AI Risk Mitigation in Disaster Response

AI Risk Mitigation in Disaster Response is sponsored by Federal Emergency Management Agency (FEMA). This grant supports applications, systems, and services utilizing artificial intelligence design and algorithms to support enhanced threat and security capability, decision-making and modeling, operational optimization, or task automation in disaster response.

Federal Emergency Management Agency – AI Use Cases | Homeland Security Homeland Security Enterprise Artificial Intelligence at DHS Federal Emergency Management Agency – AI Use Cases The Federal Emergency Management Agency (FEMA) uses AI in its day-to-day activities to help people before, during, and after disasters. Below is an overview of each AI use case within FEMA, as part of the Simplified DHS AI Use Case Inventory .

More details about these use cases are available in the Full DHS AI Use Case Inventory on the DHS AI Use Case Inventory publication library . AI use cases are listed below by deployment status. Spend Plan Analysis GPT (DHS-2709) Use Case Name: Spend Plan Analysis GPT Use Case Summary: The Spend Plan Analysis GPT leverages the Azure LLM hosted on the Azure Commercial Cloud at FEMA, ChatGPT4o model.

This tool allows users to ask questions based of the loaded data sets in common language to produce intended results to answer questions on budget and execution data leveraging both the Spend Plan and financial data as data sources. The tool also has a full audit logging feature so the user can see where the data was retrieved from and can then be validated against actual data sources, as required.

The intent of the tool is to answer complex questions around budget and execution in a more efficient manner and to reduce the number of personnel that will require extensive programming knowledge to produce similar results.

Use Case Topic Area: Procurement & Financial Management AI Classification: Agentic-AI Current Status: Pre-deployment Administrative & Productivity Support for IRC Resource Library (DHS-2712) Use Case Name: Administrative & Productivity Support for IRC Resource Library Use Case Summary: Long-term disaster recovery requires analyzing large amounts of information stored in multiple locations.

IRC typically reviews past recovery plans and projects, available federal and state funding options, considers community needs, and other recovery strategies used in the event. High-level reviews are necessary for FEMA Program Areas (IA, PA, etc.) on potential funding gaps and cost share support. Synthesizing that information from the various storage locations, including other departments, IRC SharePoint, and TRAX poses some challenges.

This use case is designed to solve these challenges by reducing inconsistency in manual research, improving access to historical knowledge, and helping staff quickly identify relevant recovery resources. The purpose of this tool is to assist in the identification of patterns in current and past disaster events. Earlier identification will expedite situational awareness and the development of recovery needs and strategies.

It allows the IRC team to define a recovery approach and deliver funding and resources that match a community’s needs more quickly. With faster decision-support for FEMA staff, more consistent analysis across regions, and better use of federal resources to support communities after disasters we are more capable of delivery of the FEMA mission.

Use Case Topic Area: Service Delivery AI Classification: Generative AI Current Status: Pre-deployment Individual Assistance Document Translation (DHS-2713) Use Case Name: Individual Assistance Document Translation Use Case Summary: When survivors apply for Individual Assistance to get help recover from disaster damages, FEMA asks for substantiating documents to validate the reported damages and to assess assistance eligibility and amount.

About 6-10% of the time, documents submitted by survivors are in a language other than English. Currently, translation services are contracted out to human translators and the contract staff create a summary of each document in English. The process takes at least 1-2 days (by contract) but can take longer during periods of high disaster activity, making survivors wait longer than needed.

Additionally, the output summary (not word for word translation) may not be enough for a caseworker to understand and process the claim. Finally, document translation is expensive, costing taxpayers more than $1. 7MM in 2024.

The goal is to automate the process such that non-English documents are translated in their entirety within a matter of minutes, reducing the wait time for the survivors drastically, enabling IA to make more rapid and fulsome decisions, and reducing the cost to the taxpayers.

Use Case Topic Area: Government Benefits Processing AI Classification: Generative AI Current Status: Pre-deployment Grants Manager Artificial Intelligence ChatBot (DHS-2717) Use Case Name: Grants Manager Artificial Intelligence ChatBot Use Case Summary: The chatbot is intended to solve inefficiencies in FEMA’s grants application review process by streamlining access to policy information, reducing the time and effort required for manual research, and simplifying the interpretation of complex policy inquiries.

