.6 M images from the Southwest Indian Ocean. Most of images are geolocated, and some are annotated with 51 distinct classes (e.g. fauna, and habitats) to train AI models. The quality of these photos taken by action cameras along the trajectories of different platforms, is highly heterogeneous (due to varying speed, depth, turbidity, and perspectives) and well reflects the challenges of underwater image recognition. Data discovery and access rely on DOI assignment while data interoperability and reuse is ensured by complying with widely used community standards. The open-source data workflow is provided to ease contributions from anyone collecting pictures.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信