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\begin{document}

\title{Novel Approach to Autonomous Mosquito Habitat Detection using Satellite
Imagery and Convolutional Neural Networks for Disease Risk Mapping}



\author[1]{Sriram Elango}%
\author[1]{Nandini Ramachandran}%
\affil[1]{The University of Texas at Austin}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\begin{abstract}
Mosquitoes are known vectors for disease transmission that cause over
one million deaths globally each year. The majority of natural mosquito
habitats are areas containing standing water such as ponds, lakes, and
marshes. These habitats are challenging to detect using conventional
ground-based technology on a macro scale. Contemporary approaches, such
as drones, UAVs, and other aerial imaging technology are costly when
implemented. Multispectral imaging technology such as Lidar is most
accurate on a finer spatial scale whereas the proposed convolutional
neural network(CNN) approach can be applied for disease risk mapping and
further guide preventative efforts on a more global scale. By assessing
the performance of autonomous mosquito habitat detection technology, the
transmission of mosquito borne diseases can be prevented in a
cost-effective manner. This approach aims to identify the spatiotemporal
distribution of mosquito habitats in extensive areas that are difficult
to survey using ground-based technology by employing computer vision on
satellite imagery. The research presents an evaluation and the results
of 3 different CNN models to determine their accuracy of predicting
large-scale mosquito habitats. For this approach, a dataset was
constructed utilizing Google Earth satellite imagery containing a
variety of geographical features in residential neighborhoods as well as
cities across the world. Larger land cover variables such as
ponds/lakes, inlets, and rivers were utilized to classify mosquito
habitats while minute sites such as puddles, footprints, and additional
human-produced mosquito habitats were omitted for higher accuracy on a
larger scale. Using the dataset, multiple CNN networks were trained and
evaluated for accuracy of habitat prediction. Utilizing a CNN-based
approach on readily available satellite imagery is cost-effective and
scalable, unlike most aerial imaging technology. Testing revealed that
YOLOv4 obtained greater accuracy in mosquito habitat detection than
YOLOR or YOLOv5 for identifying large-scale mosquito habitats. YOLOv4 is
found to be a viable method for global mosquito habitat detection and
surveillance.%
\end{abstract}%



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