It addresses the need for accurate, consistent, and accessible information to improve decision-making and enhance the efficiency of the PA Program. The AI system, powered by Azure OpenAI Services, generates outputs such as policy-based responses to user queries, concise summaries of complex policies, and historical chat records for reference.

It also provides performance insights through a dashboard, tracking usage patterns and response accuracy, and incorporates user feedback to refine its functionality. Expected benefits include increased operational efficiency, improved accuracy in policy interpretation, and cost savings for FEMA’s mission.

By providing quick, standardized responses, the chatbot supports faster and more equitable processing of grants, ensuring disaster survivors receive timely assistance. The system’s analytics and feedback mechanisms allow for continuous improvement.

Use Case Topic Area: Emergency Management AI Classification: Generative AI Current Status: Pre-deployment RTPD Division Services Desktop Automation (DHS-2718) Use Case Name: RTPD Division Services Desktop Automation Use Case Summary: Our government developers lack the capacity and consistency to produce quality code in a timely manner. Our administrative staff facilitate a multitude of processes that require manual intervention.

This requires significant overhead and is prone errors and results in simple steps or actions taking longer than necessary as requests get lost in email or other forms of communication. There is also no audit trail or logging outside of a personal email address or limited access shared mailboxes, making it difficult for others to step in a facilitate activities.

AI Coding Assistants can help identify potential issues with code, help our developers troubleshoot more quickly, and begin complex coding more efficiently. This will also enable a team of developers to build a consistency across resources and a repository of reusable code segments to speed the delivery of new features and functions. In this use case, we can expect higher quality code, reducing errors and defects in working software.

Administrative staff will be able to monitor progress vs facilitating it and focus their attention on higher value mission support activities.

Use Case Topic Area: Administrative Functions AI Classification: Generative AI Current Status: Pre-deployment AI Resume & ATS App (DHS-2721) Use Case Name: AI Resume & ATS App Use Case Summary: FEMA is using artificial intelligence to speed up and strengthen hiring during disasters, when thousands of resumes can arrive at once.

The AI Resume App automatically reads resumes, organizes the key details, compares candidates to job requirements, and produces a simple ranked list with clear reasons for each recommendation. HR reviewers keep control of decisions—the AI provides fast, consistent decision support so qualified people can be identified and hired sooner.

This capability is also intended to serve as the core of a modern, DHS wide AI Applicant Tracking System (ATS). Use Case Topic Area: Human Resources AI Classification: Natural Language Processing (NLP) Current Status: Pre-deployment Potential Impacts: Identification of potential impacts is in-progress.

Program Integrity (RRAD-PI) AI Counter-Fraud Enhancement Measures (DHS-2724) Use Case Name: Program Integrity (RRAD-PI) AI Counter-Fraud Enhancement Measures Use Case Summary: The AI use case aims to further expand and address RRAD-PI’s fraud detection and prevention challenges within disaster recovery programs.

Specifically, it seeks to mitigate fraudulent activities, identity theft, and deceptive practices that compromise program integrity, ensuring that resources are allocated efficiently and equitably to eligible individuals and entities. • Fraud Pattern Detection: Identify unusual patterns, transactions, or behaviors indicative of fraud using AI-powered anomaly detection.

• Predictive Fraud Prevention: Use machine learning models to assess fraud risk and enable proactive intervention based on historical data and behavioral trends. • Document Analysis with NLP: Automate the review of text data within applicant submitted documents to detect inconsistencies, deceptive language, or forged documents.

• Network Analysis: Map relationships between entities to uncover hidden connections and networks of fraudulent actors- using our digital data from ThreatMetrix and Akamai. • Real-Time Monitoring: Detect and halt suspicious transactions or activities in real-time. • Image/Video Verification: Validate authenticity of submitted visual evidence using AI-powered computer vision tools.

• Threat Intelligence Integration: Update fraud detection models with external threat intelligence to counter evolving schemes. • Behavioral Biometrics: Analyze user behavior patterns (e.g., typing speed, device usage) to detect anomalies and prevent identity theft or account takeovers. Using digital tools implanted at RI, possible using our tool ThreatMetrix.

