Current oil spill emergency response protocols employ standardized, universal strategies that fail to account for critical environmental variations, particularly water temperature effects on oil weathering behaviour. This research addresses this operational gap through comparative
The presentation will touch down on how earth observation data is currently used to detect building damages following an earthquake event. It will focus on the use of damage detection deep learning algorithms with very high resolution
Earthquake-induced ground deformation and slow-moving geohazards such as subsidence and landslides pose significant risks across tectonically active and rapidly urbanizing regions. Through real case studies, the session will demonstrate how multi-temporal
Overview on how SAR data contributes to Geohazards mapping and Damage Assessment Mapping, indicating some initiatives, software and platforms that help its exploitation
The webinar will provide an update on ESA’s P‑band SAR mission one year after launch, highlighting its key characteristics and in‑orbit performance. It will showcase the first scientific results, including early insights into biomass
The rapid expansion of Earth observation data from satellites, aerial platforms, and cloud-based archives has created unprecedented opportunities for large-scale environmental monitoring and analysis. At the same time, the volume, complexity, and velocity of these data demand new
Applications of various AI-based models for environmental management, including deforestation monitoring, urban tree cover change and canopy height prediction, forest health assessment, nature resources management
Join us for an introductory webinar highlighting the latest updates and capabilities of the GIQ platform and related UAE space ecosystem initiatives supporting academic, startup, and R&D entities. The session will showcase how researchers
AI/ML plays a crucial role in agriculture analytics, especially in geospatial crop monitoring and mapping. In your research-based talk, machine learning models will be covered for classifying crops using multi-sensor temporal datasets such as optical
