Using spatial video and deep learning for automated mapping of ground-level context in relief camps
The results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.
Authors
- Jayakrishnan Ajayakumar (ORCID: https://orcid.org/0000-0001-9564-7728)
- Naveen Kannan
- John Glenn Morris (ORCID: https://orcid.org/0000-0001-9347-1984)
- Sandra Bempah (ORCID: https://orcid.org/0000-0002-5374-2951)
- Afsar Ali (ORCID: https://orcid.org/0009-0008-5419-1777)
- Felicien Masanga Maisha (ORCID: https://orcid.org/0000-0001-9827-9756)
- Andrew Curtis (ORCID: https://orcid.org/0000-0003-0956-2686)
- Grace Armstrong
Institutions
- Kent State University (US)
- University of Florida (US)
- University of Goma (CD)
- Case Western Reserve University (US)
Publication Details
- Journal
- International Journal of Health Geographics
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1186/s12942-024-00382-7
- Citations
- 2
- Primary Topic
- Video Surveillance and Tracking Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.50
Funders
- National Institutes of Health