• Synthetic Identity Detection: Identify synthetic identities created by combining real and fake data, often used in financial fraud. • Voice Recognition for Authentication: Verify identities and detect impersonation attempts during phone-based interactions using AI-driven voice recognition. • Deepfake Detection: Identify manipulated images, videos, or audio files that could be used to deceive or commit fraud.

• Identity Verification Automation: Automate identity verification processes using AI to cross-check submitted documents, facial recognition, and other biometric data. • Risk Scoring and Prioritization: Assign risk scores to transactions, applications, or entities, enabling prioritization of investigations.

• Dark Web Monitoring: Monitor dark web marketplaces for stolen identities, credentials, or fraud schemes targeting your organization. • Access Control and Privilege Monitoring: Monitor access to sensitive systems and flag unusual privilege escalations or unauthorized access attempts.

• Machine Learning for Adaptive Security: Implement AI systems that continuously learn and adapt to new fraud tactics, ensuring resilience against emerging threats. • Geospatial Analysis for Fraud Detection: Analyze location-based data to identify discrepancies in claims, such as mismatched disaster relief applications. Using our digital data via ThreatMetrix and Akamai.

• Cross-Agency Data Sharing and Analysis: Facilitate secure data sharing and analysis across agencies using AI to uncover fraud schemes spanning multiple jurisdictions.

Use Case Topic Area: Service Delivery AI Classification: Classical/Predictive Machine Learning Current Status: Pre-deployment Semantic Search, Summarization, and Data/Spatial Visualization for NCR Watch COP/Dashboard (DHS-2726) Use Case Name: Semantic Search, Summarization, and Data/Spatial Visualization for NCR Watch COP/Dashboard Use Case Summary: This AI system supports emergency management by automatically gathering, sorting, and summarizing large amounts of information during incidents.

Instead of analysts spending valuable time searching for and verifying data from many sources, the AI continuously scans official and unofficial channels, identifies what is important, and presents clear summaries for analysts to review. This enables analysts to focus on adding context and insight, rather than just collecting data.

As a result, decision-makers receive timely, accurate, and relevant information through a user-friendly dashboard that displays incident details and maps. This streamlined process helps everyone involved respond more quickly and effectively, improves overall understanding of the situation, and reduces the risk of missing critical information.

Use Case Topic Area: Emergency Management AI Classification: Generative AI Current Status: Pre-deployment OCFO Response Augmentation Suite (DHS-2296) Use Case Name: OCFO Response Augmentation Suite Use Case Summary: FEMA Office of the Chief Financial Officer Generative Pre-trained Transformer (OCFO GPT), Travel Policy GPT and Fiscal Policy GPT are internal Generative AI (GenAI) tools designed to support the FEMA workforce by generating initial responses to various queries.

These tools leverage relevant public and internal documents to draft preliminary responses, which are then refined prior to formal submission. They assist in the data gathering stage, but do not replace the critical review and judgement of FEMA analysts and leadership.

FEMA OCFO GPT generates initial responses to questions for the record, leveraging public and internal documents, and provides a preliminary response to the Program Office to use in their formal response to the request. It reduces the data gathering stage, saving analysts 80% of the initial effort.

Travel Policy GPT generates initial responses to questions regarding FEMA/DHS Travel Policy, including the JTR, and provides a preliminary response to the travel specialist to use in their formal response to the queries. It improves response times, saving users 80-90% of the time compared to regular engagement with the Travel Service Center.

Fiscal Policy GPT provides preliminary responses to questions regarding FEMA/DHS Fiscal Policy and will generate a draft response with references to assist FEMA internal workforce in compliance with established policy. It saves users 80-90% of the time compared to regular engagement with DHS /FEMA OCFO policy and speeds up resolution times.

These tools do not replace analysts’ work or leadership review, but enhance efficiency in data gathering and preliminary response stages.

