## Inspiration

The inspiration for Taylor (Tracking Awareness & Yielding Local Opportunities for Relief) stemmed from witnessing the increasing frequency and impact of global disasters. According to the World Meteorological Organization (WMO), over the past 50 years, there have been nearly **12,000 weather, climate, and water-related disasters**, resulting in **more than 2 million deaths and $4.3 trillion in economic losses**. Furthermore, the UN Office for Disaster Risk Reduction reported that between 2000 and 2019, climate-related disasters alone affected 4.2 billion people and resulted in $2.97 trillion in economic losses. These alarming statistics underscore the urgent need for Taylor, to not only raise awareness but also provide actionable insights and opportunities for individuals and organizations to contribute meaningfully. By leveraging AI technology, we aimed to create a comprehensive tool that educates, maps out critical areas, and incentivizes philanthropic efforts to address these pressing global issues as they emerge.

## What it does

Taylor **watches** YouTube videos, **reads** social media posts, and **analyzes** news articles in real-time through a full-fledged mobile app. By unifying the data scattered in multiple sources, Taylor is able to:

* **Raise awareness of global unforeseen distress** by educating users through concise summaries of current events.
* Enable users to **learn while away from the phone** through audio functionality to listen to the news on the go, making it easier for them to stay informed as distress unfolds.
* **Map out major impacted areas before they become widely known**, providing users with a visual representation of the disaster's scope.
* Identify ways to help **overcome hardships and distress by suggesting actionable steps** and opportunities for individuals and organizations to contribute to relief efforts.
* Finally, Taylor is able to **analyze the macroeconomic and microeconomic effects** of the disasters on society. Providing with highlights on potential **market opportunities, industries, companies, and underlying assets** incentivizing philanthropic causes and helping to drive economic recovery globally.

## How we built it

Taylor is built on React Native for cross-platform support between multiple devices. The backend is powered by an API deployed on the Google Cloud Platform (GCP) with a Python/Flask web server. For aggregating data from multiple sources, Taylor uses the Youtube Data API to search & understand relevant videos, the Reddit API to get social media news and comments on what users are thinking about the issues, and You.com for finding the freshest news articles to minimize hallucinations.

Now, when it comes to building the entire AI infrastructure, we developed our own orchestration pipeline to use Retrieval Augmentation Generation among a class of AI agents. We ended up developing the following AI agents & tools using a combination of You.com, Intel AI, and OpenAI:

* Crawlers: Youtube (Youtube Data Api v3 + Transcriptions), Socials (Reddit), News Articles (You.com)
* Information Synthesizer/Summarizer (Helps taking big chunks of text (e.g 100k tokens through youtube and synthesizing it)
* Impact Measurer & Impact Explainer based on The Federal Emergency Management Agency (FEMA) protocols for disaster relief.
* Exposure/Awareness Measurer to find how well-known this situation is to the public.
* Audio Generation: First tried Hume.API but ended up implementing ElevenLabs to enhance the experience.
* Comment Augmentation: Use comments from people in social media posts to infer what people might think of currently uncommented global disasters.
* Financial Market Opportunities: Find underlying assets to invest in, potential industries impacted by this recent news, companies vulnerable to this, and actions to take.

## Challenges we ran into

One of the main challenges we faced was ensuring the accuracy and timeliness of the data. Aggregating data from multiple sources and ensuring it is up-to-date requires a robust backend infrastructure and efficient data processing algorithms. Additionally, creating a user-friendly interface that effectively conveys complex information was another challenge. Balancing the amount of information displayed without overwhelming the user was critical. Implementing the audio functionality also presented challenges in ensuring smooth playback and handling.

## Accomplishments that we're proud of

We are proud of creating a platform for social good that not only raises awareness but also empowers users to take actionable steps in response to global disasters. Successfully integrating real-time data and presenting it in an accessible and informative manner is a significant accomplishment.

## What we learned

Through the development of Taylor, we learned the importance of data accuracy and the challenges of integrating multiple data sources. The project underscored the value of interdisciplinary collaboration, combining expertise in technology, data science, and humanitarian response to create a meaningful tool to help communities at risk.

