From Satellite to Signal: Building a Geospatial Agentic AI Stack

*CROSS POST EVENT FROM WHEROBOTS. SIGN UP LINK: Sign up here: https://luma.com/m4bngiey # A hands-on workshop with AWS and Wherobots, plus a fireside chat with HERE Technologies ## Agenda 10:00 to 10:30am: Doors open and check-in 10:30 to 11:00am: Fireside chat: Precise answers from probabilistic AI 11:00am to 12:30pm: Hands-on workshop 12:30 to 1:30pm: Lunch and networking ## Fireside Chat: Precise Answers from Probabilistic AI [Ben Pruden](https://www.linkedin.com/in/benpruden/?utm_source=luma), Head of Go-to-Market at Wherobots, and [Hervé Utheza](https://www.linkedin.com/in/utheza/?utm_source=luma), Senior Director, AI & Emerging Technologies at HERE Technologies, on what it takes for AI to give exact answers about the physical world. Large language models are probabilistic. Ask the same question twice and you get two different ballpark answers. Questions about the physical world, like which properties sit inside a flood zone or how many people live within a wildfire hazard area, need exact, repeatable results. Ben and Hervé discuss how AI agents get there by calling deterministic geospatial tools. ## Workshop: Building a Geospatial Agentic AI Stack on AWS and Wherobots Build an end-to-end agentic geospatial pipeline in a single day. Bring your laptop, leave with a working pattern for turning raw satellite imagery and weather data into interactive risk dashboards, driven by natural language at every stage. This in-person workshop walks you through building an end-to-end geospatial AI pipeline. Raw satellite imagery and weather data go in, move through agentic data engineering and risk scoring, and come out as interactive map dashboards. You use natural language at every stage. By the end of the workshop you will be able to: Work with Amazon Kiro for AI coding to build and interact with Wherobots and AWS. *Use the Wherobots MCP server as an agentic interface…