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

arXiv:2403.14297v1 Announce Type: new
Abstract: 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 …

abstract applications arxiv assessment availability cs.ai cs.cv cs.lg data data sources earth earth observation however impact machine machine learning machine learning models noise observation predictions type will