Title: Bridging the Last-Mile Gap in Disaster Intelligence: A Standards-Based Framework for the Utilization of Earth Observation and GeoAI
Short Biography:
Hassan Abouseada is a Senior Member of IEEE, a Space Operations Specialist, and the Director of the Frequency Coordination Department at the Egyptian Space Agency (EGSA). With over 20 years of experience in space mission management, spectrum coordination, and ITU regulatory compliance, he leads national and international efforts to ensure efficient satellite operations and frequency management.
He serves as the Chair of the IEEE SA GRSS Study Group for Disaster Management and is an active member of the GRSS Standardization Community, leading efforts to standardize remote sensing applications for disaster response and climate resilience. Additionally, he represents GRSS in updating ISO 19121, a critical international standard for Earth observation and geographic information.
Hassan is also the representative of EGSA in the IEEE Government Engagement Program on Standards (GEPS) and actively participates in UN-SPIDER Expert Meetings, contributing to global discussions on space-based disaster management. His work bridges science, policy, and standardization, advocating for harmonized satellite data acquisition to support climate change monitoring, early warning systems, and sustainable development goals.
Hassan actively contributes to global space governance, engaging with organizations such as the United Nations Office for Outer Space Affairs (UNOOSA), IEEE Standards Association, and the ITU. His expertise spans space-based disaster management, remote sensing standardization, and frequency coordination, positioning him as a key advocate for enhancing global access to critical Earth observation data.
Abstract:
Earth observation (EO) has become an increasingly important source of geospatial information for disaster risk reduction, emergency response, and early warning. At the same time, advances in GeoAI are creating new opportunities to derive information from increasingly diverse and heterogeneous Earth observation datasets. However, data availability and analytical capabilities do not ensure that disaster-management organizations can obtain information that is understandable, traceable, and suitable for operational decision-making. A persistent “last-mile” gap therefore exists between EO and GeoAI capabilities and their effective utilization by disaster-management users. This paper develops a conceptual, standards-based framework for addressing this gap. Rather than proposing a new artificial intelligence algorithm or software platform, the paper examines the information and interoperability requirements that arise along the pathway from disaster-management needs to Earth observation data, analytical processing, and actionable disaster information. The framework considers key dimensions, including user requirements, data and service discovery, semantic interoperability, metadata, data provenance, information quality, uncertainty, and communicating analytical outputs to decision-makers. It pays particular attention to the role of standards in enabling consistent interaction among heterogeneous EO data sources, GeoAI-based analytical capabilities, and organizations operating at different institutional and geographic scales. The paper also considers multi-hazard disaster-management scenarios to illustrate how differences in user needs and information requirements can create interoperability and standardization gaps. The analysis is discussed in the context of emerging efforts to establish common practices for the utilization of Earth observation data and services in multi-hazard disaster management and early warning. The paper argues that effectively integrating GeoAI into disaster-management workflows requires not only advances in analytical methods but also a standards-oriented foundation for describing user needs, data, analytical processes, uncertainty, provenance, and resulting information products. Such an approach can help move the disaster community from technically available EO and GeoAI capabilities toward more interoperable, transparent, and actionable disaster intelligence.
Keywords: Earth Observation, GeoAI, Disaster Management, Early Warning, Geospatial Intelligence, Interoperability, Multi-Hazard Risk.