Computer Vision for Mapping Vector-Borne Disease Risk Factors in Africa
PhD defence by Venkanna Babu Guthula
Assessment Committee
Associate Professor Stéphanie Horion, Geosciences and Natural Resource Management, University of Copenhagen (Chairperson)
Professor Ioannis N. Athanasiadis, Wageningen University & Research
Professor Thomas B. Moeslund, Aalborg Universitet
Supervisors
Professor Christian Igel
Assistant Professor Stefan Oehmcke
Department
Department of Computer Science
Place
The defence is conducted as a hybrid defence.
To attend the defence in person:
Building: Pioneer Centre for AI - KU, Room: OEV3-Seminar,
Øster Voldgade 3, 1350, Copenhagen
To attend the defence online:
Please follow the link to attend the defence online: https://ucph-ku.zoom.us/j/62757785764?pwd=dM3eA58so1akvCCzLBxESdksaTaG0r.1
MeetingID, if relevant: 627 5778 5764
Password, if relevant: 129224
Email address to gain access to the thesis: vegu@di.ku.dk.
You will either receive a copy of the thesis or be informed where you can read a physical copy.
Recipients of copies of the thesis are not allowed to share or distribute it due to copyright compliance.
Short description of the thesis
Vector-borne diseases are a major global health threat. Effective control of malaria and many other diseases depends on appropriately targeted interventions. Computer vision applied to remote sensing data can support the identification and mapping of disease risk factors. This thesis develops methods for mapping three distinct risk factors associated with vector-borne diseases. The first part focuses on mapping potential Aedes mosquito larval container habitats across Dar es Salaam, Tanzania. The second part investigates methods for segmenting individual buildings and classifying roof attributes from drone and satellite imagery. The third part considers the detection of cattle in sub-Saharan Africa. Public health agencies could integrate the resulting tools and data products into various intervention strategies and allocate resources more efficiently.