Ground Control to Major Tom: the importance of field surveys in remotely sensed data analysis
Ian Bolliger (1), Tamma Carleton (1), Solomon Hsiang (1), Jonathan, Kadish (1), Jonathan Proctor (1), Benjamin Recht (1), Esther Rolf (1),, Vaishaal Shankar (1) ((1) University of California, Berkeley)

TL;DR
This study demonstrates that satellite imagery can predict housing prices effectively, but the quality and size of ground truth data are crucial, guiding future adaptive sampling strategies in socioeconomic analysis.
Contribution
The paper introduces a modular system for satellite image analysis and quantifies the impact of data availability on socioeconomic predictions.
Findings
Satellite imagery predicts housing prices with high accuracy.
Data size and distribution significantly influence prediction usefulness.
Image classification method is a secondary factor to data quality.
Abstract
In this project, we build a modular, scalable system that can collect, store, and process millions of satellite images. We test the relative importance of both of the key limitations constraining the prevailing literature by applying this system to a data-rich environment. To overcome classic data availability concerns, and to quantify their implications in an economically meaningful context, we operate in a data rich environment and work with an outcome variable directly correlated with key indicators of socioeconomic well-being. We collect public records of sale prices of homes within the United States, and then gradually degrade our rich sample in a range of different ways which mimic the sampling strategies employed in actual survey-based datasets. Pairing each house with a corresponding set of satellite images, we use image-based features to predict housing prices within each of…
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Taxonomy
TopicsImpact of Light on Environment and Health · Remote-Sensing Image Classification · Land Use and Ecosystem Services
