# Understanding Urban Water Consumption using Remotely Sensed Data

arXiv: https://arxiv.org/abs/2205.02932 · HTML: https://mlsanity.com/paper/2205.02932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 2 of 2)

- [Rapid building detection using machine learning](https://mlsanity.com/paper/1603.04392/) (2016)
- [Computational Model for Urban Growth Using Socioeconomic Latent Parameters](https://mlsanity.com/paper/2007.00480/) (2020)

## Built on by (most cited first, 1 of 1)

- [Predicting Household Water Consumption Using Satellite and Street View Images in Two Indian Cities](https://mlsanity.com/paper/2510.26957/) (2025)

## Abstract (arXiv)

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.