Understanding Urban Water Consumption using Remotely Sensed Data
This work addresses urban water management challenges for city planners and environmental researchers, but it is incremental as it applies existing methods to a new data context.
The researchers tackled the problem of estimating urban water consumption by developing a three-step analysis pipeline using satellite imagery: identifying building pixels, classifying building types, and applying municipal survey data on average water consumption per unit area. They demonstrated this approach for estimating water consumption by buildings in urban regions captured by satellite imagery.
Urban metabolism is an active field of research that deals with the estimation of emissions and resource consumption from urban regions. The analysis could be carried out through a manual surveyor by the implementation of elegant machine learning algorithms. In this exploratory work, we estimate the water consumption by the buildings in the region captured by satellite imagery. To this end, we break our analysis into three parts: i) Identification of building pixels, given a satellite image, followed by ii) identification of the building type (residential/non-residential) from the building pixels, and finally iii) using the building pixels along with their type to estimate the water consumption using the average per unit area consumption for different building types as obtained from municipal surveys.