## What's next for TAYLOR

Moving forward, we plan to enhance Taylor by incorporating more advanced data analytics and predictive modeling to provide even more precise and actionable insights. We aim to expand our data sources to cover various disasters and impacts. Additionally, we plan to introduce more features that enable users to contribute directly to relief efforts, such as donation platforms and volunteer coordination. By continuously improving the platform, we hope to make an even greater impact on global disaster awareness and response. Additionally, we would like to enhance Taylor's functionality to include multi-language support, and offline playback is also on our roadmap to make Taylor more accessible and available to any location a user calls home.
## Inspiration

## What it does

ResQ is a holistic natural disaster recovery tool.
-Officials (FEMA, Red Cross, etc.) can manage their resource centers on a WebApp: by managing the distribution of resources, the location of distressed individuals, and publicly sourced alerts.

-Private Citizens can use a mobile app as a one-stop-shop solution for their emergency needs including: finding the nearest medical service, food/water source, or shelter source, alerting officials of power outages, fallen trees, or obstructed roads, and finally trigger and emergency response. Users can use augmented reality to point them towards the closest resource.
-Emergency Response Teams: can be dispatched through the app to find private citizens. A convolutional neural network processes Arial imagery to tag and find distressed citizens without cell service

## How I built it

The WebApp is built in React.JS. The mobile app is built with swift in Xcode. The backend was made using Firebase. The AI/Deep Learning portion was built using Keras.

## Challenges I ran into

We ran into several challenges throughout the course of this project. We all dealt with some ideas and technologies we had very little experience with before. Implementing the AR/VR was challenging as this technology is very new and is still very hard to use. Using a pretrained neural network to do image detection (drawing the boudning box) was very challenging as it is a machine learning problem we had never tackled before and one in which very little documentation exists. Also, dealing with many of the sponsor APIs was initially very challenging to deal with as some of the endpoints were hard to successfully interact with.

## Accomplishments that I'm proud of

We think that the scale of this project is huge and has a tremendous amount of application in the real world. This app (on the mobile side) gives people who are victims of a natural disaster a place to report their location, find any resources they may need, and report anything potentially dangerous. This app also (on the web side) gives rescuers a central database to locate and keep track of people who are currently in danger. Lastly, this app also uses deep learning to use drones to identify stranded humans who may not have cell service. We are truly proud of the scale this project achieves and all the rich and various technologies involved.

## What I learned

We all learned various skills in our respective parts of the application: React, iOS with AR/VR, Firebase, Keras.

## What's next for ResQ

The next steps would be to actually implement the deep learning portion of the project, preferably with a drone that could transmit a constant stream that could be processed to see if there are any humans in a certain area and transmit their coordinates appropriately. We also want to build out each specific feature of the mobile app, including directions to water, food, shelter, hospital, or gas.
## Inspiration

During extreme events such as natural disasters or virus outbreaks, crisis managers are the decision makers. Their job is difficult since the right decision can save lives while the wrong decision can lead to their loss. Making such decisions in real-time can be daunting when there is insufficient information, which is often the case.

Recently, big data has gained a lot of traction in crisis management by addressing this issue; however it creates a new challenge. How can you act on data when there's just too much of it to keep up with? One example of this is the use of social media during crises. In theory, social media posts can give crisis managers an unprecedented level of real-time situational awareness. In practice, the noise-to-signal ratio and volume of social media is too large to be useful.

I built CrisisTweetMap to address this issue by creating a dynamic dashboard for visualizing crisis-related tweets in real-time. The focus of this project was to make it easier for crisis managers to extract useful and actionable information. To showcase the prototype, I used tweets about the current coronavirus outbreak.

## What it does

* Scrape live crisis-related tweets from Twitter;
* Classify tweets in relevant categories with deep learning NLP model;
* Extract geolocation from tweets with different methods;
* Push classified and geolocated tweets to database in real-time;
* Pull tweets from database in real-time to visualize on dashboard;
* Allows dynamic user interaction with dashboard

## How I built it

* Tweepy + custom wrapper for scraping and cleaning tweets;
* AllenNLP + torch + BERT + CrisisNLP dataset for model training/deployment;
* Spacy NER + geotext for extracting location names from text
* geopy + gazetteer elasticsearch docker container for extracting geolocation from locations;
* shapely for sampling geolocation from bounding boxes;
* SQLite3 + pandas for database push/pull;
* Dash + plotly + mapbox for live visualizations;

## Challenges I ran into

* Geolocation is hard;
* Stream stalling due to large/slow neural network;
* Responsive visualization of large amounts of data interactively;

## Accomplishments that I'm proud of

* A working prototype

## What I learned

* Different methods for fuzzy geolocation from text;
* Live map visualizations with Dash;

## What's next for CrisisTweetMap

* Other crises like extreme weather events;