## Inspiration

After learning about the current shortcomings of disaster response platforms, we wanted to build a modernized emergency services system to assist relief organizations and local governments in responding faster and appropriately.

## What it does

safeFront is a cross between next-generation 911 and disaster response management. Our primary users are local governments and relief organizations. The safeFront platform provides organizations and governments with the crucial information that is required for response, relief, and recovery by organizing and leveraging incoming disaster related data.

## How we built it

safeFront was built using React for the web dashboard and a Flask service housing the image classification and natural language processing models to process the incoming mobile data.

## Challenges we ran into

Ranking urgency of natural disasters and their severity by reconciling image recognition, language processing, and sentiment analysis on mobile data and reported it through a web dashboard. Most of the team didn't have a firm grasp on React components, so building the site was how we learned React.

## Accomplishments that we're proud of

Built a full stack web application and a functioning prototype from scratch.

## What we learned

Stepping outside of our comfort zone is, by nature, uncomfortable. However, we learned that we grow the most when we cross that line.

## What's next for SafeFront

We'd like to expand our platform for medical data, local transportation delays, local river level changes, and many more ideas. We were able to build a fraction of our ideas this weekend, but we hope to build additional features in the future.
## Inspiration

Natural disasters do more than just destroy property—they disrupt lives, tear apart communities, and hinder our progress toward a sustainable future. One of our team members from Rice University experienced this firsthand during a recent hurricane in Houston. Trees were uprooted, infrastructure was destroyed, and delayed response times put countless lives at risk.

* **Emotional Impact**: The chaos and helplessness during such events are overwhelming.
* **Urgency for Change**: We recognized the need for swift damage assessment to aid authorities in locating those in need and deploying appropriate services.
* **Sustainability Concerns**: Rebuilding efforts often use non-eco-friendly methods, leading to significant carbon footprints.

Inspired by these challenges, we aim to leverage AI, computer vision, and peer networks to provide rapid, actionable damage assessments. Our AI assistant can detect people in distress and deliver crucial information swiftly, bridging the gap between disaster and recovery.

## What it Does

The Garuda Dashboard offers a comprehensive view of current, upcoming, and past disasters across the country:

* **Live Dashboard**: Displays a heatmap of affected areas updated via a peer-to-peer network.
* **Drones Damage Analysis**: Deploy drones to survey and mark damaged neighborhoods using the Llava Vision-Language Model and generate reports for the Recovery Team.
* **Detailed Reporting**: Reports have annotations to classify damage types [tree, road, roof, water], human rescue needs, site accessibility [Can response team get to the site by land], and suggest equipment dispatch [Cranes, Ambulance, Fire Control].
* **Drowning Alert**: The drone footage can detect when it identifies a drowning subject and immediately call rescue teams
* **AI-Generated Summary**: Reports on past disasters include recovery costs, carbon footprint, and total asset/life damage.

## How We Built It

* **Front End**: Developed with Next.js for an intuitive user interface tailored for emergency use.
* **Data Integration**: Utilized Google Maps API for heatmaps and energy-efficient routing.
* **Real-Time Updates**: Custom Flask API records hot zones when users upload disaster videos.
* **AI Models**: Employed MSNet for real-time damage assessment on GPUs and Llava VLM for detailed video analysis.
* **Secure Storage**: Images and videos stored on Firebase database.

## Challenges We Faced

* **Model Integration**: Adapting MSNet with outdated dependencies required deep understanding of technical papers.
* **VLM Setup**: Implementing Llava VLM was challenging due to lack of prior experience.
* **Efficiency Issues**: Running models on personal computers led to inefficiencies.

## Accomplishments We're Proud Of

* **Technical Skills**: Mastered API integration, technical paper analysis, and new technologies like VLMs.
* **Innovative Impact**: Combined emerging technologies for disaster detection and recovery measures.
* **Complex Integration**: Successfully merged backend, frontend, and GPU components under time constraints.

## What We Learned

* Expanded full-stack development skills and explored new AI models.
* Realized the potential of coding experience in tackling real-world problems with interdisciplinary solutions.
* Balanced MVP features with user needs throughout development.

## What's Next for Garuda

* **Drone Integration**: Enable drones to autonomously call EMS services and deploy life-saving equipment.
* **Collaboration with EMS**: Partner with emergency services for widespread national and global adoption.
* **Broader Impact**: Expand software capabilities to address various natural disasters beyond hurricanes.
## Inspiration

-- Two of our team members from Florida have experienced the impact of Hurricane Irma firsthand during the past week even before it made landfall: barren shelves in the grocery store, empty fuel pumps, and miles upon miles of traffic due to people evacuating. Even amidst the chaos and fear, there are stories of people performing altruistic acts to help one another. One Facebook post recounted the story of a woman going to the store in search of a generator for her father who relies on a ventilator. There were no generators left at any store in town, so a generous person who overheard her situation offered her their generator. If an app were able to connect people who were able to offer assistance with people in need, many more beautiful stories like this could exist.

## What it does

-- Our app brings together communities to promote cooperation in both the preparation for and the aftermath of hurricanes and other natural disasters. Users are able to offer or request shelter, assistance, supplies, or rides. Others may view these offers or requests and respond to the original poster. Users may find important information, such as evacuation and flood warnings and the contact information of local authorities. Additionally, local authorities can utilize this app to plan their route more effectively and provide the fastest and most efficient care for those who need it the most.

## How we built it

-- We built the app in Expo using React Native and React Navigation. We chose Firebase for our database because of it's reliability and sheer ability to scale for an influx of users - prevalent in circumstances such as a natural disaster. Additionally, Firebase provides real time updates so that people can offer or receive help as soon as possible, saving more lives and ensuring the safety of the people in our communities. We also used React MapView to provide a visual for the areas affected.

## Challenges we ran into

-- None of our team knew anything about Javascript, React, Expo, or Firebase before we the project.
 Despite encountering countless roadblocks, we took advantage of PennApp's resources such as mentors, hackpacks, workshops, and students to help us through the difficult, but also very rewarding times.

## Accomplishments that we're proud of

-- Being able to tackle a real life problem that is affecting countless of lives in front of our eyes inspired and motivated us, to not only become more empathetic to those around us but to unite and help out our community.

## What we learned

-- We learned how to use cutting edge technology such as React Native and Firebase to rapidly prototype a solution that has the potential to save many lives and empower our community in less than 36 hours. We also learned how quickly it can be to help others when we free ourselves from our differences and work together.

## What's next for Crisis Connect

-- Next, we will improve the user interface to become more friendly for all of our users.
 If time allows, we will also let people with disabilities and/or health concerns have priority within the app and introduce a chatbot to let the users have an easier time looking for the information they need.
 Additionally, we would like to add a feature that allows users to report major damages, shortages, or traffic jams to keep up with the disasters. We understand that during natural disasters, internet may not always be available, but mobile networks are usually still available with a slower, 2g connection. As a result, we hope to utilize a text/chatbot to effectively communicate with those who are stranded or require immediate attention.