Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Autonomous Mosquito Habitat Detection Using Satellite Imagery and
  Convolutional Neural Networks for Disease Risk Mapping   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 that 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 and are only 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 for proof of concept. 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
containing a variety of geographical features. Larger land cover variables such
as ponds/lakes, inlets, and rivers were utilized to classify mosquito habitats
while minute sites 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 for identifying large-scale mosquito habitats.

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The categories of this paper are: eess
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat