用卫星和街景图预测印度城市家庭用水量，效果接近传统调查。
Predicting Household Water Consumption Using Satellite and Street View Images in Two Indian Cities
- 结合街景分割与遥感数据建模预测用水
- 准确率达0.55，接近调查模型的0.59
- 适合城市水资源研究者快速获取用水估算
快速城市化地区监测家庭用水面临成本高、耗时长的问题。本文研究是否可利用公开影像（卫星图、谷歌街景GSV分割）及简单地理空间变量（夜间光照强度、人口密度）预测印度胡布利-达瓦德市家庭用水量。比较四种方法：调查特征（基准）、CNN嵌入（卫星、街景、融合）、街景语义图加辅助数据。在有序分类框架下，街景分割结合遥感变量实现0.55的准确率，接近调查模型的0.59。误差分析显示对用水极端值预测精度高，中等用水群体因视觉特征重叠导致混淆。还将用水估算与主观收入估计对比。结果表明，开放影像配合少量地理数据，为城市分析中替代调查获取可靠家庭用水估计提供了可行路径。
原文摘要 · Abstract (English)
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.
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