abstract
- Machine Learning (ML) holds significant potential for enhancing Remote Sensing (RS) data analysis. Today, many ML models have been introduced to process RS data, including a vast amount of Geospatial Foundation Models (GeoFM). However, selecting an effective (i.e., best performing) model is both time-consuming and technically challenging, especially for domain scientists without ML experience. Therefore, this work introduces a model recommendation method for a particular downstream task by using a lightweight approach. Our method is composed of a two-phase mechanism that leverages the similarity of feature vectors from provenance data within clustered representations. Thus, the recommendation is based on the accuracy and computational resources of each model. This allows us to yield a model recommendation and later selection for each sample point instead of the whole dataset. Importantly, high resource and energy gains are observed during the second phase of our method, leading to an efficient way to select ML-based solutions in RS applications.