AI-OBSERVER: Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence


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Project title
AI-OBSERVER: Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence
Budget
1,489,379€
Project Coordinator
Status
Completed
Start date
01-10-2022
Expected Completion
30-09-2025
 
Funder
EC
Funding Program
Horizon Europe
OpenAire ID
info:eu-repo/grantAgreement/EC/HE/101079468
Participant
Abstract
Artificial Intelligence (AI) has a major impact on many sectors and its influence is predicted to expand rapidly in the coming years. One area where there is considerable untapped potential for AI is in the field of Earth Observation (EO), where it can be used to manage large datasets, find new insights in data and generate new products and services. AI is one of the missing core areas that need to be integrated in the EO capabilities of the ERATOSTHENES Centre of Excellence (ECoE). AI-OBSERVER project aims to significantly strengthen and stimulate the scientific excellence and innovation capacity, as well as the research management and administrative skills of the ECoE, through several capacity building activities on AI for EO applications in the Disaster Risk Reduction thematic area.
 
Keyword(s)
Artificial Intelligence
Earth Observation
Disaster Risk Reduction

Publications
(All)

Results 21-23 of 23 (Search time: 0.001 seconds).

Issue DateTitleAuthor(s)
211-May-2025A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion MonitoringChristofi, Demetris A.D. ; Mettas, Christodoulos ; Evagorou, Evagoras S. ; Stylianou, Neophytos ; Eliades, Marinos ; Theocharidis, Christos ; Chatzipavlis, Antonis ; Hasiotis, Thomas ; Hadjimitsis, Diofantos G. 
222025Spatio-Temporal Diffusion Model for Satellite ImageryJavanmardi, Alireza ; Jaiswal, Pragati ; Pagani, Alain ; Reis, Gerd 
2315-Sep-2025Synthesis of PRISMA data from Landsat 8 or 9 data using machine learning toolsGjerazi, Ari ; La Pegna, Valeria ; Del Frate, Fabio