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MM4Drone: A Multi-spectral Image and mmWave Radar Approach for Identifying Mosquito Breeding Grounds via Aerial Drones

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Pervasive Computing Technologies for Healthcare (PH 2022)

Abstract

Mosquitoes spread disases such as Dengue and Zika that affect a significant portion of the world population. One approach to hamper the spread of the disases is to identify the mosquitoes’ breeding places. Recent studies use drones to detect breeding sites, due to their low cost and flexibility. In this paper, we investigate the applicability of drone-based multi-spectral imagery and mmWave radios to discover breeding habitats. Our approach is based on the detection of water bodies. We introduce our Faster R-CNN-MSWD, an extended version of the Faster R-CNN object detection network, which can be used to identify water retention areas in both urban and rural settings using multi-spectral images. We also show promising results for estimating extreme shallow water depth using drone-based multi-spectral images. Further, we present an approach to detect water with mmWave radios from drones. Finally, we emphasize the importance of fusing the data of the two sensors and outline future research directions.

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Notes

  1. 1.

    https://landsat.gsfc.nasa.gov/data/.

  2. 2.

    https://github.com/you359/Keras-FasterRCNN.

  3. 3.

    https://github.com/amweerasekara/mmWave-IWR1843Boost-UART-Data-Recorder.

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Acknowledgements

This work has been partly funded by Digital Futures and the Swedish Research Council (Grant 2018-05024).

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Authors and Affiliations

  1. University of Colombo, School of Computing, Colombo, Sri Lanka

    K. T. Y. Mahima, Malith Weerasekara, Kasun De Zoysa & Chamath Keppitiyagama

  2. KTH Royal Institute of Technology, Electrical Engineering and Computer Science, Stockholm, Sweden

    Markus Flierl

  3. RISE Research Institutes of Sweden, Borås, Sweden

    Luca Mottola & Thiemo Voigt

  4. Department of Information Technology, Uppsala University, Uppsala, Sweden

    Luca Mottola & Thiemo Voigt

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  1. K. T. Y. Mahima
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  4. Chamath Keppitiyagama
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Correspondence to K. T. Y. Mahima .

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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  2. Centre for Research and Technology Hellas, Thessaloniki, Greece

    Andreas Triantafyllidis

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Mahima, K.T.Y. et al. (2023). MM4Drone: A Multi-spectral Image and mmWave Radar Approach for Identifying Mosquito Breeding Grounds via Aerial Drones. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_27

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  • DOI: https://doi.org/10.1007/978-3-031-34586-9_27

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