Use Case Topic Area: Administrative Functions AI Classification: Generative AI Hazard Mitigation Assistance Chatbot (DHS-2439) Use Case Name: Hazard Mitigation Assistance Chatbot Use Case Summary: The Hazard Mitigation Assistance (HMA) Chatbot, will be developed for Internal Stakeholders Navigating HMA Grant Applications with Benefit-Cost Analysis (BCA) Assistance, Project Scoping, and Feasibility Support to serve as a centralized, intuitive user interface for staff across all Federal Emergency Management Agency (FEMA) regions and headquarters.

The AI-powered chatbot will utilizing Natural Language Processing (NLP) to provide tailored, context-specific guidance to assist FEMA HMA staff, including new hires, who face significant challenges in accessing and processing vast amounts of information related to applications and grants.

Benefits include accelerating onboarding of new hires, reducing the time to full productivity, ensuring compliance and accountability through transparency and accuracy, reducing errors, enhancing decision-making by providing access to precise citations and real-time data, and improving the quality of decisions which benefit program outcomes.

The chatbot aims to enhance overall efficiency and service delivery, aligning with FEMA's goal of improving program delivery and serving the whole community. The chatbot will provide accurate, uniform answers with citations, ensuring everyone is aligned and reducing the risk of misinterpretation of publicly available HMA data sourced from FEMA.

gov. The chatbot will provide clear, plain-language explanation, highlighting eligibility criteria, funding priorities, and application processes for each HMA program. The chatbot will quickly retrieve relevant HMA policy documents, provides precise citations, and summarizes key points. The chatbot will compile publicly available HMA data sourced from FEMA.

gov, perform analyses, and present it in a clear report with visualizations.

Use Case Topic Area: Administrative Functions AI Classification: Generative AI Current Status: Pilot (The use case has been deployed in a limited test or pilot capacity) OCFO Code Assist GPT (DHS-2441) Use Case Name: OCFO Code Assist GPT Use Case Summary: Code Assist Generative Pre-trained Transformer (GPT) is an internal facing Generative AI (GenAI) tool to augment the FEMA workforce in generating and troubleshooting existing queries in established query languages (e.g., SQL, Java, COBOL).

Users enter the language they are querying, and the Code Assist GPT then provides a proposed query based on the elements provided. If the query is unsuccessful, the tool maintains the session, allowing users to prompt for enhancements until expected results are achieved. At the end of the session, all prompts and queries are removed, and no data is stored outside of the active session.

The tool provides improves query generation and rapid iteration, saving users 80-90% of the time compared to custom query development. It also supports various computer languages to assist the data analytics community.

Use Case Topic Area: Administrative Functions AI Classification: Generative AI Executive Summary GPT (DHS-2710) Use Case Name: Executive Summary GPT Use Case Summary: Executive Summary GPT leverages the Azure LLM hosted on the Azure Commercial Cloud at FEMA, ChatGPT4o model.

The intent of the tool is to take large, complex documents and summarize them in a shorter form for easier consumption by all users and to help users move quickly to relevant sections, as needed, to gain a full understanding of relevant portions of the uploaded documents that apply to their duties or functions.

This tool increases the efficiency of users by providing them a summary of the document quickly so that they can determine relevance to their responsibilities and also assists leadership in quickly understanding the main points or concerns with any uploaded documents to more quickly enable discussion and decision support.

Use Case Topic Area: Administrative Functions AI Classification: Generative AI Technical Resource for Mitigation Programs (DHS-2711) Use Case Name: Technical Resource for Mitigation Programs Use Case Summary: The FEMA Hazard Mitigation Assistance (HMA) AI solution addresses the challenge of managing complex grant processes that currently rely on manual review of thousands of applications, modifications, and closeout packages.

Analysts must manually extract and reconcile data scattered across multiple nonstandardized systems (NEMIS, PARS, PDFs, spreadsheets), risking delays in obligation and closeout of grants. This fragmentation leads to inconsistent compliance determinations, increased audit risk, and inefficient use of limited staff resources.

The initial document scan alone takes 45-60 human minutes per modification, with full reviews requiring 1-2 human days, significantly delaying the release of mitigation funds to communities in need. The AI system produces both machine-readable and human-readable artifacts to support grant management throughout the lifecycle.

