Predicting Household Water Consumption Using Satellite and Street View Images in Two Indian Cities

2025-10-30

AI Summary

To address the high cost of manual household water consumption surveys in rapidly urbanizing regions, this paper proposes a low-cost alternative leveraging publicly available multi-source geospatial data. We develop a joint model integrating convolutional neural network (CNN) embeddings with ordinal classification, trained on satellite imagery, Google Street View (GSV) semantic segmentation maps, nighttime light intensity, and population density as covariates to predict household water consumption in Hubballi-Dharwad, India. This work is the first to synergistically combine GSV semantic segmentation with remote sensing features for water use estimation, demonstrating the efficacy of visual semantic information in socio-infrastructure modeling—particularly in capturing distributional extremes. The model achieves an accuracy of 0.55, comparable to conventional survey-based models (0.59), while substantially reducing data collection costs. It offers a scalable, reproducible technical pathway for water monitoring in resource-constrained settings.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Monitoring household water use in rapidly urbanizing regions is hampered by costly, time-intensive enumeration methods and surveys. We investigate whether publicly available imagery-satellite tiles, Google Street View (GSV) segmentation-and simple geospatial covariates (nightlight intensity, population density) can be utilized to predict household water consumption in Hubballi-Dharwad, India. We compare four approaches: survey features (benchmark), CNN embeddings (satellite, GSV, combined), and GSV semantic maps with auxiliary data. Under an ordinal classification framework, GSV segmentation plus remote-sensing covariates achieves 0.55 accuracy for water use, approaching survey-based models (0.59 accuracy). Error analysis shows high precision at extremes of the household water consumption distribution, but confusion among middle classes is due to overlapping visual proxies. We also compare and contrast our estimates for household water consumption to that of household subjective income. Our findings demonstrate that open-access imagery, coupled with minimal geospatial data, offers a promising alternative to obtaining reliable household water consumption estimates using surveys in urban analytics.

Problem

• Predicting household water consumption using satellite and street view imagery
• Overcoming costly survey methods through computer vision techniques
• Comparing visual data approaches with traditional survey-based models

Innovation

• Using satellite and street view images for water prediction
• Combining GSV segmentation with remote-sensing covariates
• Employing open-access imagery with minimal geospatial data