Transductive Transfer-Learning for Land Use Land Cover Classification using Geospatial Foundation Models

Remote sensing for land use and land cover (LULC) classification using satellite imagery provides valuable insights to the prediction of the dynamical change of land use, the risk assessments due to climate change by identifying areas prone to natural disasters, the protection of the environment, as well as the land management and planning. In recent years, deep learning-based image analysis approaches, such as geospatial foundation models for earth observation data that are trained through self-supervised learning, are increasingly used for earth observation tasks. A typical downstream task for such models is LULC classification, where the foundation model is fine-tuned using a set of labeled data. Depending on the dataset, these labels can be very specific to certain targets (only agriculture-focused labels), or to a certain region (only covering the US), and usually vary a lot in spatial resolution. To improve the generalizability and accuracy, there is a need for LULC models that better capture diverse land use patterns across different geographical areas. Here, we introduce an approach leveraging techniques from domain adaptation to train an existing LULC model with new data. This allows us to integrate information of a target LULC application into the model more easily and improve the model's performance. We were able to show that our approach increased the accuracy by $32\%$ and $7\%$ compared to fine-tuning the model with different labels individually, while also reducing the training time by over $65\%.$