Automated urban landscape segmentation and classification tool for eco-epidemiological surveillance using drones

Authors

DOI:

https://doi.org/10.29105/ingenierias29.101-1007

Keywords:

Urban landscape, Aedes aegypti, superpixels, machine learning, Drone

Abstract

This study proposes an automated tool for the segmentation and classification of urban land associated with Aedes aegypti breeding sites using RGB aerial imagery captured by drone in Tapachula, Chiapas, México. Under an Object-Based Image Analysis (OBIA) framework, images were segmented into superpixels using the SLIC algorithm, and statistical, edge, and texture features were extracted for each object. The dataset SetDroneDataset-TapachulaRGB was constructed (109,956 labeled superpixels, 10 classes). Among the classifiers evaluated (KNN, SVM, and MLP), the MLP neural network achieved the best overall performance (weighted F1-score) and the shortest prediction time.

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Author Biographies

Sarahi Ventura Angoa, Centro de Investigación en Matemáticas A.C.

Bachelor’s degree in Applied Mathematics from the Universidad Autónoma de Tlaxcala (UATx). Her professional interests and expertise include data science, statistics, optimization, operations research, and machine learning.

Víctor Muñiz Sánchez, Centro de Investigación en Matemáticas

Ph.D. in Computer Science from the Center for Research in Mathematics (CIMAT). He is a researcher at CIMAT’s Monterrey Unit, with expertise in machine learning and deep learning, natural language processing (NLP), spatiotemporal statistics, and complex data analysis. Level I member of Mexico’s National System of Researchers (SNII I).

Francisco Javier Hernández López, Centro de Investigación en Matemáticas A.C.

Computer Systems Engineer, graduated from ITSLP, and holds both a Master’s and a Ph.D. in Computer Science from CIMAT. Since 2014, he has served as a Researcher for Mexico affiliated with CIMAT-Mérida. His main research interests include computer vision, image and video processing, parallel computing, and machine learning. Level II member of Mexico’s National System of Researchers and Researchers (SNII II).

Rogelio Danis Lozano, Instituto Nacional de Salud Pública/Centro Regional de Investigación en Salud Pública

Ph.D. in Public Health and is an epidemiologist. Director of CRISP in Tapachula from 2018 to 2024. Level II member of Mexico’s National System of Researchers (SNII II), author of 72 scientific articles, and leader of 17 research projects. He has received distinctions including the Jorge Rosenkranz Award. He is a full professor at the School of Public Health of Mexico.

Kenia Mayela Valdez Delgado, Instituto Nacional de Salud Pública/Centro Regional de Investigación en Salud Pública

Biologist with a Master’s degree in Medical Entomology and a Ph.D. in Medical and Veterinary Entomology from the Facultad de Ciencias Biológicas de la Universidad Autónoma de Nuevo León (FCB/UANL). Head of the Department of Health Systems and is a professor at ESPM/INSP. She is a Level I member of Mexico’s National System of Researchers and Researchers (SNII I). Her research focuses on the use of emerging technologies for the surveillance and control of disease-vector mosquitoes.

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Portada: Cuadrirrotor, Centro de Investigación e Innovación en Ingeniería Aeronáutica (CIIIA) de la UANL.

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Published

2026-07-25

How to Cite

Ventura Angoa, S., Muñiz Sánchez, V., Hernández López, F. J., Danis Lozano, R., & Valdez Delgado, K. M. (2026). Automated urban landscape segmentation and classification tool for eco-epidemiological surveillance using drones. Revista Ingenierías, 29(101), 60–75. https://doi.org/10.29105/ingenierias29.101-1007

Issue

Vol. 29 No. 101 (2026): Julio-Diciembre

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Artículos

License

Copyright (c) 2026 Lcda. Sarahi Ventura Angoa, Dr. Victor Hugo Muñiz Sánchez, Dr. Francisco Javier Hernández López, Dr. Rogelio Danis Lozano, Dra. Kenia Mayela Valdez Delgado

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This work is licensed under a Creative Commons Attribution 4.0 International License.