Earth Observation & Climate Research through Self-Supervised Learning

Earth Observation (EO) satellites generate petabytes of data annually to monitor environmental changes, from land surface dynamics to atmospheric processes. While raw satellite data is increasingly accessible, expert annotations needed to derive insights are much more sparse, limiting the potential of supervised machine learning approaches. Self-supervised learning (SSL) addresses this challenge by first learning meaningful features from unlabeled data, after which models can be adapted to specific tasks using only a small number of labeled examples.
Our research demonstrates SSL's versatility in two critical domains: land monitoring and cloud modeling. For land monitoring, we develop models that are pre-trained on Sentinel-1 Synthetic Aperture Radar (SAR) or Sentinel-2 optical imagery, and later fine-tuned for tasks like vegetation prediction, biomass estimation, and land cover mapping. We compare different state-of-the-art SSL techniques based on spatial masking and feature matching, and show that SSL strongly reduces the requirements for labeled examples.

For our second application, we extend this approach to 3D cloud modeling - crucial for reducing uncertainties in climate models. We first learn cloud features through SSL on geostationary satellite imagery, and fine-tune our models on limited radar measurements of atmospheric profiles. By explicitly encoding space and time, we create geospatially-aware models that can generate large-scale 3D cloud maps with low regional biases. With this, we demonstrate the potential of SSL to advance Earth System understanding, and maximally leverage the wealth of information in EO data.


The AI for Good Global Summit is the leading action-oriented United Nations platform promoting AI to advance health, climate, gender, inclusive prosperity, sustainable infrastructure, and other global development priorities. AI for Good is organized by the International Telecommunication Union (ITU) – the UN specialized agency for information and communication technology – in partnership with 40 UN sister agencies and co-convened with the government of Switzerland.

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