Earth observation (EO) applications involving complex and heterogeneous data
sources are commonly approached with machine learning models. However, there is
a common assumption that data sources will be persistently available. Different
situations could affect the availability of EO sources, like noise, clouds, or
satellite mission failures. In this work, we assess the impact of missing
temporal and static EO sources in trained models across four datasets with
classification and regression tasks. We compare the predictive quality of
different methods and find that some are naturally more robust to missing data.
The Ensemble strategy, in particular, achieves a prediction robustness up to
100%. We evidence that missing scenarios are significantly more challenging in
regression than classification tasks. Finally, we find that the optical view is
the most critical view when it is missing individually.

地球观测应用中使用机器学习模型来处理复杂和异构的数据源，但是对于数据源的持久性可用性存在一种常见的假设。本研究评估了训练模型中缺失时间性和静态的观测数据源对于四个数据集中分类和回归任务的影响，并比较了不同方法的预测质量，发现其中一些方法对于缺失数据自然更具鲁棒性。特别是，集成策略实现了高达 100％的预测稳健性。我们发现，在回归任务中缺失情景比分类任务更具挑战性。最后，我们发现当光学视图单独缺失时，它是最关键的视图。

地球观测应用模型预测中缺失数据的影响评估

Impact Assessment of Missing Data in Model Predictions for Earth  Observation Applications

Machine learning models always make a prediction, even when it is likely to
be inaccurate. This behavior should be avoided in many decision support
applications, where mistakes can have severe consequences. Albeit already
studied in 1970, machine learning with a reject option recently gained
interest. This machine learning subfield enables machine learning models to
abstain from making a prediction when likely to make a mistake.
This survey aims to provide an overview on machine learning with a reject
option. We introduce the conditions leading to two types of rejection,
ambiguity and novelty rejection. Moreover, we define the existing architectures
for models with a reject option, describe the standard learning strategies to
train such models and relate traditional machine learning techniques to
rejection. Additionally, we review strategies to evaluate a model's predictive
and rejective quality. Finally, we provide examples of relevant application
domains and show how machine learning with rejection relates to other machine
learning research areas.

此调查研究了具有拒绝选项的机器学习，介绍了拒绝的两种类型，现有模型的结构和训练策略，以及如何评估模型的预测能力和拒绝质量，并提供了相关应用领域的示例。

带有拒绝选项的机器学习：一份调查

Machine Learning with a Reject Option: A survey

It is commonly believed that increasing the interpretability of a machine
learning model may decrease its predictive power. However, inspecting
input-output relationships of those models using visual analytics, while
treating them as black-box, can help to understand the reasoning behind
outcomes without sacrificing predictive quality. We identify a space of
possible solutions and provide two examples of where such techniques have been
successfully used in practice.

通过视觉分析技术，我们可以在不牺牲预测质量的情况下理解黑盒机器学习模型的推理过程，从而提高模型的可解释性。