Towards Streaming Land Use Classification of Images with Temporal Distribution Shifts

In this study, we introduce a new pipeline that integrates Streaming Machine Learning (SML) models and the Momentum Contrastive Learning (MoCo) technique for the streaming classification of satellite images subject to temporal variations in distribution. We present preliminary results of an experimental campaign conducted on the Functional Map of the World-Time dataset, one of the first benchmarks specifically designed to address temporal distribution shifts in satellite imagery. The results demonstrate that the proposed pipeline enhances robustness and generalization over time, surpassing traditional strategies.