Logo des Repositoriums
Zur Startseite
  • English
  • Deutsch
Anmelden
  1. Startseite
  2. SuUB
  3. Forschungsdokumente
  4. Large Scale Detailed Mapping of Dengue Vector Breeding Sites Using Street View Images
 
Zitierlink URN
https://nbn-resolving.de/urn:nbn:de:gbv:46-00107025-12

Large Scale Detailed Mapping of Dengue Vector Breeding Sites Using Street View Images

Veröffentlichungsdatum
2019
Autoren
Schöning, Johannes  
Zusammenfassung
Targeted environmental and ecosystem management remain crucial in control of dengue. But providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. In this paper we present the design and implementation of a pipeline to detect potential dengue vector breeding sites from geotagged images to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of being somewhat historical so that the data can be aligned with other types of data for analysis. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Extensive analyses of the approach is carried out over three provinces in Thailand. Results show that the container density counts agree well with manual container counts, with larval survey data, and with dengue case data. To delineate conditions under which the density counts are indicative of risk, a number of factors affecting agreement with larval survey and dengue case data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale. Ultimately, we intended to include our newly proposed index in the identification of dengue high-risk areas in Thailand.
Schlagwörter
AI

; 

Machine Learning

; 

Deep Learning

; 

Dengue Vector

; 

Dengue Breeding Sites

; 

Google Street View Images

; 

Street View Images

; 

Dengue

; 

Mapping

; 

Thailand
Institution
Universität Bremen  
Fachbereich
Fachbereich 03: Mathematik/Informatik (FB 03)  
Dokumenttyp
Bericht, Report
Seitenzahl
39
Zweitveröffentlichung
Ja
Dokumentversion
Preprint
Sprache
Deutsch
Dateien
Lade...
Vorschaubild
Name

00107025-1.pdf

Size

9.01 MB

Format

Adobe PDF

Checksum

(MD5):b5f17bbd3124ba97052dc3dede07d5e9

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Datenschutzbestimmungen
  • Endnutzervereinbarung
  • Feedback schicken