Among much misinformation, one post from NASA stood out by shedding the fundamental light of science on the matter.

In the image below, you see a colored high-resolution satellite image showing how the fire smokes spread to the southeast states of my country.

The solution

Sintecsys´s growing customer base of clients on farms and forests can confirm. The company installs cameras on top of communication towers to capture images that are sent to a monitoring center. Once there is fire (or smoke) detected on images, it sends alerts and fire fighting actions. This saves lives and infrastructure costs.

Sintecsys is not alone in its mission as there are many other companies around the world dedicated to this mission also in a very successful way.

The company installed 50 towers distributed in Brazil (2019 data.

To extend the customer reach and scale their business model to thousands of cameras with the capability of accurately and quickly detecting wildfire outbreaks, Omdena’s AI capabilities come into play.

Omdena is a global platform where organizations collaborate with a diverse AI community to build solutions for real problems in a faster and more effective way.

How the team solved the problem

#1 Scoping the problem

To tackle this problem, Omdena and Sintecsys agreed to deal with day images in their first joint challenge and in a second challenge improve the solution by dealing with night images.

The main difference between day and night images for fire detection is that during the day images usually show smoke and during the night these images show live fire. Both sunset and dawn, where smoke and live fire coexist on images, represent boundary conditions for the problem.

#2 Working on the dataset

The dataset was really big comprising footage and images from different cameras with and without fires outbreaks happening. Combining the original images given, our team had almost 7.600 images of 1920 x 1080 size (day images without fires outbreaks, day images with fires, and some night images (around 16%)) to start labeling.

To add even more images, Gary Diana built an algorithm to successfully extract images from the footage and at the same time avoiding the generation of images with the same landscape among them (de-duplication). This initiative added another 1.150 images of 1280 x 720 size to our dataset.

#3 Labeling with Labelbox

Having the datasets prepared for labeling, we gathered around 20 people dedicated to the task, created the environments on Labelbox, which is the best tool available for computer vision by allowing labeling data, managing its quality and operating a production training data pipeline, and then, at last, we started to make tests and to label the final datasets.

I managed the task but I received huge support from Alyona Galyeva who helped the whole team not only by labeling but also by reviewing and managing everyone´s work.

In her own words:

It always starts with a mess when a group of people collaborates on a labeling project. In our case, Labelbox saved us a lot of time and effort by not allowing multiple users to label the same data. On top of that, it made our lives easier by proposing 4 roles: Labeler, Reviewer, Team Manager, and Admin. So, nobody was able to mess with data sources, data formats, and, of course, the labels made by other people.