INDICATOR Framework

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Introducing the "Urban Green Classification Index"

 
 

Mapping the quantity of urban green space is nothing new, but mapping its quality is.

Global Green City Watch recognizes the quality of urban green space from high resolution satellite imagery and provides an index that is as good as the one established in the field. We harness the power of OpenStreetMap (OSM), a database that anyone can contribute to, together with high resolution satellite imagery on a versatile cloud-based analysis platform. The unique combination of assets enables machine learning on information collected by everyday citizens. Going beyond the size and shape of urban green space, we are able to upscale the approach using GBDX, OSM vector services, and training classification algorithms, in particular forest classifiers, to handle the data variability. Having a classification as a base, image processing algorithms found in the scikit-image library allow for automatically measuring water bodies, the composition of greenery in riparian vegetation zones, quantifying and classifying facilities, and many other variables. The result is an unprecedented index, revealing the current quality of a green space and thereby empowering municipalities across the world to design better greenery for their citizens.