ResQ-AI: Intelligent Disaster Response

ResQ Assistant & Tools: Built-in assistant interface providing routing, resource tracking, and scenario simulation tools.
Dashboard Overview: Main disaster intelligence dashboard displaying live alerts, filtering options, and system summaries.
Live Disaster Map: Interactive map interface tracking hazards and incident reports across target regions.
Incident Reporting Panel: Structured form for capturing incident details, severity, and affected population estimates.
Resource & Shelter Information: Directory view displaying nearby relief camps, medical aid, and available emergency assets.
Analytics & Reports Panel: Data summary view providing historical hazard trends and impact metrics.

ResQ-AI: Intelligent Disaster Response

Inspiration

Floods in Nepal and Bihar highlighted the challenges of identifying affected areas, estimating how many people need help, and coordinating timely rescue efforts.
We wanted to bring disaster information and incident reports together so users could better understand where help may be needed.

What It Does

-Live Disaster Intelligence: Displays India-focused hazard information from GDACS, IMD, and NDMA-related sources, with 7-, 30-, and 90-day monitoring windows, an event stream, and a map.

Incident Reporting: Captures incident descriptions, affected population estimates, latitude and longitude, accessibility status, and optional reporter details and images.
AI-Assisted Prioritization: Analyzes submitted incident details and assigns a priority to support review.
Additional Tools: Includes scenario simulation, resource and shelter information, route planning, analytics, and a ResQ Assistant.

How We Built It

Built the web application using Python and Streamlit.
Integrated disaster-information sources and map-based event visualization.
Added structured incident reporting and AI-assisted prioritization.
Tested incident persistence across page refreshes and new sessions.

##Challenges

Combining information from multiple sources while maintaining reliable geographic coverage.
Distinguishing reported information from verified incidents.
Finding dependable location and availability data for nearby rescue services.
Narrowing our original global vision to an India-focused monitoring system.

What We Learned

Reliable data is just as important as connecting APIs.
Accurate locations and incident details are essential for useful response planning.
AI-generated priorities should support human review, not replace it.
A focused, testable implementation is more practical than promising capabilities that cannot yet be verified.