Pretraining AI models for earth observation: transfer-learning and meta-learning

Pretraining involves training an AI model on a large dataset to learn general features, which can then be finetuned on specific tasks with smaller datasets. This decreases the need for time intensive dataset acquisition and training efforts for each new use case, reducing the costs of application development. While pretrained models are widely used in computer vision and natural language processing, their adoption for satellite data and earth observation applications remains limited. Our investigation focuses on comparing the capabilities of transfer-learning and meta-learning approaches for the pretraining of AI models in earth observation tasks, particularly crop type classification, and their potential to generalize insights across different geographical regions.