Earth observation (EO) is crucial for monitoring environmental changes,
responding to disasters, and managing natural resources. In this context,
foundation models facilitate remote sensing image analysis to retrieve relevant
geoinformation accurately and efficiently. However, as these models grow in
size, fine-tuning becomes increasingly challenging due to the associated
computational resources and costs, limiting their accessibility and
scalability. Furthermore, full fine-tuning can lead to forgetting pre-trained
features and even degrade model generalization. To address this,
Parameter-Efficient Fine-Tuning (PEFT) techniques offer a promising solution.
In this paper, we conduct extensive experiments with various foundation model
architectures and PEFT techniques to evaluate their effectiveness on five
different EO datasets. Our results provide a comprehensive comparison, offering
insights into when and how PEFT methods support the adaptation of pre-trained
geospatial models. We demonstrate that PEFT techniques match or even exceed
full fine-tuning performance and enhance model generalisation to unseen
geographic regions, while reducing training time and memory requirements.
Additional experiments investigate the effect of architecture choices such as
the decoder type or the use of metadata, suggesting UNet decoders and
fine-tuning without metadata as the recommended configuration. We have
integrated all evaluated foundation models and techniques into the open-source
package TerraTorch to support quick, scalable, and cost-effective model
adaptation.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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