If you are an (aspiring) data scientist, AI engineer, or an enthusiast just starting your AI journey, apply for the following two-month challenge to boost your skills on real data while helping to find a solution for one of the grandest challenges on the planet. The challenge starts on October 8th.
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Applications open for our 1st #AI Challenge in Brazil to solve one of the biggest environmental problems on the planet. If you are an (aspiring) #datascientist, AI engineer, or enthusiast, apply now and save one of 40 spots to make a real impact while improving your real world skills.🌍🌎🌏💻 . Apply through the link in our bio 💯 . #AIforGood #techforgood #stopforestfires #savetheplanet #programmingmemes #datascience #techforgood #Aistartup #machinelearningalgorithms #deeplearning#keras #amazonrainforest #forestfires #savethetrees #saveearth #datasciencetraining #sdgs2030
Deforestation and agriculture damages contribute 17% to climate change. Wild Fires are responsible for 8 billion tons of CO2 emissions, which is one-third of all global CO2 emissions!
The challenge partner Sintecsys, a mission-driven Brazilian startup, built an automatic-early-stage-exact-location-fire-outbreak-detection-solution, which already reduced losses by 90%!
The challenge goal
You will help to improve the current solution by building machine learning models that detect fires in real-time (or near real-time) using landscape images generated by cameras on top of communication towers. The focus will lie on,
1. identifying smoke in the footage or images during daytime as being a fire outbreak.
2. identifying lightness in nighttime images as being a fire outbreak.
• Thousands of high-resolution images for every condition (fire, non-fire, day, night, sunrise, nightfall, etc.)
• Data for transition conditions (from day to night) will require labeling effort
Apply and be one of 40 collaborators to make a real-world impact.
Sintecsys is a mission-driven company based in Sao Paulo and has designed a solution to detect fire outbreaks in forests and plantations in an average time of 5 minutes.