# A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data

arXiv: https://arxiv.org/abs/2401.17342 · HTML: https://mlsanity.com/paper/2401.17342/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 3 of 3)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions](https://mlsanity.com/paper/2104.06982/) (2021)

## Built on by (most cited first, 2 of 2)

- [Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space](https://mlsanity.com/paper/2409.13774/) (2024)
- [A Conformal Predictive Measure for Assessing Catastrophic Forgetting](https://mlsanity.com/paper/2505.10677/) (2025)

## Abstract (arXiv)

This study presents a new approach for estimating confidence in machine learning model predictions, specifically in regression tasks utilizing Earth Observation (EO) data, with a particular focus on mosquito abundance (MA) estimation. We take advantage of a Variational AutoEncoder architecture, to derive a confidence metric by the latent space representations of EO datasets. This methodology is pivotal in establishing a correlation between the Euclidean distance in latent representations and the Absolute Error (AE) in individual MA predictions. Our research focuses on EO datasets from the Veneto region in Italy and the Upper Rhine Valley in Germany, targeting areas significantly affected by mosquito populations. A key finding is a notable correlation of 0.46 between the AE of MA predictions and the proposed confidence metric. This correlation signifies a robust, new metric for quantifying the reliability and enhancing the trustworthiness of the AI model's predictions in the context of both EO data analysis and mosquito abundance studies.