“AI really helps us see things that we can’t see [in an image or in text] to help us learn new workflows in each model. […] It helps accelerate our reading capabilities, analysis and creation of different layers.” - Rami Alouta, ESRI.
AI is primarily used in geospatial analysis in two key ways: (1) enhancing GIS workflows by rapidly automating data extraction at scale, and (2) powering AI assistants and agents that understand user intent, generate insights, perform GIS tasks and create geospatial content. The AI for Good webinar, “Unlocking the Power of Geospatial Artificial Intelligence for humanitarian use cases,” explores how GeoAI solutions can accelerate geospatial analysis across a wide range of applications. In this session, Rami Alouta, Geospatial Enterprise Systems Expert at ESRI, explained how AI integrates complex ML and deep learning workflows to enable cohesive analysis of large-scale geospatial data.
Over 150 pre-trained models and foundational models to create, manage and analyse spatial and geographic data are currently available on ArcGIS, a growing open-source deep learning library developed by ESRI. These models provide powerful, ready-to-use tools, such as flood detection, imagery and remote sensing, and time-series forecasting, that allow users to leverage AI to prevent and mitigate humanitarian harms. Object detection and text extraction features accelerate the analysis of multidimensional models to uncover hidden insights that enhance decision-making and support timely intervention.