Confidence assessments of semantic segmentation algorithms in remote sensing
are important. It is a desirable property of models to a priori know if they
produce an incorrect output. Evaluations of the confidence assigned to the
estimates of models for the task of classification in Earth Observation (EO)
are crucial as they can be used to achieve improved semantic segmentation
performance and prevent high error rates during inference and deployment. The
model we develop, the Confidence Assessments of classification algorithms for
Semantic segmentation (CAS) model, performs confidence evaluations at both the
segment and pixel levels, and outputs both labels and confidence. The outcome
of this work has important applications. The main application is the evaluation
of EO Foundation Models on semantic segmentation downstream tasks, in
particular land cover classification using satellite Copernicus Sentinel-2
data. The evaluation shows that the proposed model is effective and outperforms
other alternative baseline models.

通过分割和像素级别的置信度评估，开发了用于遥感语义分割算法的置信度模型，并在 Copernicus Sentinel-2 卫星数据的地表覆盖分类等任务中表现出比其他基准模型更好的效果。