ResQintel: AI-Powered All-in-One Disaster App

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📌 Overview

ResQintel (Rescue Intel) is a full-stack mobile application designed to empower Filipino citizens with real-time information, preparedness guides, and emergency alerts for a wide range of disasters, including fires, typhoons, and earthquakes. Leveraging the power of artificial intelligence, image recognition, and cloud technologies, ResQintel serves as an intelligent, inclusive, and proactive disaster management platform.

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🧠 Problem Statement

The Philippines faces frequent natural and man-made disasters such as typhoons, fires, and earthquakes. These catastrophes often result in loss of lives and property, especially in vulnerable communities, due to:

• Limited early warning systems
• Delayed emergency response
• Lack of localized, real-time data
• Fragmented disaster management operations

Current solutions tend to be reactive rather than proactive. ResQintel aims to bridge these gaps through a unified, AI-powered mobile application.

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🎯 Project Objectives

1. Fire Detection AI
   Develop an AI-based fire detection module using image classification technologies like YOLOv11 and TensorFlow.

2. Educational Disaster Materials
   Provide localized and age-appropriate educational resources to teach pre- and post-disaster safety protocols.

3. Typhoon Monitoring & Geo-mapping
   Monitor typhoon activity using weather APIs and visualize impact areas by province and municipality.

4. Real-time Notifications & Alerts
   Automatically send alerts to users and responders during emergencies, reducing response time and potential casualties.

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👥 Target Users

• Students (All levels)
• Teenagers and Young Adults
• Middle-aged Individuals and Senior Citizens
• Civilians in both Urban and Rural Areas
• Local Government Units (LGUs) & Emergency Responders

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🔍 Project Scope

✅ Included Features

• AI-based fire detection through camera/image input
• Typhoon tracking with real-time map-based impact zones
• Earthquake risk awareness and safety checklists
• Educational modules tailored by age group
• Real-time alerts for nearby hazards
• Automated reports sent to responders
• Multi-language interface (Tagalog, English, Local Dialects)
• Configurable settings for user-specific disaster responses

❌ Excluded

• Direct integration with satellite communication systems
• Manual input of emergency data by users
• Government-level response dispatch integration (Phase 2)

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🛠 Technologies To Be Used

Layer	Tools/Technologies
Mobile Frontend	Flutter
Backend	Firebase, YOLOv11, TensorFlow
Database	Firebase Firestore, Google Cloud Platform
APIs / Libraries	Google Maps API, Text Recognition API, Image Classifier, Gemma, Gemini
Dataset Source	Kaggle

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⚠️ Anticipated Challenges

1. Training AI Fire Detection Model

   • Difficulty in obtaining high-quality fire datasets
   • Balancing performance with resource constraints on mobile

2. Data Collection & Curation

   • Ensuring diverse and inclusive datasets for multiple disaster types
   • Processing accurate and verified local information

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📅 Initial Timeline (8 Weeks Plan)

Week	Activity
Week 1–2	Planning & Requirements Gathering
Week 3–4	UI/UX Design
Week 5–6	System Development
Week 7	Testing & Debugging
Week 8	Presentation & Final Output

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📘 Getting Started (Flutter Project)

This repository contains the source code for the ResQintel mobile app built with Flutter.

🔧 Prerequisites

• Flutter SDK
• Android Studio or VS Code
• Firebase project setup

🚀 Run Locally

git clone https://github.com/darknecrocities/ResQintel.git
cd ResQintel
flutter pub get
flutter run