These include structured findings reports that categorize issues by scope, schedule, and budget with source citations; anomaly/discrepancy KPIs highlighting timeline gaps, invoice pattern shifts, and budget-to-scope mismatches; and compliance checklists identifying missing or non-conforming items.

For documentation support, the system generates auto-drafted Requests for Information (RFIs), lock-in letters, and closeout letters with precise regulatory citations in a professional tone. It also creates CSV exports listing flagged terms and financial variances with page references.

Additionally, the system provides on-demand answers to regulatory questions and prioritized worklists showing grants needing immediate action, supporting knowledge democratization and workflow optimization. The AI solution will deliver significant benefits to both FEMA operations and disaster-affected communities.

Operationally, it will reduce document review time by 40-70%, saving 15-20 analyst hours per week to prioritize higher-value activities requiring human judgement and stakeholder interaction. The system will enhance compliance through consistent regulatory interpretation, reducing errors and improving financial calculation accuracy.

The AI will identify eligibility concerns in real-time, reducing the risk of funding grants that do not align with federal laws, regulations, and executive orders. For the public, the AI will accelerate application review, obligation and closeout of mitigation grants, enabling states, tribes, territories, and local communities to implement risk-reduction projects sooner.

This faster release of funds directly enhances public safety and disaster resilience while providing a more consistent application experience across regions.

Use Case Topic Area: Emergency Management AI Classification: Generative AI Current Status: Pilot (The use case has been deployed in a limited test or pilot capacity) Public Assistance Workload Projections (DHS-2720) Use Case Name: Public Assistance Workload Projections Use Case Summary: The use case is predicting recovery program quantities of interest using supervised learning models to include predicting the number of applicants who will apply for Public Assistance, predicting the number of PA projects that applicants will submit, predicting the number of sites that will need to be inspected per PA project, predicting the cost of delivering assistance, etc. Supervised learning models include but are not limited to the use of sample statistics, generalized linear models, decision trees, and deep neural networks for the purpose of predicting unknown quantities.

The models will produce predictions for to-be-determined quantities of interest. These quantities are often of interest to Agency personnel in the field, region, and headquarters, as well as DHS, OMB, NSC, and the White House. In addition to being informative, the model’s predictions are likely to be used for decision making.

Projections help inform staffing levels and timing. These supervised learning models will produce point predictions for the different quantities of interest for disaster declarations. Additionally supervised learning models may produce prediction intervals or predictive distributions as feasible and appropriate for the given prediction problem.

Often these outputs will be shared via business intelligence tools (e.g., Tableau or PowerBI) for wide internal FEMA use. Some predictions may be shared to a more restricted audience through simpler means (e.g., an excel workbook) as appropriate.

Use Case Topic Area: Emergency Management AI Classification: Classical/Predictive Machine Learning Individual Assistance (IA) Predictive Models for Program Quantities (DHS-2722) Use Case Name: Individual Assistance (IA) Predictive Models for Program Quantities Use Case Summary: The use case is predicting recovery program quantities of interest using supervised learning models to include predicting the number of applicants who will apply for Individual Assistance, how many inspections will be issued, and how many units are required for direct housing.

Supervised learning models include but are not limited to the use of sample statistics, generalized linear models, decision trees, and deep neural networks for the purpose of predicting unknown quantities.

The models are intended to quickly quantify and reduce uncertainty around key quantities of interest to enable better programmatic decision making, such as workload management, pre-placement of staff, etc. Its outputs include the predicted values for the quantities of interest, e.g. number of survivors who will register for assistance, number of inspections issued, etc. Use Case Topic Area: Emergency Management AI Classification: Classical/Predictive Machine Learning Large Language Model (LLM) Guided Data Dictionary Generation (DHS-2727) Use Case Name: Large Language Model (LLM) Guided Data Dictionary Generation Use Case Summary: This LLM model utilizes a Retrieval Augmented Generation (RAG) technique for data dictionary generation by retrieving relevant provided metadata from the source system intake form and an acronym key.

The LLM then uses this context to generate clear, brief descriptions for each field. The LLM-generated data dictionary eases the burden of metadata documentation on the data stewards when integrating their data into FEMADex by creating field definitions.

