Greenizing started from our hope to save polar bears and stop global warming which have been the most prominent environmental crisis in the world.
We are applying machine learning to identify trees or solar panels in the city for the systemic planing of greenizing.
We also find suitable places for installing solar panels and roof garden to reduce CO2 emissions with estimations.
Machine learning, deep learning, AI are known as one of the best way of processing huge data such as online maps and satellite imagery.
As shown in the right flipping image where Japan Tokyo tower nearby, machine learning can identify trees, buildings accross wide areas efficiently extract ing features
Greenizing bigdata can analyze city or district level green status.
How many green has in each city?
How many solar panel installed in the city?
How many we can install solar panel and roof gardens?
Greenizing bigdata also can be presented dynamically.
Left pane shows the Seoul city of Korea, by hoovering mouse above the map with small district, it provides green status(solar panel installation, roof to installation, potential area statistics for installing solar panel and roof garden)
Greenizing interactive comprehsive user interacting greenizing tool.
It can define area of interest, followed by green analysis.
Along with visualization, the analyzed big data also can be exported with csv file.
Rooftop BnB can assess your unusing roof for monetization along with contributing reduction of CO2 emmisions.
By putting address you will get how much your roof can generate electricity. CO2 reduction in case of installing roof top garden.
Solar panel installer and roof garen installer can identify ROI with specified area.
The plarform also help out match making among roof owner, PV, and Greeening installer.