In emergency management, AI can support preparedness, mitigation, response, and recovery. While generative AI models have grown in popularity, primarily through the use of LLMs to create new data and text, like natural language, emergency managers also have access to machine learning and deep learning models for things like predictive modeling, object tracking, and imagery analysis.<\/p>\n\n ArcGIS is a foundational enterprise technology used by both business and government organizations. As a geographic information system (GIS), it provides location intelligence, contributes to the digital transformation of an organization, integrates with the Internet of Things (IoT), helps create digital twins, and enables teams and organizations to collaborate, make decisions, and act. Emergency managers have been using this system for decades to map and understand the world around them.<\/p>\n\n However, what may surprise some emergency managers is that AI in ArcGIS started with the inclusion of machine learning tools in 2008. In recent years, ArcGIS has been integrating smart assistants, the Text Segment Anything Model, vision language foundational models, and most recently, new generative AI assistants.<\/p>\n\n In nearly all of these cases, AI was architected directly into ArcGIS without the need for any additional configuration.<\/p>\n\n This means that emergency managers who have utilized GIS for data analysis, imagery analysis, and prediction modeling, have been successfully integrating AI into their programs for nearly a decade.<\/em><\/p>\n\n ArcGIS is enhanced with AI in two ways: Geospatial AI (or GeoAI) and AI assistants.<\/p>\n\n GeoAI<\/strong> advances the science of GISby using AI tools and models to extract information from imagery, video, and data for analysis. It can analyze, make predictions, and extract features from unstructured text or imagery sourced from satellite imagery, drones, aircraft, video feeds, and even mobile phones. GeoAI can also track, detect, and classify objects for additional analysis. To get started and to deploy ArcGIS deep learning packages, check out this blog article: Deep Learning with ArcGIS Pro Tips and Tricks<\/a>.<\/p>\n\n AI assistants<\/strong> enhance the ArcGIS experience by using intelligent agents and assistants that use natural language to understand the user\u2019s intent and perform GIS tasks. These assistants employ LLMs that have been trained and bound to focus on ArcGIS, and can be likened to chatbots. AI assistants retrieve knowledge from your data, collaborate with the user by sharing their understanding and clarifying things as necessary, and then suggest actions relevant to your intent.<\/p>\n\n Let\u2019s break down some scenarios where emergency managers would leverage these capabilities within their programs.<\/p>\n\n Pre-disaster<\/em><\/p>\n\n One of the more common ways to leverage AI pre-disaster is for predictive modeling and simulations. In emergency management terms, leveraging ArcGIS to simulate coastal, riverine, or flash flooding uses GeoAI capabilities to conduct predictive analysis. Or, when organizations work to identify structures in a floodplain that should be considered for mitigation projects, the building footprints visualized in map layers can leverage GeoAI\u2019s extraction capabilities. These extraction capabilities are pre-trained models to find and identify objects that look similar to one another.<\/p>\n\n For emergency managers interested in modeling how the climate may change in the future, check out the National Oceanic and Atmospheric Administration\u2019s (NOAA\u2019s) Sea Level Rise Viewer<\/a>, which is based on ArcGIS.<\/p>\n\n GeoAI can also assist emergency managers with situational awareness. For organizations with watch centers or watch desks, GeoAI pre-trained models can be used to track moving vehicles or humans visible by CCTV. This capability is often utilized in security operations centers, where vehicles and VIP protective detail personnel are tracked, and their movements visualized on a screen.