🌡️ UrbanTherm - AI-Powered Urban Heat Island Detection & Prediction

Our app uses satellite imagery and AI to detect and predict urban heat islands, helping cities identify hotspots, plan smarter, and cool communities more effectively.

📖 Story

Picture this: You're at a hackathon, it's 2 AM, someone mentions "urban heat islands," and suddenly you're deep in a rabbit hole about how cities are literally cooking people alive. 🔥

Turns out, cities can be 15°F hotter than surrounding areas (who knew?!), and the tools to analyze this are either crazy expensive or take forever to run. So we thought: "What if we could just point at any spot on a map and instantly know how hot it's gonna get?"

UrbanTherm was born from that 2 AM energy - we smashed together satellite imagery, AI video analysis, and way too many APIs to create something that can analyze urban heat anywhere on Earth in under 10 seconds. Because why wait when you can have answers NOW?

💡 Inspiration

🔥 "Wait, cities are HOW much hotter?!" - The moment we learned about 15°F temperature differences

🎥 "Dude, what if we fed satellite images to video AI?" - Late night brainstorming with TwelveLabs docs

🛰️ "Hold up, Google Maps tiles are FREE?!" - The realization that changed everything

⚡ "We can do this in REAL-TIME?" - When we realized we could skip all the preprocessing

🌍 "Every city deserves this data" - The hackathon motivation to build something actually useful

Immediate Priorities

[ ] 📱 Mobile App: React Native with AR heat visualization
[ ] 🎥 Custom Video Models: Urban heat-specific training datasets
[ ] 🏘️ Community Analytics: Neighborhood-level social impact analysis
[ ] 🌱 Solution Tracking: Before/after analysis for implemented strategies

Future Vision

[ ] 🔮 Predictive Modeling: ML forecasting of heat island evolution
[ ] 🌍 Global Network: Worldwide monitoring with real-time alerts
[ ] 🚁 Autonomous Monitoring: Drone integration for hyperlocal analysis
[ ] 🎓 Educational Platform: Tools for climate education and research

🎯 What it does

🎥 AI-Powered Video Analysis

Analyzes satellite imagery using TwelveLabs' advanced video understanding
Detects urban features: buildings, roads, green spaces, heat-absorbing surfaces
Provides confidence scoring and detailed infrastructure classification
Identifies heat-contributing patterns in urban development

🛰️ Real-Time Satellite Intelligence

Downloads live satellite tiles from Google Maps API
Performs instant vegetation analysis using computer vision
Calculates NDVI (Normalized Difference Vegetation Index)
Generates comprehensive heat island scores (0-10 scale)

🌡️ Multi-Source Data Fusion

OpenWeather API: Real-time weather and heat index data
NASA Satellite Data: Land surface temperature validation
Environmental Metrics: Air quality, thermal comfort, climate trends
Risk Assessment: Comprehensive heat island threat analysis

💡 Smart Recommendations

AI-generated cooling strategies for specific locations
Cost-benefit analysis for urban interventions
Implementation timelines and resource requirements
Community-focused solutions for vulnerable populations

🔨 How we built it

Backend Stack

# Core Framework
FastAPI          # High-performance async API
TwelveLabs SDK   # Video understanding and AI analysis
Vellum           # AI-Powered Agentic Analysis
OpenCV           # Image processing and vegetation detection
NumPy            # Satellite data numerical computations
asyncio          # Concurrent multi-source data processing

AI & Data Pipeline

🎥 Video Analysis: TwelveLabs official Python SDK for satellite imagery understanding
🛰️ Live Processing: Real-time Google Maps satellite tile downloading and stitching
🌿 Vegetation Detection: Multi-algorithm approach (color-based HSV, NDVI calculation)
📊 Data Fusion: Seamless integration of 5+ environmental data sources

Key Innovations

Zero-Storage Processing: Entire pipeline operates in-memory
Real-Time Analysis: Complete assessment in under 10 seconds
Multi-API Orchestration: Intelligent fallback and data validation
Geographic Precision: Accurate coordinate mapping across projection systems

🚧 Challenges we ran into

Technical Hurdles

🎥 Video AI Integration: Optimizing TwelveLabs for satellite imagery analysis
⚡ Performance: Processing large satellite images in real-time without storage
🌐 API Coordination: Managing rate limits across multiple external services
💾 Memory Management: Efficient in-memory processing of multi-gigabyte datasets

User Experience

📊 Data Complexity: Making climate science accessible to non-technical users
🎯 Visual Communication: Creating intuitive heat visualizations
🌍 Global Accuracy: Ensuring precision across different geographic regions
⏱️ Speed Expectations: Maintaining sub-10-second response times

🏆 Accomplishments that we're proud of

🤯 Things That Actually Work

We made video AI analyze satellite images - TwelveLabs wasn't designed for this, but we made it happen!
Sub-10 second analysis - From coordinates to full heat report faster than you can say "urban heat island"
10,000+ satellite tiles downloaded - Our API calls went BRRRR during testing
Zero crashes - Surprisingly stable for something built in 48 hours

🚀 Cool Technical Stuff

Real-time satellite processing - No preprocessing, no databases, just pure chaos and it works
Multi-API orchestration - We integrated 5+ APIs and somehow they all play nice together
Global coverage - Tested from NYC to Tokyo, works everywhere (we think)
Privacy-first - We don't store your location data because storage is for quitters

🎆 Hackathon Wins

Actually solves a real problem - Cities need this data and we made it free
Scales infinitely - Can analyze any coordinate on Earth
No expensive infrastructure - Runs on free APIs and pure determination
Scientific accuracy - Validated against actual NASA data (we're legit!)

📚 What we learned

Technology Insights

🎥 Video AI Potential: Advanced models can revolutionize environmental monitoring
🛰️ Open Data Power: Free satellite imagery + smart processing rivals expensive solutions
⚡ Real-Time Architecture: In-memory processing beats traditional data storage approaches
🌐 API Orchestration: Thoughtful integration creates exponential value beyond individual services

User Experience Lessons

🎯 Simplicity Wins: Complex climate science must be distilled into actionable insights
📱 Visual Communication: Heat maps communicate more effectively than raw numbers
🏃‍♂️ Speed Matters: Users expect instant results in today's digital landscape
🌍 Local Relevance: Recommendations must be tailored to local climate and infrastructure

Impact & Scale

🤝 Open Collaboration: Community input improved both technical and user experience
📊 Data-Driven Decisions: User feedback and performance metrics guided development
🔄 Iterative Approach: Rapid prototyping with real data led to better architecture
🌱 Environmental Focus: Technology can democratize climate action at scale

🚀 What's next for UrbanTherm

Mobile & AR Experience

📱 Native Mobile Apps: iOS/Android with augmented reality heat visualization
🎥 Real-Time Camera Analysis: Point phone at any area for instant heat assessment
📍 Location Intelligence: GPS-based automatic analysis and personalized recommendations

Advanced Analytics

🔮 Predictive Modeling: Machine learning models forecasting heat island development
📈 Time-Series Analysis: Historical heat pattern tracking and trend identification
🏘️ Community Impact: Neighborhood-level social vulnerability integration
🌱 Green Solution ROI: Before/after tracking of implemented cooling strategies

Global Platform

🌍 Worldwide Network: Real-time global heat monitoring with alert systems
🏛️ Government Integration: Direct APIs for city planning and policy departments
🎓 Educational Tools: Curriculum integration for climate science education
🤝 Community Portal: Citizen science platform for local heat data collection