XtressVue

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XtressVue provides first responders with a map identifying areas with high emergency levels based on the number of people involved and the urgency of the situation by analyzing satellite imagery using semantic segmentation. ...learn more

Project status: Under Development

RealSense™, Networking, Internet of Things, Artificial Intelligence

Intel Technologies
OpenVINO, Movidius NCS

Overview / Usage

_If a natural disaster strikes, the emergency responders and government officials in developing and underdeveloped nations don't know where to put their resources and supplies first. _During disasters, critical infrastructure like roads and buildings get destroyed, rendering traditional routing methods ineffective, thus delaying the rescue process, resulting in loss of lives. The Main Problem Faced By First-Responders is:

"How to provide the required supplies and aid required to the victims who need it the most and in the least possible time"?

During natural disasters, emergency centers get overwhelmed, and the first-responders don't know which areas need immediate attention and the number of people affected in a particular site. Our platform, "XtressVue," prioritizes the situations based on their emergency level. When a disaster occurs, victims can send text messages (SMS) to a chatbot that will ask general inquisitions about their status and needs, like health, food, etc. The responses are analyzed to extract critical information, such as the number of victims, type of emergency, and location. This information is then aggregated and visualized as a heatmap in the responder's dashboard, prioritizing areas with the most need.

Methodology / Approach

XtressVue then provides first responders with a map identifying areas with high emergency levels based on the number of people involved and the urgency of the situation. XtressVue analyzes satellite imagery using semantic segmentation; this real-time satellite imagery analysis enables rescue workers to quickly reach victims by generating routes avoiding damaged buildings and roads. Thus accelerating the rescue process and saving more lives.

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