Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
CNN-Based Estimation of Water Depth from Multispectral Drone Imagery for Mosquito Control
KTH Royal Institute of Technology, Sweden.
University of Colombo, Sri Lanka.
University of Colombo, Sri Lanka.
RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.ORCID iD: 0000-0003-4560-9541
Show others and affiliations
2023 (English)In: 2023 IEEE International Conference on Image Processing (ICIP), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 3250-3254Conference paper, Published paper (Refereed)
Abstract [en]

We present a machine learning approach that uses a custom Convolutional Neural Network (CNN) for estimating the depth of water pools from multispectral drone imagery. Using drones to obtain this information offers a cheaper, timely, and more accurate solution compared to alternative methods, such as manual inspection. This information, in turn, represents an asset to identify potential breeding sites of mosquito larvae, which grow only in shallow water pools. As a significant part of the world’s population is affected by mosquito-borne viral infections, including Dengue and Zika, identifying mosquito breeding sites is key to control their spread. Experiments with 5-band drone imagery show that our CNN-based approach is able to measure shallow water depths accurately up to a root mean square error of less than 0.5 cm, outperforming state-of-the-art Random Forest methods and empirical approaches.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 3250-3254
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:ri:diva-68550DOI: 10.1109/ICIP49359.2023.10222934OAI: oai:DiVA.org:ri-68550DiVA, id: diva2:1819203
Conference
2023 IEEE International Conference on Image Processing (ICIP)
Note

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

Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Mottola, LucaVoigt, Thiemo

Search in DiVA

By author/editor
Mottola, LucaVoigt, Thiemo
By organisation
Data Science
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 23 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf