Find Features - AI-Assisted Vigilance for Mosquito Control and Anticipatory Action HRC - La Paz, Honduras (1) Cruz Roja Hondureña
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La Paz, Honduras
Cruz Roja Hondureña
Oct 15, 2026

Project overview
**### Español**
Este proyecto se basa en una iniciativa en curso de la Cruz Roja Hondureña (CRH), la Cruz Roja Alemana (GRC) y HeiGIT para detectar y cartografiar recipientes que acumulan agua y que sirven como criaderos del mosquito Aedes aegypti en comunidades de los alrededores de La Paz, Honduras. Las zonas prioritarias serán inspeccionadas mediante imágenes aéreas de alta resolución capturadas con drones, con el fin de generar ortofotos georreferenciadas que permitan entrenar un modelo de inteligencia artificial para la detección de objetos, capaz de identificar recipientes individuales, como tanques, cubetas y llantas. Los resultados del modelo se utilizarán para elaborar mapas de densidad que muestren dónde se concentran los posibles recipientes que sirven como criaderos de mosquitos. Esto permitirá a la Cruz Roja Hondureña optimizar y orientar mejor sus actividades actuales de sensibilización y eliminación de criaderos, en lugar de depender únicamente de mensajes generales.
Ayudar a las comunidades a concentrar sus esfuerzos precisamente en los lugares donde más se necesitan puede marcar una gran diferencia en la lucha contra enfermedades transmitidas por Aedes aegypti, como el dengue y el zika, en La Paz. Este proyecto fortalecerá tanto la respuesta inmediata para el control de mosquitos en La Paz como el ecosistema más amplio de cartografía humanitaria abierta.
**### English**
This project builds on an ongoing initiative by the Honduran Red Cross (HRC), the German Red Cross (GRC), and HeiGIT to detect and map water-holding containers that serve as breeding sites for Aedes aegypti mosquitoes in communities around La Paz, Honduras. Priority areas will be surveyed with high-resolution drone imagery to produce georeferenced orthophotos to train an AI object-detection model capable of identifying individual containers (e.g. tanks, buckets, tyres). The model outputs will be used to create density maps of where potential mosquito-breeding containers are concentrated, leading the Honduran Red Cross to make its existing awareness-raising and source-reduction activities more efficient and better targeted, rather than relying on generic messaging alone. Helping communities focus their effort exactly where it's needed most can make a real difference in the fight against Aedes aegypti-borne diseases like dengue and Zika in La Paz.
This project strengthens both the immediate mosquito-control response in La Paz and the broader open humanitarian mapping ecosystem.
2
km2
31
Contributors
Project Completion
20% completed
Last updated: Oct 16, 2026
Download the data
Below you'll find the data downloads for this MapSwipe project, including a GeoJSON file that can be imported into the HOT Tasking Manager for more detailed mapping of the area. If you need more information or if you have a special request related to MapSwipe data get in contact with the team at the Heidelberg Institute for Geoinformation Technology
Aggregated Results
Aggregated Results. This gives you the unfiltered MapSwipe results aggregated on the task level. This is most suited if you want to apply some custom data processing with the MapSwipe data, e.g. select only specific tasks. Check our documentation for more details. (Note that you need to unzip this .gz file before you can use it.)
Aggregated Results (with Geometry)
Aggregated Results. This gives you the unfiltered MapSwipe results aggregated on the task level. This is most suited if you want to apply some custom data processing with the MapSwipe data, e.g. select only specific tasks. Check our documentation for more details. (Note that you need to unzip this .gz file before you can use it.)
HOT Tasking Manager Geometries
This dataset contains shapes that are ready to use in the HOT Tasking Manager. Currently, the geometries consist of maximum 15 MapSwipe Tasks, where at least 35% of all users indicated the presence of a building by classifying as "yes" or "maybe"
Our license
This project is part of the OpenStreetMap community. The goal is high-quality geographical data, freely accessible and available to everyone. OSM’s reciprocal license protects the data from being appropriated by services that do not share back to OSM.
MapSwipe is released under a "liberal" non-reciprocal license (Creative Commons Attribution). Whenever you want to use the data, just make sure to credit the MapSwipe contributors.
