On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?
This work addresses the problem of optimizing multi-source models for Earth Observation applications, offering insights that could streamline data usage, though it is incremental in nature.
The study evaluated six state-of-the-art multi-source models in Earth Observation to understand factors affecting their robustness to missing data, finding that task nature, data complementarity, and model design influence performance, with some cases showing improved results when data sources are removed.
In recent years, the development of robust multi-source models has emerged in the Earth Observation (EO) field. These are models that leverage data from diverse sources to improve predictive accuracy when there is missing data. Despite these advancements, the factors influencing the varying effectiveness of such models remain poorly understood. In this study, we evaluate the predictive performance of six state-of-the-art multi-source models in predicting scenarios where either a single data source is missing or only a single source is available. Our analysis reveals that the efficacy of these models is intricately tied to the nature of the task, the complementarity among data sources, and the model design. Surprisingly, we observe instances where the removal of certain data sources leads to improved predictive performance, challenging the assumption that incorporating all available data is always beneficial. These findings prompt critical reflections on model complexity and the necessity of all collected data sources, potentially shaping the way for more streamlined approaches in EO applications.