Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Mapping Malaria Vectors in West Africa: Drone Imagery and Deep-Learning Analysis for Targeted Vector Surveillance

Fedra Trujillano
* ORCID logo ,
Gabriel Jimenez Garay
ORCID logo ,
Hugo Alatrista-Salas
,
Isabel Byrne
ORCID logo ,
Miguel Nunez-del-Prado
ORCID logo ,
Kallista Chan
,
Edgar Manrique
,
Emilia Johnson
,
Nombre Apollinaire
,
Pierre Kouame Kouakou
,
Welbeck A. Oumbouke
,
Alfred B. Tiono
,
Moussa W. Guelbeogo
,
Jo Lines
,
Gabriel Carrasco-Escobar
,
Kimberly Fornace
Version 1 : Received: 24 March 2023 / Approved: 28 March 2023 / Online: 28 March 2023 (15:57:27 CEST)

A peer-reviewed article of this Preprint also exists.

Trujillano, F.; Jimenez Garay, G.; Alatrista-Salas, H.; Byrne, I.; Nunez-del-Prado, M.; Chan, K.; Manrique, E.; Johnson, E.; Apollinaire, N.; Kouame Kouakou, P.; Oumbouke, W.A.; Tiono, A.B.; Guelbeogo, M.W.; Lines, J.; Carrasco-Escobar, G.; Fornace, K. Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance. Remote Sens. 2023, 15, 2775. Trujillano, F.; Jimenez Garay, G.; Alatrista-Salas, H.; Byrne, I.; Nunez-del-Prado, M.; Chan, K.; Manrique, E.; Johnson, E.; Apollinaire, N.; Kouame Kouakou, P.; Oumbouke, W.A.; Tiono, A.B.; Guelbeogo, M.W.; Lines, J.; Carrasco-Escobar, G.; Fornace, K. Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance. Remote Sens. 2023, 15, 2775.

Abstract

Disease control programs need to identify breeding sites of mosquitoes which transmit malaria and other diseases to target interventions and identify environmental risk factors. Increasing availability of very high resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, we identified land cover types associated with malaria vector breeding sites in West Africa. Drone images from two malaria endemic regions in Burkina Faso and Côte d’Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region of interest-based and deep learning methods to classify these habitat types from very high resolution natural color imagery. Analysis methods achieved a dice coefficient ranging between 0.68 and 0.88 for different vector habitat types; however, this classifier consistently identified the presence of specific habitat types of interest. This establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.

Keywords

malaria vector; deep learning; image classification; drone images; epidemiological control

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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