The urban population is rapidly increasing, with ~90% of the population predicted to live in cities by 2100. Since urban environments serve as hot spots for greenhouse gas and pollutant emissions, and as the urban areas are destined to become even denser, sustainable urban life requires the careful and well-informed urban planning of infrastructures that could adequately mitigate these emissions.

A common means to achieve this goal is through urban green vegetation, which is considered a net sink for CO2. We propose to develop a big data-based platform to improve the management practices of urban vegetation and promote its contribution to sustainable cities. Our platform will be based on big data mining of municipal vegetation mapping and remote observations together with machine learning methods to generate insightful algorithms. These algorithms will enable urban vegetation management personnel to map urban vegetation species, estimate plant status, and predict its productivity capacity as a first approximation to how much carbon it sequesters.

 To develop these algorithms, we plan to retrieve data and work closely with the Modi’in municipality, the Center for Israel Mapping – Survey of Israel, and the Israeli Space Agency. The developed algorithms could be used in other Israeli cities.