Use Case Topic Area: Information Technology AI Classification: Generative AI Current Status: Pilot (The use case has been deployed in a limited test or pilot capacity) Incident Management Workforce Deployment Model (depmod) (DHS-248) Use Case Name: Incident Management Workforce Deployment Model (depmod) Use Case Summary: The Incident Management Workforce Deployment Model helps predict how FEMA's incident management team might respond to disasters.

It uses historical data to plan staffing needs but does not directly decide who gets staffed. The model is used to make predictions about how the incident management (IM) workforce could respond to the Stafford Act incidents. A key area of application is the setting of IM workforce staffing levels (i.e., force structure).

The model was constructed in R and delivered as an R package. The model uses various kinds of statistical inference and machine learning methods (i.e., multinomial regression, Bayesian multi-level modeling, etc.). This use case leverages historic data on the deployment of FEMA personnel to anticipate requirements for disaster staffing and supporting assessments for overall readiness.

It does not leverage demographic data, nor is it used to directly inform staffing decisions. It is purely used for planning and assessment purposes. The model is able to extract patterns from big data, combine these patterns in a way that mirrors a real-world process, and simulate likely outcomes to yield predictions in the form of relatable, mission-centric metrics.

The system primarily outputs predictions that can then (optionally) be summarized and communicated as recommendations.

Current Status: Retired (AI module deactivated /not used) Individual Assistance (IA) & Public Assistance (PA) Projections (DHS-251) Use Case Name: Individual Assistance (IA) & Public Assistance (PA) Projections Use Case Summary: Individual Assistance (IA) and Public Assistance (PA) Projections predicts recovery program quantities of interest using supervised learning models.

These models include, but not limited to, predicting the number of households that will register for the Individuals and Households Program (IHP), the number of households that will require direct housing assistance, the number of temporary transitional housing units needed, the number of applicants for Public Assistance, the number of PA projects that applicants will submit, the number of sites that will need to be inspected per PA project, and the cost of delivering assistance.

Supervised learning models include sample statistics, generalized linear models, decision trees, and deep neural networks.

The purpose of these supervised learning models is for informational purposes, to produce predictions for to-be-determined quantities of interest and for decisional purposes that may include implicit use (e.g., seeing a large number of households requiring assistance may motivate FEMA Chief Financial Officer (CFO) to consider requesting additional funding for the Disaster Relief Fund) or explicit use (e.g., the number of estimated housing units required may be used to determine the number of housing units to order just-in-time).

Anticipated benefits of using supervised learning models include increased predictive performance, timely predictions, quantification of uncertainty, and rigor. These supervised learning models produce predictions, not recommendations or decisions (though they can inform human users in making recommendations and decisions).

At minimum, these models will produce point predictions for the different quantities of interest for disaster declarations. Additionally, they may produce prediction intervals or predictive distributions as feasible and appropriate for the given prediction problem. Often these outputs will be shared via business intelligence tools (e.g., Tableau or PowerBI) for wide internal FEMA use.

Some predictions may be shared with a more restricted audience through simpler means (e.g., an excel workbook) as appropriate.

Current Status: Retired (T his use case has been separated into individual entries: Public Assistance Workload Projections (DHS-2720), and Individual Assistance (IA) Predictive Models for Program Quantities (DHS-2722)) Planning Assistant for Resilient Communities (PARC) (DHS-254) Use Case Name: Planning Assistant for Resilient Communities (PARC) Use Case Summary: The proposed Generative AI (GenAI) solution, Planning Assistant for Resilient Communities (PARC) will create efficiencies for the hazard mitigation planning process for local governments, including underserved communities.

Hazard mitigation plans are not only a foundational step that communities can take to build their resilience but can be lengthy to produce and challenging for communities that lack resources to do so.

PARC will specifically support State, Local, Tribal, and Territorial (SLTT) governments’ understanding of how to craft a plan that identifies risks and mitigation strategies as well as generate draft plan elements—from publicly available, well researched sources—that governments could customize to meet their needs.

