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CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models

Nikolaos Dionelis, Jente Bosmans, Nicolas Longépé

TL;DR

The paper tackles the challenge of confidence quantification for pixel-wise regression in Earth Observation Foundation Models, focusing on building density estimation from Sentinel-2 data. It introduces CARE, a dual-head U-Net that outputs both a regression value and a confidence score, trained with a composite loss L = L0 + \lambda L1 and a mini-batch sample-sorting strategy to assign per-pixel confidence. CARE demonstrates superior performance over baselines on the PhilEO Bench dataset, with a strong correlation between confidence and error and the ability to abstain on low-confidence predictions to improve reliability. The work highlights the practical significance of integrating uncertainty estimation into regression tasks for EO, enabling self-correction, anomaly detection, and improved decision-making in urban planning and environmental monitoring.

Abstract

Performing accurate confidence quantification and assessment in pixel-wise regression tasks, which are downstream applications of AI Foundation Models for Earth Observation (EO), is important for deep neural networks to predict their failures, improve their performance and enhance their capabilities in real-world applications, for their practical deployment. For pixel-wise regression tasks, specifically utilizing remote sensing data from satellite imagery in EO Foundation Models, confidence quantification is a critical challenge. The focus of this research work is on developing a Foundation Model using EO satellite data that computes and assigns a confidence metric alongside regression outputs to improve the reliability and interpretability of predictions generated by deep neural networks. To this end, we develop, train and evaluate the proposed Confidence-Aware Regression Estimation (CARE) Foundation Model. Our model CARE computes and assigns confidence to regression results as downstream tasks of a Foundation Model for EO data, and performs a confidence-aware self-corrective learning method for the low-confidence regions. We evaluate the model CARE, and experimental results on multi-spectral data from the Copernicus Sentinel-2 satellite constellation to estimate the building density (i.e. monitoring urban growth), show that the proposed method can be successfully applied to important regression problems in EO and remote sensing. We also show that our model CARE outperforms other baseline methods.

CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models

TL;DR

The paper tackles the challenge of confidence quantification for pixel-wise regression in Earth Observation Foundation Models, focusing on building density estimation from Sentinel-2 data. It introduces CARE, a dual-head U-Net that outputs both a regression value and a confidence score, trained with a composite loss L = L0 + \lambda L1 and a mini-batch sample-sorting strategy to assign per-pixel confidence. CARE demonstrates superior performance over baselines on the PhilEO Bench dataset, with a strong correlation between confidence and error and the ability to abstain on low-confidence predictions to improve reliability. The work highlights the practical significance of integrating uncertainty estimation into regression tasks for EO, enabling self-correction, anomaly detection, and improved decision-making in urban planning and environmental monitoring.

Abstract

Performing accurate confidence quantification and assessment in pixel-wise regression tasks, which are downstream applications of AI Foundation Models for Earth Observation (EO), is important for deep neural networks to predict their failures, improve their performance and enhance their capabilities in real-world applications, for their practical deployment. For pixel-wise regression tasks, specifically utilizing remote sensing data from satellite imagery in EO Foundation Models, confidence quantification is a critical challenge. The focus of this research work is on developing a Foundation Model using EO satellite data that computes and assigns a confidence metric alongside regression outputs to improve the reliability and interpretability of predictions generated by deep neural networks. To this end, we develop, train and evaluate the proposed Confidence-Aware Regression Estimation (CARE) Foundation Model. Our model CARE computes and assigns confidence to regression results as downstream tasks of a Foundation Model for EO data, and performs a confidence-aware self-corrective learning method for the low-confidence regions. We evaluate the model CARE, and experimental results on multi-spectral data from the Copernicus Sentinel-2 satellite constellation to estimate the building density (i.e. monitoring urban growth), show that the proposed method can be successfully applied to important regression problems in EO and remote sensing. We also show that our model CARE outperforms other baseline methods.

Paper Structure

This paper contains 12 sections, 3 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Our proposed methodology
  4. Proposed method
  5. CARE flowchart diagram
  6. Evaluation and Results
  7. Evaluation of CARE using absolute error in Table I and comparison to other models
  8. Further evaluation of CARE based on threshold $\zeta$, Table I
  9. Evaluation of CARE using n-shot experiments in Table II
  10. Baseline model n-shot evaluation, Table III
  11. Qualitative evaluation of CARE
  12. Conclusion

Figures (4)

  • Figure 1: CARE for confidence-aware regression: assign confidence metric to predictions, identify wrong predictions and refine the model.
  • Figure 2: Pixel-wise regression and confidence estimation and assessment by the model CARE described in Sec. \ref{['sec:secmethod']} on Sentinel-2 data.
  • Figure 3: CARE regression and confidence estimation: Building density.
  • Figure 4: Regression and confidence quantification: CARE using \ref{['eq:newequ1']}-\ref{['eq:confidequation']}.