The past decade has witnessed an increasing frequency and intensity of disasters, from
extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely …

The longer the COVID-19 pandemic lasts, the more apparent it becomes that understanding
its social drivers may be as important as understanding the virus itself. One such social …

Knowledge graphs are a rapidly growing paradigm and technology stack for integrating
large-scale, heterogeneous data in an AI-ready form, ie, combining data with the formal …

The chapter provides an overview of the methodological foundations of GeoAI, with a focus
on the use of deep learning and knowledge graphs. It covers a range of key concepts and …

Geospatial Knowledge Graphs (GeoKGs) have become integral to the growing field of
Geospatial Artificial Intelligence. Initiatives like the US National Science Foundation's Open …

In this chapter, we explain what knowledge graphs are, how they relate to GeoAI research
such as knowledge engineering and representation learning, discuss their value proposition …

It is crucial for emergency management organizations to have rapid access to relevant
experts who can advise and assist following a disaster. To improve expert-mining and …

Advances in multi-task learning (MTL) models have improved the performance and
explainability of recommender systems (RS) by jointly learning the recommendation and …

Open data initiatives and knowledge graphs, in synergy, have contributed to an increasing
volume of disaster-related data in the Semantic Web. Synthesizing and enriching these data …