Geospatial Readiness for a Changing Climate

Extreme weather events are testing the limits of how cities and communities prepare for, respond to, and recover from disasters. In this session, presenters showcase how high-resolution imaging, UAV data collection, and AI-powered analytics are transforming emergency management—from rapid post-event assessment to long-term resilience planning. Through case studies spanning tornado recovery in St. Louis and hurricane resilience efforts in The Bahamas, attendees will see how advanced aerial imaging, feature extraction, and digital twin technologies enable faster situational awareness and data-driven recovery. Presentations will also explore how integrating UAV photogrammetry with AI-based terrain modeling supports real-time risk mapping, community engagement, and local capacity building.

Together, these projects illustrate how geospatial innovation is closing the gap between crisis response and continuity planning—empowering municipalities and coastal communities alike to adapt, rebuild, and thrive in a changing climate.

The following presentations will be shared in this session:

From Crisis to Continuity: High-Resolution Emergency Response and Digital Twin Integration for Municipal Resilience

Presented by Jamie Reford, Phase One, and Scott Merritt, Surdex Corporation

The May 16, 2025 tornado that struck St. Louis created an unprecedented opportunity to demonstrate how cutting-edge aerial imaging technology, AI-powered feature extraction, and comprehensive digital twin planning can transform emergency response and long-term municipal resilience. This presentation showcases the complete workflow from immediate crisis response to comprehensive urban digital modeling, highlighting how Phase One’s imaging systems and Surdex’s geospatial expertise delivered actionable intelligence within 48 hours and established the foundation for a city-wide digital twin.

This session presents a real-world case study of the complete emergency response-to-recovery pipeline, demonstrating how high-resolution imaging capabilities enable both immediate damage assessment and long-term municipal planning through integrated AI/ML workflows and digital twin implementation.

Integrating ArcGIS Pro’s MaxEnt and Forest-Based Machine Learning Tools for Rapid Post-Hurricane Helene Landslide Susceptibility Mapping

Presented by Grace Braver, East Tennessee State University

Landslides triggered by extreme rainfall events pose increasing threats to infrastructure, transportation networks, and communities across the Southern Appalachian region. This project focuses on the Nolichucky headwaters in East Tennessee, which experienced widespread slope failures following Hurricane Helene (2024). Leveraging ArcGIS Pro’s integrated machine learning environment, this study compares two predictive modeling approaches: Maximum Entropy (MaxEnt) and Forest-based & Boosted Classification and Regression (FBCR) to map landslide susceptibility and assess hazard potential across the area. Both models were developed entirely within ArcGIS Pro, eliminating the need for external coding environments and enabling rapid model iteration. Environmental predictor variables included lidar-derived terrain indices (slope, curvature, roughness, elevation, aspect), soil erodibility (K-factor), geologic units, and NDVI from post-Helene Sentinel-2 imagery. Model performance was evaluated using AUC-ROC, and confusion matrix outputs to assess predictive accuracy and interpret variable importance.

The resulting susceptibility maps identify high-risk slope zones and infrastructure corridors vulnerable to debris flows during extreme rainfall. By comparing MaxEnt’s presence-only predictive framework with the ensemble capabilities of FBCR, this work demonstrates how GIS-integrated machine learning tools can provide fast, data-driven risk assessments for real-world applications in hazard mitigation, infrastructure planning, and emergency management.

This work highlights the growing potential of ArcGIS Pro as a full-stack geospatial modeling platform, enabling agencies and private industry partners to operationalize machine learning and remote sensing data for resilient terrain and infrastructure management across mountainous regions. Results demonstrate that both the MaxEnt and FBCR offer reliable, scalable approaches for assessing landslide risk in data-limited, geologically complex terrains. These tools support future efforts in predictive risk mapping, emergency response planning, and climate-resilient infrastructure development in the southern Appalachians.

Reality Mapping Fighting Urban Heat Islands

Presented by Arkadiusz Szadkowski, Esri, and Kamil Wojcik, IGI Systems

Cities are heating up and the impacts are not evenly distributed. As urban heat islands intensify, planners need solid spatial evidence to convince decision-makers, guide interventions, and prioritize the health of citizens most at risk.

This presentation showcases an integrated geospatial approach to urban heat resilience. By capturing the city in 3D using a combination of aerial imaging sensors to create a thermal-aware digital twin of the urban environment.

This model reveals where heat builds up, who is most exposed, and where interventions will be most effective — without conflicting with existing infrastructure or utilities. Through thermal imaging combined shadow analysis, solar exposure mapping, and full 3D volume analysis of buildings and vegetation, planners gain a powerful decision-support tool that bridges the gap between science, policy, and design. This is not just about visualizing heat — it’s about using reality-based data in combination with GIS to act faster, smarter, and more precisely in the face of rising temperatures.

Geospatial Readiness for a Changing Climate – Solar Construction Perspective

Presented by Eric Ebert, Falcon Unmanned Systems

When we talk about geospatial readiness for a changing climate in solar construction, it really comes down to seeing problems early and often. Solar sites are massive, exposed, and constantly changing—and with today’s weather patterns, things can shift overnight. A single rain event can change drainage, move material, or set a project back days if it isn’t caught quickly.

At Falcon Unmanned Systems, we run daily autonomous drone dock flights over active solar construction sites using photogrammetry. That daily cadence gives construction teams a clear, up-to-date picture of what actually happened on site—yesterday, not last month. We’re capturing grading progress, access roads, erosion, ponding, laydown areas, and installation alignment as they evolve in real conditions.

Photogrammetry gives us more than just visuals. It creates a consistent record that teams can measure against—cut and fill, material movement, slope changes, and site readiness. When weather hits hard, which is happening more often now, crews can immediately see where water is sitting, where erosion is starting, and where corrective work needs to happen before it becomes a bigger issue.

Because these flights are dock-based and automated, they happen whether someone is physically on site or not. That matters when sites are remote, spread out, or temporarily inaccessible due to weather. The data keeps flowing, and construction teams stay informed without waiting for boots on the ground.

For solar construction, geospatial readiness isn’t about fancy dashboards—it’s about keeping projects moving. Daily photogrammetry allows teams to react faster, protect schedules, reduce rework, and build sites that are better prepared for the realities of a changing climate. It turns aerial data into a practical construction tool, not just a deliverable.