A Latent Space Metric for Enhancing Prediction Confidence in Earth
  Observation Data
Ioannis Pitsiorlas Argyro Tsantalidou George Arvanitakis Marios Kountouris Charalambos Kontoes
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.