Federated learning is a machine learning approach that allows multiple organizations or devices to collaboratively learn a shared model while keeping their data localized. In remote sensing, this technique is particularly valuable because it enables various entities, like satellite operators or environmental monitoring agencies, to improve models for analyzing geographic data without having to share sensitive raw data. This is important due to privacy concerns, data ownership issues, and the large sizes of remote sensing datasets.
One practical application of federated learning in remote sensing can be seen in land cover classification. Different organizations may have remote sensing data from various regions, but sharing the original imagery can be challenging due to privacy regulations or proprietary interests. By using federated learning, each organization can train a portion of a shared neural network on its own data and only share the model updates (gradients) instead of the data itself. These updates are then aggregated in a central server, which refines the model. This process can improve land cover maps by combining insights from diverse geographic locations while keeping each organization's data secure.
Another example is the monitoring of environmental changes like deforestation or urban expansion. Federated learning allows organizations from multiple countries to build models that detect changes using their unique datasets, for instance, satellite images taken of different urban areas. Each organization can contribute to the model without exposing its raw data, thus fostering better collaboration across borders. By leveraging federated learning, remote sensing applications can enhance their model accuracy through collective learning, while respecting data privacy and integrity.