This capability could lead to more communities having the ability to submit grant applications for funding to become more resilient and reduce disaster risks. The Beta Release GenAI Plan Generator (OpenAI GPT-4o) is a Large Language Model (LLM) that generates sections of the Hazard Mitigation Plans based on user inputs. It processes prompts and user responses to create detailed, regulatory-compliant Hazard Mitigation Plan sections.

Beta Release PARC Assistant (OpenAI GPT-4o) produces responses through an interactive chat assistant. The Assistant helps planners by answering questions related to the Hazard Mitigation Plan process, offering explanations of policy guidelines, and guiding users through the Federal Emergency Management Agency’s (FEMA’s) regulatory requirements.

Current Status: Retired (The AI use case's development and/or use has been discontinued) Geospatial Damage Assessments (DHS-346) Use Case Name: Geospatial Damage Assessments Use Case Summary: The Response Geospatial Office (RGO) is exploring the use of AI to assist in the prioritization of structural and debris assessments. RGO reviews satellite, aerial, and radar imagery to expedite damage assessments in the aftermath of a disaster.

RGO is utilizing several AI techniques, including computer vision, machine learning, and deep learning, to help prioritize imagery exploitation by identifying areas likely having damage or debris. Areas indicated as likely having damage or debris are prioritized for imagery exploitation by analysts, reducing the time required for visual damage assessments.

FEMA will integrate these automated tools for imagery-based damage assessments into RGO processes and systems to inform FEMA geospatial analysts. FEMA geospatial analysts will review output and develop recommendations, so FEMA can quickly detect and characterize damaged and undamaged buildings, identify concentrations of damage, detect debris, and support rapid response and recovery activities.

AI models generate points or polygons over areas where damage or debris is likely. This output is a recommendation meant to trigger investigation by human analysts via traditional damage assessments. In large-scale incidents, imagery collection can be comprised of hundreds of thousands of miles of land and millions of structures.

AI automation allows for the prioritization of imagery exploitation where damage is likely to have occurred, reducing the time required to identify impacts and summarize damage sustained.

Current Status: Retired (The AI use case's development and/or use has been discontinued) Recovery and Resilience Resource (RRR) Portal (DHS-2440) Use Case Name: Recovery and Resilience Resource Use Case Summary: The Recovery and Resilience Resource (RRR) Portal aims to simplify the process of finding and using disaster recovery and resilience resources and information for State, Local, Tribal, and Territorial (SLTT) decision-makers as well as the communities they serve.

The RRR Portal brings together technical, financial, and information assistance into one easy-to-use platform to reduce the time it takes to navigate the wide range of available resources. The non-AI layers (currently funded) will provide an intuitive user interface, a business intelligence reporting tool, and an expansive data library.

The AI-layer (currently unfunded) would include a smart matching wizard functionality via AI Large Language Model (LLM) to connect SLTT partners with the resources that meet their unique needs. Increasing access and improving navigation of federal resources will benefit a wide array of stakeholders across all sectors, including private and public.

The primary stakeholders are SLTTs that are looking to support recovery and/or resilience in communities. This includes 50 states, Washington D. C.

, 5 U.S. territories, about 80,000 local governments (including special districts), 574 federally recognized tribal governments, and their partners.

Additional stakeholders include the 25+ federal departments and agencies that are members of the Mitigation Framework Leadership Group (MitFLG), who have equity in the availability of comprehensive resources, best practices, or tools for SLTTs to recover from or increase resilience to disasters.

The smart matching wizard will be an AI LLM that analyzes user search behavior and matches recommended resources from across the expansive data library that will include resources from federal, state, local, nonprofit sectors.

Current Status: Retired (The AI use case's development and/or use has been discontinued) Digital Processing Procedure Manual (D-PPM) (DHS-2442) Use Case Name: Digital Processing Procedure Manual (D-PPM) Use Case Summary: The Digital Processing Procedures Manual (D-PPM) technology is an AI-based search that will be implemented in call centers and remote field services locations to enhance agent support, encourage proper utilization of guidance and procedures, and ensure a consistent, empathic survivor experience.

Leveraging this intelligent retrieval technology does not rely on extensive application changes and offers substantial benefits in providing round-the-clock support while prioritizing complex inquiries. This initiative aims to enhance survivor satisfaction and call