## Inspiration

We took inspiration from the multitude of apps that help to connect those who are missing to those who are searching for their loved ones and others affected by natural disaster, especially flooding. We wanted to design a product that not only helped to locate those individuals, but also to rescue those in danger. Through the combination of these services, the process of recovering after natural disasters is streamlined and much more efficient than other solutions.

## What it does

Spotted uses a drone to capture and send real-time images of flooded areas. Spotted then extracts human shapes from these images and maps the location of each individual onto a map and assigns each victim a volunteer to cover everyone in need of help. Volunteers can see the location of victims in real time through the mobile or web app and are provided with the best routes for the recovery effort.

## How we built it

The backbone of both our mobile and web applications is HERE.com’s intelligent mapping API. The two APIs that we used were the Interactive Maps API to provide a forward-facing client for volunteers to get an understanding of how an area is affected by flood and the Routing API to connect volunteers to those in need in the most efficient route possible. We also used machine learning and image recognition to identify victims and where they are in relation to the drone. The app was written in java, and the mobile site was written with html, js, and css.

## Challenges we ran into

All of us had a little experience with web development, so we had to learn a lot because we wanted to implement a web app that was similar to the mobile app.

## Accomplishments that we're proud of

Accomplishment: We are most proud that our app can collect and stores data that is available for flood research and provide real-time assignment to volunteers in order to ensure everyone is covered in the shortest time

## What we learned

We learned a great deal about integrating different technologies including XCode, . We also learned a lot about web development and the intertwining of different languages and technologies like html, css, and javascript.

## What's next for Spotted

Future of Spotted: We think the future of Spotted is going to be bright! Certainly, it is tremendously helpful for the users, and at the same time, the program improves its own functionality as data available increases. We might implement a machine learning feature to better utilize the data and predict the situation in target areas. What's more, we believe the accuracy of this prediction function will grow exponentially as data size increases. Another important feature is that we will be developing optimization algorithms to provide a real-time most efficient solution for the volunteers. Other future development might be its involvement with specific charity groups and research groups and work on specific locations outside US.
## Inspiration

In response to recent tragic events in Turkey, where the rescue efforts for the Earthquake have been very difficult. We decided to use Qualcomm’s hardware development kit to create an application for survivors of natural disasters like earthquakes to send out distress signals to local authorities.

## What it does

Our app aids disaster survivors by sending a distress signal with location & photo, chatbot updates on rescue efforts & triggering actuators on an Arduino, which helps rescuers find survivors.

## How we built it

We built it using Qualcomm’s hardware development kit and Arduino Due as well as many APIs to help assist our project goals.

## Challenges we ran into

We faced many challenges as we programmed the android application. Kotlin is a new language to us, so we had to spend a lot of time reading documentation and understanding the implementations. Debugging was also challenging as we faced errors that we were not familiar with. Ultimately, we used online forums like Stack Overflow to guide us through the project.

## Accomplishments that we're proud of

The ability to develop a Kotlin App without any previous experience in Kotlin. Using APIs such as OPENAI-GPT3 to provide a useful and working chatbox.

## What we learned

How to work as a team and work in separate subteams to integrate software and hardware together. Incorporating iterative workflow.

## What's next for ShakeSafe

Continuing to add more sensors, developing better search and rescue algorithms (i.e. travelling salesman problem, maybe using Dijkstra's Algorithm)
## Inspiration

In 2017, one of our team members bunkered down through Hurricane Harvey, which devastated the city of Houston. Many low-lying areas were flooded, stranding families inside their homes and cutting off supply lines and infrastructure. However, the resulting community response was astonishing - thousands of boat-owning residents took it upon themselves to rescue their less fortunate neighbors by ferrying people out of dangerous waters and bringing supplies, compensating for the city police and fire departments that were severely backlogged with many other tasks.
To facilitate their efforts, the volunteer rescuers used social media and Google Maps, building impromptu pages to communicate with each other and identify hazardous areas for rescue. Our idea is to combine all of the disparate command centers that saved Houston during Hurricane Harvey into a single app so rescuers and even victims are prepared ahead of time. The location, status, and situational needs of every user will all be viewable on a live map, broadcasting vital information to everyone in the disaster zone and strengthening the bonds of local communities during difficult times.

## What it does

Beacon is primarily a location-tracking app, broadcasting the user’s location as a dot on a map to other users whenever the app is enabled. Users can choose between 5 different settings for their status - emergency (requesting aid ASAP), help (requesting non-urgent aid), neutral (the default setting), safe (no aid necessary), and rescuer (offering aid). Every user’s dot will be assigned a certain color depending on their status, and additional comments describing specific types of aid or situations can be viewed by rescuers as a pop-up on the map. By tracking location, rescuers can easily identify swathes of rescue locations and find victims, and victims can determine where the nearest rescuer to their position is.

## How we built it

Beacon is an iOS app built in Xcode 10.3 using the Swift 4 programming language, with MapKit tools to support the location-tracking features.

## Challenges we ran into

When we initially brainstormed the idea for a disaster response app, location tracking was the most daunting issue since we had no idea how to pull location data from devices and implement it. We used MapKit to track our own location, but it was too difficult to set up our app to track other client devices as well, so for the purposes of the demonstration, we decided to simulate that data instead.
Early Saturday morning, we ran into a massive merge fiasco when 3 of our team members tried to improve the functionality of the user’s status colors all at the same time. After painfully moving back to a previous version and redownloading all of our code, we learned to coordinate our git commands.

## Accomplishments that we're proud of

Coming together as a team and producing our planned idea is one of our most significant accomplishments this weekend - we are a team of 4 college freshmen and first-time hackers, so we’re completely new to everything. In just 36 hours, we figured out how to pool our thoughts together, generate a unique idea, determine our constraints, and relentlessly debug to hack the final product together.

## What we learned

We discovered how to take full advantage of version control with Git, as we broke our app many times during development and became very well acquainted with reverting back to our last working version.
Additionally, we learned how to designate tasks between back- and front-end development and link them together to construct the app. For example, we split button creation tasks among team members by delegating one person to construct the button UI, while another linked buttons to Beacon’s functionalities.

## What's next for Beacon

Adding communication features would significantly improve the utility of Beacon if rescuers and victims could quickly share information with each other within the disaster zone. A potential next step could be to partner Beacon’s functionality with the Zello app, a communications app that simulates walkie-talkies and can run on low signal, which was also used by rescuers during Hurricane Harvey.