AI Mosquito Alert Challenge Dataset 2023

The dataset was created through the efforts of the Mosquito Alert team, collaborators and thousands of citizen scientists. Please credit the Mosquito Alert Community (www.mosquitoalert.com) if you use this dataset (e.g., 'Mosquito Alert dataset, downloaded from [link], CC BY-NC-SA 4.0'). T...

ver descrição completa

Detalhes bibliográficos
Autores: Bartumeus, Frederic, Garriga, Joan, Falk, Monika, Mosquito Alert
Tipo de documento: conjunto de datos
Estado:Versão publicada
Data de publicação:2025
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/400034
Acesso em linha:http://hdl.handle.net/10261/400034
Access Level:Acceso aberto
Palavra-chave:Artificial intelligence
Computer vision
Mosquito Alert
Mosquito-borne disease
AI
Citizen science
AI Challenge
Mosquito
Deep learning
id ES_bf82614fc0841da0d1483d4a0caebb05
oai_identifier_str oai:digital.csic.es:10261/400034
network_acronym_str ES
network_name_str España
repository_id_str
spelling AI Mosquito Alert Challenge Dataset 2023Bartumeus, FredericGarriga, JoanFalk, MonikaMosquito AlertArtificial intelligenceComputer visionMosquito AlertMosquito-borne diseaseAICitizen scienceAI ChallengeMosquitoDeep learningThe dataset was created through the efforts of the Mosquito Alert team, collaborators and thousands of citizen scientists. Please credit the Mosquito Alert Community (www.mosquitoalert.com) if you use this dataset (e.g., 'Mosquito Alert dataset, downloaded from [link], CC BY-NC-SA 4.0'). The intellectual property (IP) rights of this dataset belong to the Mosquito Alert team. The license is included in the file license.txt within the dataset zip file, along with the images, labels and dataset description. The dataset consists of 10357 labeled images (approximately 9.8 GB in total). Images are accompanied by a designated CSV file called: annotations.csv. The CSV files include bounding box coordinates in the format: top left and bottom right notation ("bbx_xtl", "bbx_ytl", "bbx_xbr", "bbx_ybr"). The dataset consists of six distinct classes, including species and genus levels as well as a species complex. A summary of the mosquito classes, their descriptions, and corresponding class names used in the dataset: Aedes aegypti (species level) - class name: "aegypti" Aedes albopictus (species level) - class name: "albopictus" Anopheles (genus level) - class name: "anopheles" Culex (genus level) - class name: "culex" (species classification is challenging, so it is given at the genus level) Culiseta (genus level) - class name: "culiseta" Aedes japonicus/Aedes koreicus (species complex - difficult to differentiate between the two species) - class name: "japonicus-koreicus"[Label file:] The dataset includes a single CSV file: annotations.csv, which contains all the annotations for the images. Each row in the file provides the following information: img_fName: image file name img_w: image width img_h: image height bbx_xtl: bounding box top-left x-coordinate bbx_ytl: bounding box top-left y-coordinate bbx_xbr: bounding box bottom-right x-coordinate bbx_ybr: bounding box bottom-right y-coordinate class_label: class label (e.g., 'albopictus').[Additional notes:] a broader description of the dataset and classes will be provided in the https://www.aicrowd.com/challenges/mosquitoalert-challenge-2023#dataset and https://www.youtube.com/watch?v=qSWJZUY-5DM challenge video exif information has been removed from the images for privacy protection most images contain a single mosquito with its corresponding bounding box and class label. However, in rare cases with multiple mosquitoes, only one mosquito is assigned a bounding box and label for consistency and compatibility.European Commission VEO - Versatile Emerging infectious disease Observatory: 874735Peer reviewedZenodoEuropean CommissionBartumeus, Frederic [0000-0001-6908-3797]Garriga, Joan [0000-0002-4561-7835]Falk, Monika [0009-0003-0963-5360]Garriga, JoanFalk, MonikaBartumeus, FredericConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/datasethttp://purl.org/coar/resource_type/c_ddb1Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdftext/csvhttp://hdl.handle.net/10261/400034reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/874735https://doi.org/10.5281/zenodo.15063886Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4000342026-05-22T06:33:51Z
dc.title.none.fl_str_mv AI Mosquito Alert Challenge Dataset 2023
title AI Mosquito Alert Challenge Dataset 2023
spellingShingle AI Mosquito Alert Challenge Dataset 2023
Bartumeus, Frederic
Artificial intelligence
Computer vision
Mosquito Alert
Mosquito-borne disease
AI
Citizen science
AI Challenge
Mosquito
Deep learning
title_short AI Mosquito Alert Challenge Dataset 2023
title_full AI Mosquito Alert Challenge Dataset 2023
title_fullStr AI Mosquito Alert Challenge Dataset 2023
title_full_unstemmed AI Mosquito Alert Challenge Dataset 2023
title_sort AI Mosquito Alert Challenge Dataset 2023
dc.creator.none.fl_str_mv Bartumeus, Frederic
Garriga, Joan
Falk, Monika
Mosquito Alert
author Bartumeus, Frederic
author_facet Bartumeus, Frederic
Garriga, Joan
Falk, Monika
Mosquito Alert
author_role author
author2 Garriga, Joan
Falk, Monika
Mosquito Alert
author2_role author
author
author
dc.contributor.none.fl_str_mv European Commission
Bartumeus, Frederic [0000-0001-6908-3797]
Garriga, Joan [0000-0002-4561-7835]
Falk, Monika [0009-0003-0963-5360]
Garriga, Joan
Falk, Monika
Bartumeus, Frederic
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Artificial intelligence
Computer vision
Mosquito Alert
Mosquito-borne disease
AI
Citizen science
AI Challenge
Mosquito
Deep learning
topic Artificial intelligence
Computer vision
Mosquito Alert
Mosquito-borne disease
AI
Citizen science
AI Challenge
Mosquito
Deep learning
description The dataset was created through the efforts of the Mosquito Alert team, collaborators and thousands of citizen scientists. Please credit the Mosquito Alert Community (www.mosquitoalert.com) if you use this dataset (e.g., 'Mosquito Alert dataset, downloaded from [link], CC BY-NC-SA 4.0'). The intellectual property (IP) rights of this dataset belong to the Mosquito Alert team. The license is included in the file license.txt within the dataset zip file, along with the images, labels and dataset description. The dataset consists of 10357 labeled images (approximately 9.8 GB in total). Images are accompanied by a designated CSV file called: annotations.csv. The CSV files include bounding box coordinates in the format: top left and bottom right notation ("bbx_xtl", "bbx_ytl", "bbx_xbr", "bbx_ybr"). The dataset consists of six distinct classes, including species and genus levels as well as a species complex. A summary of the mosquito classes, their descriptions, and corresponding class names used in the dataset: Aedes aegypti (species level) - class name: "aegypti" Aedes albopictus (species level) - class name: "albopictus" Anopheles (genus level) - class name: "anopheles" Culex (genus level) - class name: "culex" (species classification is challenging, so it is given at the genus level) Culiseta (genus level) - class name: "culiseta" Aedes japonicus/Aedes koreicus (species complex - difficult to differentiate between the two species) - class name: "japonicus-koreicus"
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
http://purl.org/coar/resource_type/c_ddb1
Publisher's version
info:eu-repo/semantics/publishedVersion
format dataset
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/400034
url http://hdl.handle.net/10261/400034
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/874735
https://doi.org/10.5281/zenodo.15063886
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
text/csv
dc.publisher.none.fl_str_mv Zenodo
publisher.none.fl_str_mv Zenodo
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869418397660348416
score 15,812455

Registros relacionados