Data-Efficient Deep Learning for Earth Observation
Type
conference contribution
Date Issued
2024-07-07
Author(s)
Abstract
Deep Learning methods have proven highly successful across a wide range of Earth Observation (EO)-related downstream tasks, such as image classification, image-based regression and semantic segmentation. Supervised learning of such tasks typically requires large amounts of labeled data, which oftentimes are expensive to acquire, especially for EO data. Recent advances in Deep Learning provide the means to drastically reduce the amount of labeled data needed to train models with a given performance and to improve the general performance of these models on a range of downstream tasks. As part of this tutorial, we will introduce and showcase the use of three such approaches that strongly leverage the multi-modal nature of EO data: Data Fusion, Multi-task learning and Self-supervised Learning. The fusion of multi-modal data may improve the performance of a model by providing additional information; the same applies to multi-task learning, which supports the model in generating richer latent representations of the data by means of learning different tasks. Self-supervised learning enables the learning of rich latent representations based on large amounts of unlabeled data, which are ubiquitous in EO, thereby improving the general performance of the model and reducing the amount of labeled data necessary to successfully learn a downstream task. We will introduce the theoretical concepts behind these approaches and provide hands-on tutorials for the participants utilizing Jupyter Notebooks. Participants, who are required to have some basic knowledge in Deep Learning with Pytorch, will learn through realistic use cases how to apply these approaches in their own research for different data modalities (Sentinel-1, Sentinel-2, land-cover data, elevation data, seasonal data, weather data, etc.). Finally, the tutorial will provide the opportunity to discuss the participants’ use cases.