Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing

TL;DR

This paper presents a novel framework that combines multi-spectral remote sensing data with uncertainty quantification techniques to improve socio-economic estimation and guide data collection efforts.

Contribution

It introduces a new approach using foundational vision models with multi-spectral bands and uncertainty modeling for better socio-economic predictions from remote sensing.

Findings

Outperforms existing RGB and multi-spectral models in accuracy.

Effectively identifies uncertain predictions for targeted data collection.

Enhances socio-economic estimation with uncertainty-aware remote sensing.

Abstract

Remote sensing imagery offers rich spectral data across extensive areas for Earth observation. Many attempts have been made to leverage these data with transfer learning to develop scalable alternatives for estimating socio-economic conditions, reducing reliance on expensive survey-collected data. However, much of this research has primarily focused on daytime satellite imagery due to the limitation that most pre-trained models are trained on 3-band RGB images. Consequently, modeling techniques for spectral bands beyond the visible spectrum have not been thoroughly investigated. Additionally, quantifying uncertainty in remote sensing regression has been less explored, yet it is essential for more informed targeting and iterative collection of ground truth survey data. In this paper, we introduce a novel framework that leverages generic foundational vision models to process remote sensing imagery using combinations of three spectral bands to exploit multi-spectral data. We also employ methods such as heteroscedastic regression and Bayesian modeling to generate uncertainty estimates for the predictions. Experimental results demonstrate that our method outperforms existing models that use RGB or multi-spectral models with unstructured band usage. Moreover, our framework helps identify uncertain predictions, guiding future ground truth data acquisition.