Wildfires are ravaging through various forests, wildlife and human settlements as we

speak. Arctic fires, Australian Fires and the Amazon Fires are some of the examples.

Their short-term destruction includes destruction of human establishments and

wildlife settlements and long term destruction includes increased air pollution and

ecological imbalance.

Background

Our idea is based on three major entities; Analyse - Reconnaissance - Mitigate. The

implementation includes having mechanisms to -

- Aggregate wildfire information from MODIS and VIIRS satellites and via

crowd-sourcing by allowing users to report wildfires with a smartphone.

- Validate and map wildfires via autonomous drones that are capable of

recognizing fires utilzing Computer Vision. Drones are more effective in

evaluating the rate of spread as they are immune to smoke and thick forest

canopies. Using this, we predict the extent of rate and direction of spread.

Drones also help to find stranded humans and wildlife and alert the rescue

authorities.

- Help in mitigation by providing aggregated data from drone reconnaissance to

fire-fighters to help curb the spread and to individuals to plan their escape

routes.

Implementation

-Data aggregated from MODIS and VIIRS to find plausible wildfire location

Object Detector using CenterNet-ResDCN34 to detect fire and people

-Use the object detector to validate crowd source data by detecting fire

-Build Flask API with Gunicorn and Flask

-Build the App with Flutter

-Geofence areas of Wildfires that are confirmed via the drone and

crowdsourcing

-Provide general instructions and escape routes to individuals in areas of

wildfires

GitHub: Click here!