Mapping Urban Heat Risk Without Coding | GriidAI Workflow Demo

In this GriidAI use case, we demonstrate a fully automated, human-in-the-loop workflow for developing a Chronic Heat Risk Index (HRI) for urban planning and climate resilience, using Jeddah, Saudi Arabia, as our case study. Watch as we direct the AI engine to integrate 40 years of temperature data, land cover classifications, and population density to identify extreme heat clusters—without writing a single line of code. 🔍 Workflow Breakdown: -Intelligent Data Integration: The agent ingests open-source data (Landsat LST from 1984–2025, ESA WorldCover, and WorldPop), automatically verifying spatial alignment, resolution, and coordinate systems before processing begins. -High-Precision Grid Modeling: Instead of heavy, failure-prone raster stacking, the system dynamically converts the data into a 30m point grid for efficient, highly precise calculations. -Advanced Index Development: The engine normalizes and calculates the three pillars of heat risk: Exposure (40-year temperature anomalies), Adaptive Capacity (vegetation vs. built-up land), and Sensitivity (population redistributed to habitable zones). -Decision-Ready Intelligence: The calculated HRI is converted back into a thematic raster, successfully isolating 46 extreme heat clusters and generating targeted mitigation strategies (cool roofs, tree canopy expansion). This workflow empowers urban designers and climate scientists to accelerate environmental mitigation strategies while retaining complete control over the analytical logic. 🛡️ Command AI with the Architecture of Certainty. GriidAI puts the human expert firmly in the loop. Our orchestration engine builds the analytical plan, but it waits for your validation before executing the science. 👉 Step into the Spatial Age. Get your access to GriidAi:https://griidai.com/