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.

通过利用变分自编码器架构，本研究提出了一种衡量机器学习模型预测置信度的新方法，特别针对利用地球观测数据进行回归任务，以蚊子数量估计为重点。这种方法通过观测数据的潜在空间表示来派生置信度度量，建立了潜在表示的欧式距离与单个蚊子数量预测的绝对误差之间的相关性。我们的研究着重于意大利威尼托地区和德国上莱茵河流域的地球观测数据集，重点关注蚊子数量较为显著的地区。一个关键发现是蚊子数量预测的绝对误差与所提出置信度度量之间的相关性达到0.46，这一相关性意味着一种稳健的新指标，用于量化人工智能模型在地球观测数据分析和蚊子数量研究中预测的可靠性和可信度的提升。

增强地球观测数据预测置信度的潜在空间度量