Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification

Accurate identification of mosquito species is essential for effective vector control and mitigation of mosquito-borne disease outbreaks. Traditional morphological identification requires highly specialized personnel and is time-consuming, while molecular techniques can be cost-effective and dependent on comprehensive genetic information. Wing geometric morphometry has emerged as a promising alternative, leveraging detailed geometric measurements of wing shapes and vein patterns to distinguish between species and detect intraspecies variations. This paper presents a curated dataset of 18,104 mosquito wing images, collected from 10,500 mosquito specimens, annotated with extensive meta-information, designed to support research in wing geometric morphometry and the development of machine learning models, ultimately supporting efforts in vector surveillance and research.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-04-29
DOI
https://doi.org/10.1038/s41597-025-05043-3
Citations
7
Primary Topic
Morphological variations and asymmetry
Type
article
Field-Weighted Citation Impact
28.61
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification

Albert Eisenbarth, Felix Gregor Sauer, Hanna Jöst, Stéphanie Jansen et al.
7 citations
Scientific Data
Morphological variations and asymmetry
28.61
article

Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification

Albert Eisenbarth, Felix Gregor Sauer, Hanna Jöst, Stéphanie Jansen, Artin Tokatlian, Gabriela A. Garcia, Alexandra Meyer, Carmen Villacañas de Castro, Eric Agboli, Anna Heitmann, Jun Eguchi, Kristopher Nolte, Artur Jöst, Athanase Badolo, Thirumalaisamy P. Velavan, Rafael Maciel de Freitas, Tarja Viviane Dworrak, Tatiana Șuleșco, Ellen Kiel, Renke Lühken, Jonas Schmidt‐Chanasit, Magdalena Laura Wehmeyer, Wolf Peter Pfitzner, Julien Z. B. Zahouli, Norbert Becker, Ange Gatien Doumna-Ndalembouly, Joy Saathoff, Do Huy Loc
article en
7 citations

Abstract

Accurate identification of mosquito species is essential for effective vector control and mitigation of mosquito-borne disease outbreaks. Traditional morphological identification requires highly specialized personnel and is time-consuming, while molecular techniques can be cost-effective and dependent on comprehensive genetic information. Wing geometric morphometry has emerged as a promising alternative, leveraging detailed geometric measurements of wing shapes and vein patterns to distinguish between species and detect intraspecies variations. This paper presents a curated dataset of 18,104 mosquito wing images, collected from 10,500 mosquito specimens, annotated with extensive meta-information, designed to support research in wing geometric morphometry and the development of machine learning models, ultimately supporting efforts in vector surveillance and research.

Scientific DataVol. 12(1)
Universidade Federal do Rio de Janeiro (BR), Université Joseph Ki-Zerbo (BF), Carl von Ossietzky Universität Oldenburg (DE), Universität Hamburg (DE), Duy Tan University (VN), Heidelberg University (DE), Universitätsklinikum Tübingen (DE), Centre de Recherche Médicales de Lambaréné (GA), Bundeswehrkrankenhaus (DE), Swiss Centre for Scientific Research (CI), Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (BR), Vietnamese - German Center of Excellence in Medical Research (VN), Fundação Oswaldo Cruz (BR), Bernhard Nocht Institute for Tropical Medicine (DE)
Openalex Percentile: Top 0%
Morphological variations and asymmetry
28.61
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.