TY - GEN
T1 - GeoAI in Resource-Constrained Environments
AU - Sughiarta, Gede
AU - Kurnianingsih, Atiek
AU - Gopaladinne, Srikar Reddy
AU - Shrivastava, Sujay
AU - Gorla, Hemanth Kumar Reddy
AU - Bohlen, Marc
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper describes spatially aware Artificial Intelligence, GeoAI, tailored for small organizations such as NGOs in resource constrained contexts where access to large datasets, expensive compute infrastructure and AI expertise may be restricted. We furthermore consider future scenarios in which resource-intensive, large geospatial models may homogenize the representation of complex landscapes, and suggest strategies to prepare for this condition.
AB - This paper describes spatially aware Artificial Intelligence, GeoAI, tailored for small organizations such as NGOs in resource constrained contexts where access to large datasets, expensive compute infrastructure and AI expertise may be restricted. We furthermore consider future scenarios in which resource-intensive, large geospatial models may homogenize the representation of complex landscapes, and suggest strategies to prepare for this condition.
KW - GeoAI
KW - artificial intelligence and non-government organizations
KW - foundation models
KW - local knowledge
KW - resource constraints
UR - https://www.scopus.com/pages/publications/85215090892
U2 - 10.1109/AIxSET62544.2024.00024
DO - 10.1109/AIxSET62544.2024.00024
M3 - Conference contribution
AN - SCOPUS:85215090892
T3 - Proceedings - 2024 Conference on AI, Science, Engineering, and Technology, AIxSET 2024
SP - 129
EP - 136
BT - Proceedings - 2024 Conference on AI, Science, Engineering, and Technology, AIxSET 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conferences of AI, Science, Engineering, and Technology, AIxSET 2024
Y2 - 30 September 2024 through 2 October 2024
ER -