<\/p>\n\n Post-disaster<\/em><\/p>\n\n Imagery analysis for damage assessments is a GeoAI capability that emergency managers use to expedite the recovery process. This capability was successfully applied in the aftermath of Hurricane Ian<\/a>, where geospatial damage assessments were conducted using artificial intelligence, crowdsourcing, and high-resolution imagery from satellite, air, and ground. This process resulted in $78.3 million in assistance to survivors without requiring an in-person inspection. <\/p>\n\n Often, satellite, drone, or aircraft lidar images are collected after an emergency, showing aerial photographs of the extent and scope of damage. Through AI\u2019s feature extraction and segmentation processes, viewers can classify and identify an object, like a building or structure, and the machine can be trained to identify similar structures throughout the image. These pre-trained models can then identify trends and patterns in the images of the disaster area and provide estimates of structure damage, extent, and debris amounts. Check out this Learn Lesson<\/a> on Classifying Objects Using Deep Learning in ArcGIS Pro.<\/p>\n\n Using pre-trained models for object classification in ArcGIS Pro, GeoAI can detect patterns from imagery. These capabilities are frequently seen and used as change detection in images, like \u201cswipe maps,\u201d<\/a> which show damage pre- and post-disaster. The models can also learn to quantify and classify the extent of building damage (i.e., minor, major, destroyed, etc.), estimate debris volumes, and blur objects that may be sensitive.<\/p>\n\n For a more interactive experience with post-disaster imagery, generative AI can be used via a chatbot in ArcGIS Pro. In this, users can prompt a map model with a question or statement in the prompt box, and the model will describe the image and trends for analysis in natural language. These capabilities are available through this Vision Language Context-Based Classification deep learning package<\/a> that bridges ArcGIS Pro and OpenAI\u2019s vision language classification models.<\/p>\n\n Lastly, ArcGIS Survey123 can be configured to extract information from a photo and used to populate data in a form, using an AI assistant and computer vision AI. Check out this video<\/a> showcasing how AI assistants can be prompted to help generate a Survey123 form, post-disaster. Auto Translate also helps to translate survey components into many different languages (see the section below on Text Translation for more details).<\/p>\n\n Search and Rescue<\/em><\/p>\n\n Search and rescue missions can be streamlined using many of the same AI capabilities as damage assessments. As consumers of upstream data\u2014like a building footprint data layer and aerial imagery collected pre- and post-disaster\u2014search and rescue teams can quickly identify and prioritize search areas using deep learning pre-trained models. These models search for trends and indicators in the images where damage is most extreme and suggest a prioritized list of geographic areas that should be searched first as an output.<\/p>\n\n As search and rescue teams are deployed, mobile operators can then utilize the mobile capabilities of Survey123, ArcGIS QuickCapture, and ArcGIS Field Maps to feed real-time data from the field to an operations center, capturing geolocation, text, images, and video. As this information is fed back into a common operating picture, search and rescue teams can swiftly and methodically ensure all affected areas are searched accordingly.<\/p>\n\n To read more about how GeoAI assists in search and rescue operations, check out this blog article<\/a> from Hurricane Ian and this animated map<\/a> showing the mission\u2019s progress over time.<\/p>\n\n Text Translation<\/em><\/p>\n\n Emergency managers frequently need to translate information into a variety of foreign languages. Preparedness materials, such as educational pamphlets, brochures, and videos explaining family and individual preparedness techniques, are often translated. Emergency alerts, messages, emails, and broadcasts are also translated to reach the community\u2019s broadest number of people.<\/p>\n\n![\"\"](\"https:\/\/uat.esri.com\/en-us\/industries\/blog\/wp-content\/uploads\/2025\/02\/Knowledge-Breakdown.png\")
<\/figure>\n\n![\"\"](\"https:\/\/uat.esri.com\/en-us\/industries\/blog\/wp-content\/uploads\/2025\/02\/For-AI-Enthusiasts.png\")
<\/figure>\n\nTwo Types of AI within ArcGIS<\/strong><\/h2>\n\n
![\"\"](\"https:\/\/uat.esri.com\/en-us\/industries\/blog\/wp-content\/uploads\/2025\/02\/AI-Enthusiasts-2.png\")
<\/figure>\n\nApplying GeoAI and AI Assistants into Emergency Management Workflows<\/strong><\/h2>\n\n
![\"\"](\"https:\/\/uat.esri.com\/en-us\/industries\/blog\/wp-content\/uploads\/2025\/02\/Insurance.png\")
<\/figure>\n\n