UrbanHeat AI

->Inspiration Urban areas face street-level heat due to concrete surfaces and low greenery. UrbanHeat AI predicts hotspots and gives actionable suggestions to help cities stay cooler. ->What it does Detects street-level heat hotspots with AI. Interactive map shows 🔴 Heat, 🟠 Moderate, 🟢 Cool areas. Suggests actions: plant trees, reflective roofs, maintain green cover. Users can input latitude & longitude to see local heat levels. ->How we built it Street-level dataset of temperature, humidity, and green cover. Predictive model using Python, Pandas, NumPy, ML. Web app with HTML, CSS, JS, and Google Maps/Leaflet.js. ->Challenges we ran into Centering the map on user locations. Representing realistic street-level variations. Making map clear and interactive. Accomplishments that we're proud of Fully interactive street-level heat detection tool. Dynamic popups with actionable suggestions and emojis. Polished demo ready for hackathons. ->What we learned Working with geospatial data. Building predictive ML models for environmental applications. Integrating interactive maps with AI insights.

Built With

• built-with-languages:-python
• css
• data-visualization
• flask-other-tools:-google-maps-api
• folium
• html
• javascript-libraries/frameworks:-pandas
• leaflet.js-purpose:-machine-learning
• numpy
• scikit-learn
• web