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

• URL: http://arxiv.org/abs/2502.13734v2
• Date: Thu, 03 Apr 2025 10:04:54 GMT
• Title: CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models
• Authors: Nikolaos Dionelis, Jente Bosmans, Nicolas Longépé,
• Abstract summary: This research work is on developing a Foundation Model using EO satellite data that computes and assigns a confidence metric alongside regression outputs.<n>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.
• Score: 0.9558392439655016
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• KRAIL: A Knowledge-Driven Framework for Base Human Reliability Analysis Integrating IDHEAS and Large Language Models [2.7378790256389047]
  This paper introduces a novel two-stage framework for knowledge-driven reliability analysis, integrating IDHEAS and LLMs (KRAIL) Inspired by the success of large language models (LLMs) in natural language processing, this paper introduces a novel two-stage framework for knowledge-driven reliability analysis.
  arXiv  Detail & Related papers  (2024-12-20T06:21:34Z)
• Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regression [3.409728296852651]
  We introduce two methods that adapt the Average-Over-Time Spiking Neural Network (AOT-SNN) framework to regression tasks.<n>We evaluate our approaches on both a toy dataset and several benchmark datasets.
  arXiv  Detail & Related papers  (2024-11-29T23:13:52Z)
• Improving EO Foundation Models with Confidence Assessment for enhanced Semantic segmentation [0.0]
  We develop a Confidence Assessment for enhanced Semantic segmentation (CAS) model. It evaluates confidence at both the segment and pixel levels, providing both labels and confidence scores as output. This work has significant applications, particularly in evaluating EO Foundation Models on semantic segmentation downstream tasks.
  arXiv  Detail & Related papers  (2024-06-26T12:05:49Z)
• Predictability Analysis of Regression Problems via Conditional Entropy Estimations [1.8913544072080544]
  Conditional entropy estimators are developed to assess predictability in regression problems. Experiments on synthesized and real-world datasets demonstrate the robustness and utility of these estimators.
  arXiv  Detail & Related papers  (2024-06-06T07:59:19Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation with GPT-4 in Cloud Incident Root Cause Analysis [17.362895895214344]
  Large language models (LLMs) are used to help humans identify the root causes of cloud incidents. We propose to perform confidence estimation for the predictions to help on-call engineers make decisions on whether to adopt the model prediction. We show that our method is able to produce calibrated confidence estimates for predicted root causes, validate the usefulness of retrieved historical data and the prompting strategy.
  arXiv  Detail & Related papers  (2023-09-11T21:24:00Z)
• GEO-Bench: Toward Foundation Models for Earth Monitoring [139.77907168809085]
  We propose a benchmark comprised of six classification and six segmentation tasks. This benchmark will be a driver of progress across a variety of Earth monitoring tasks.
  arXiv  Detail & Related papers  (2023-06-06T16:16:05Z)
• Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust [2.0393477576774752]
  Our research introduces a method for uncertainty estimation. It enhances any base regression model with reasonable uncertainty estimates. Using various time-series forecasting data, we found that our surrogate model-based technique delivers significantly more accurate confidence intervals.
  arXiv  Detail & Related papers  (2023-02-06T14:52:56Z)
• Learning to be a Statistician: Learned Estimator for Number of Distinct Values [54.629042119819744]
  Estimating the number of distinct values (NDV) in a column is useful for many tasks in database systems. In this work, we focus on how to derive accurate NDV estimations from random (online/offline) samples. We propose to formulate the NDV estimation task in a supervised learning framework, and aim to learn a model as the estimator.
  arXiv  Detail & Related papers  (2022-02-06T15:42:04Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• MDN-VO: Estimating Visual Odometry with Confidence [34.8860186009308]
  Visual Odometry (VO) is used in many applications including robotics and autonomous systems. We propose a deep learning-based VO model to estimate 6-DoF poses, as well as a confidence model for these estimates. Our experiments show that the proposed model exceeds state-of-the-art performance in addition to detecting failure cases.
  arXiv  Detail & Related papers  (2021-12-23T19:26:04Z)
• SLURP: Side Learning Uncertainty for Regression Problems [3.5321916087562304]
  We propose SLURP, a generic approach for regression uncertainty estimation via a side learner. We test SLURP on two critical regression tasks in computer vision: monocular depth and optical flow estimation.
  arXiv  Detail & Related papers  (2021-10-21T14:50:42Z)
• Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates [68.09049111171862]
  This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. We formulate the regression-free model updates into a constrained optimization problem. We empirically analyze how model ensemble reduces regression.
  arXiv  Detail & Related papers  (2021-05-07T03:33:00Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Cross Learning in Deep Q-Networks [82.20059754270302]
  We propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods. Our algorithm builds on double Q-learning, by maintaining a set of parallel models and estimate the Q-value based on a randomly selected network.
  arXiv  Detail & Related papers  (2020-09-29T04:58:17Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• TraDE: Transformers for Density Estimation [101.20137732920718]
  TraDE is a self-attention-based architecture for auto-regressive density estimation. We present a suite of tasks such as regression using generated samples, out-of-distribution detection, and robustness to noise in the training data.
  arXiv  Detail & Related papers  (2020-04-06T07:32:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.