Projects
Below are highlights of a few projects I have worked on over the years.
Publications
Peer-Reviewed Articles
Carrera, D., Ovienmhada, U., Hussein, S., Soden, R. (2023). "The Unseen Landscape of Abolitionism: Examining the role of digital maps in grassroots organizing". In CSCW ’23: ACM CSCW Conference on Computer Supported Cooperative Work, October 14-18, 2023, Minneapolis, Minnesota. New York, NY, USA. Accepted.
Ovienmhada, U., Mouftaou, F., Wood, D (2021). “Inclusive Design of Earth Observation Decision Support Systems: A case study of Lake Nokoue”. Frontiers in Climate.
Articles - Under Review
Ovienmhada, U., Pellow, D., Wood, D. (2023). "Satellite Data for Environmental Justice: Perspectives from Anti-prison Activists on the uses of Geospatial Data". Environmental Justice. Under Review.
Carter, T. S., Kerr, G. H., Amini, H., Martin, R. V., Ovienmhada, U., Schwartz, J., van Donkelaar, A., & Anenberg, S. C. PM2.5 data inputs alter identification of disadvantaged communities. Environmental Research Letters. Under Review
Bennett, M., Gleason, C.J., Alvarez León, L.F., Friedrich, H. , Mathews, A., Ovenmhada, U., Tellman, B. (2023). "Bringing satellites down to Earth: Six steps to more ethical remote sensing". Global Environmental Change Advances. Under Review
Articles - In Prep
Ovienmhada, U*, Sayyed, T.K.*, Kashani, M*, Vohra, K., Kerr, G., O’Donnell, C., Harris, M., Gladson, L., Titus, A., Adamo, S., Fong, K., Gargulinski, E., Soja, A., Anenberg, S., and Kuwayama, Y. (2023). "Satellite Data for Environmental Justice: A Scoping Review". In Prep. (* indicates co-first authorship)
Ovienmhada, U., West, A., Wood, D. (2023). "Behind Bars and Under the Radar: How PM 2.5 Data inputs alter identification of pollution burdened U.S. Carceral Facilities". In Prep.
Ovienmhada, U., Hines, M., Wood, D. (2023). "Burning Behind Bars: Using Satellite Remote Sensing to Estimate Internal Extreme Temperatures in Texas Prisons". In Prep.
Ovienmhada, U., Lombardo, S., Baltezar, P., Wood, D. (2023). "Multi-sensor Change Detection and Machine Learning for Water Hyacinth Identification: A Case Study of Benin". In Prep.