## Inspiration

More than 4.5 million acres of land have burned on the West Coast in the past month alone
Experts say fires will worsen in the years to come as climate change spikes temperatures and disrupts precipitation patterns
Thousands of families have been and will continue to be displaced by these disasters

## What it does

When a wildfire strikes, knowing where there are safe places to go can bring much-needed calm in times of peril
Mildfire is a tool designed to identify higher-risk areas with deep learning analysis of satellite data to keep people and their families out of danger
Users can place pins at locations of themselves or people in distress
Users can mark locations of fires in real time
Deep learning-based treetop detection to indicate areas higher-risk of forest fire
Heatmap shows safe and dangerous zones, and can facilitate smarter decision making

## How I built it

User makes a GET request w/ latitude/longitude value, which is then handled in real time, hosted on Google Cloud Functions
The request triggers a function that grabs satellite data in adjacent tiles from Google Maps Static API
Detects trees w/ RGB data from satellite imagery using deep-learning neural networks trained on existing tree canopy and vegetation data (“DeepForest”, Weinstein, et al. 2019)
Generates a heat map from longitude/latitude, flammability radius, confidence from ML model
Maps public pins, Broadcasts distress and First-Responder notifications in real-time
Simple, dynamic Web-interface

## Challenges I ran into

Completely scrapped mobile app halfway through the hack and had to change to web app.

## Accomplishments that I'm proud of

Used a lottt of GCP and learned a lot about it. Also almost finished the web app despite starting on it so late. ML model is also very accurate and useful.

## What I learned

A lot of GCP and ML and Flutter. Very fun experience overall!

## What's next for Mildfire

Finish the mobile and web app
## Inspiration

The devastating wildfires that have ravaged California in recent years have left huge mark on how we manage disasters. One of our team members witnessed the chaos firsthand during the Oakland Hills Fire, where hundreds were forced to evacuate their homes with little warning. The sight of families frantically packing their belongings, the sound of sirens piercing the air, and the thick smoke obscuring familiar landmarks created a scene of unimaginable urgency and fear.

Emotional Impact: The panic and confusion during evacuation attempts is scary, with gridlocked streets trapping people.

Urgency for Action: We realized the critical need for a more efficient, technology-driven evacuation system to guide people to safety and prevent tragic losses.

Community Resilience: The aftermath revealed how ill-prepared many communities were for such large-scale evacuations, highlighting the importance of proactive planning.

## What it does

Ember Evac gives the most optimal directions to safety based on real-time data.

1.) We first determine the resident’s location to the nearest point to the fire.

2.) The platform calculates the most efficient and safest evacuation routes for individuals and communities by analyzing factors including…

* live traffic data
* road capacities
* current road closures
* fire growth

3.) Ember Evac proactively sends outbound calls to residents, delivering personalized evacuation instructions and updates.

## How we built it

* **Frontend**: React.js
* **Map Display**: MapBox for interactive maps
* **Location Data**: JavaScript and MapBox API to integrate real-time location tracking
* **Evacuation Outbound Assistant**: Vapi for efficient evacuation guidance
* **Voice Processing**: Deepgram for voice recognition and transcription
* **Language Model**: OpenAI for powering the voice assistant’s natural language understanding
* **Hosting & Database**: Firebase for scalable hosting and database management

## Challenges we ran into

* **Animating the forest fire**: Making the forest fire look realistic with a method that is not too computationally intensive was a big challenge. Ultimately, we used mathematical modeling to speed up the process
* **Integrating Voice agent with location data**: One of the biggest challenges was making sure our voice agent accurately interprets the location data and directions we are giving the user

## Accomplishments that we're proud of

* **Technical Skills**: We strengthened are JavaScript and React.js skills while gaining experience with new APIs such as VAPI and MapBox
* **Project Management**: Improved our communication, teamwork and version control skills, utilizing github significantly
* **Impact**: Created a tool that can significantly improve public safety during natural disasters and other emergency event

## What we learned

* Combine various technologies to build a platform capable of handling complex tasks such as real-time mapping and voice-assisted evacuation guidance
* Realized the potential of coding experience in tackling real-world problems with interdisciplinary solutions

## What's next for Ember Evac

* Integrate more complex fire prediction models that consider wind patterns, terrain, and vegetation, along with ML algorithms for real-time traffic analysis
* Expand our comms channels by adding social media alerts and offer multi-language support to serve diverse communities
* Adapt Ember Evac to assist with other types of natural disasters like hurricanes and floods, all while ensuring data privacy and security is secure
## Inspiration

We were interested in disaster relief for those impacted by Hurricances like Dorian and Maria for people that don't know what areas are affected and for first responders that don't know what infrastructure works are damaged and can't deliver appropriate resources in time.

## What it does

This website shows the location of the nearest natural disasters.

## How we built it

We used Amazon API and Google Cloud and Maps API and Python and Java Script.

## Challenges we ran into

We have not been to a hackathon before so we weren't sure about how in-depth or general our problem should be. We started with an app that first responders can use during a natural disasters to input vitals

## Accomplishments that we're proud of

A website that can map the GPS locations of flood data that we are confident in and a uniquely trained model for urban flooding.

## What we learned

We learned about Google Cloud APIs, AWS S3 Visual Recognition Software and about how to operate in a hackathon.

## What's next for Crisis Apps