Geolytics.AI

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Inspiration

The inception of Team TerraHackers can be attributed to the common desire to leverage the power of technology to have a positive impact on the world.

Accelerated industrialization has caused rapid deterioration of the climate. The frequency of natural disasters like storms etc. have proportionally increased with climate change. The development of sophisticated technology such as satellite imagery etc. have aided timely prediction of natural disasters.

However, there are many limitations in the Disaster Risk Management sector. A staggering 110 countries (56.4%) do not have approved Disaster Risk Management Strategies. Limited data verification pipelines can cause mass panic among people. For example, the Pacific Early Warning System has a false alarm rate of 75%. Finally, much of the current efforts involve manually going through terabytes of data during sensitive, time-critical scenarios, which often leads to loss of thousands of avoidable deaths. There is almost an absence of the use of cutting-edge advancements of Artificial Intelligence to optimize processes.

Hence, came the inspiration for Geolytics.AI – a one-stop solution to solve all the above problems with 3 major features: Disaster Detection, Damage Assessment and Organizational Workflows. The main motivation was to leverage the power of technology to build disaster-resilient communities and accomplish the 11th Sustainable Development Goal of Sustainable Cities and Communities as laid down by the United Nations.

What it does

Geolytics.AI will be the next big step forward in accomplishing the 11th Sustainable Development Goal of Sustainable Cities and Communities laid down by United Nations. It has the following features:

Data verification and validation via witness reports, Twitter APIs and News APIs using Natural Language Processing and citizen-integrated technologies
State-of-the-art AI systems for hazard-agnostic damage localization and assessment
Latest developments on recent disaster management by data collation and validation from many humanitarian portals
Reliable REST API endpoints that expose our AI models and rich backend data in easy-to-integrate JSON format
Dashboards, analytics and support for getting quick insights and embedding into organizational workflows

The value addition of Geolytics.AI is tremendous, as it addresses specific pain points of current DRM systems effectively:

1. Artificial Intelligence powered decision making

Powerful AI algorithms and backend computations for disaster-specific, real-time intelligent solutions completely eliminate the need for manual supervision
Object Classification to record and classify disasters based on witness images
Computer Vision and Image Segmentation for damage localization and assessment provide deep insights about the on-ground situation
Natural Language Processing for real-time tweet classification and geotagging

2. Multiple eyes, single platform

An integrated platform with a single database ensures data consistency
The same set of information is visible to all people using the software. Gone are the times of miscommunication in time-sensitive scenarios!
AI models and validation pipelines run 24/7 to only expose authentic data

3. Organizational workflow integration

For users who prefer to manipulate and use data in their custom way, our highly reliable API services provide simple access to rich data in the industry-standard JSON format. Plug-and-use in this sector has never been easier!
All website offerings are available as consumable APIs, namely dashboards, data visualization, live area monitoring and inventory management.

Challenges we ran into

To build this product, we ran into several challenges, but each of them were promptly addressed to ensure the development of a well-working prototype. Some of the major challenges that we ran into are:

The technical architecture of the entire system had to go through several iterations to ensure that all the systems are coherent. Due to the pipelined nature of the application wherein the information goes through disaster detection followed by damage assessment, we had to figure out solutions to problems like where to fetch information, fastest information source, latency for damage assessment etc. to get properly functioning services
We had to go through the implementation and innovation of several ML models for different services (like Disaster Detection, Damage Assessment etc) to ensure that the accuracy of the returned results was high
Since there were so many services, a significant amount of time was spent to come up with the design of the frontend. We had to ensure it was intuitive and easy for the user, and at the same time powerful to complete all the required tasks with the lowest possible latency. We followed Shneidermann's 8 Golden Rules for the User Interface design.

Accomplishments that we're proud of

This is one of the first times we are developing a software of this scale and impact. In times of disasters, there is a need for quick, accurate and reliable information to support relief organizations and rescue missions. We are extremely proud of having developed such a scalable application from scratch and not only engineer a production-ready hack but also innovate on the technologies we used. We developed Algorithms for extracting information from raw data and process it to provide the in best possible manner.

The sheer scale of the problem we were attempting to solve, combined with the fun we had in working on an impactful project such as Geolytics.AI makes this a very memorable and proud moment for us.

What we learned

Building Geolytics.AI was a great learning experience for us, as we researched on a niche area that can make use of innovations in technology. We learnt about the following:

Existing Disaster-Risk Management solutions, their offering and their shortcomings. This helped us find pain points that can be addressed by Geolytics.AI
Existing methodologies by real disaster-response authorities and the technical innovations used by them
The communication gaps between authorities and citizens, and how this gap can be reduced by bringing and spreading verified informtion as quickly as possible
We learnt deployments to multiple cloud platforms like GCP and Azure. We learnt a lot about REST APIs and how every service can be managed easily by containerizing it and exposing it as a public API
We worked with a wide range of tools, technologies and platforms like Azure, GCP, Python, JavaScript, Rect, Node.js, Web Scraping etc. which was very enriching.
We worked heavily on System Design of the platform to ensure scalability, which was a completely new and exciting prospect for us.