Forest Disturbance Monitoring with Geospatial Foundation Models

The growing availability of Earth Observation (EO) data enables monitoring of terrestrial ecosystems at unprecedented spatio-temporal resolutions. In practice, however, effective use of EO data remains constrained by substantial technical barriers. Working with raw, multi-modal EO imagery requires specialised domain expertise, large data transfers, access to high-performance computing infrastructure, and advanced machine learning (ML) skills. These requirements limit the accessibility of EO-based analytics for many downstream applications.

Geospatial foundation models (GFMs) provide a promising alternative by learning general-purpose representations from large volumes of unlabeled EO data. By decoupling representation learning from downstream task modelling, GFMs allow users to exploit expressive features from modern deep learning models with limited EO or deep learning expertise and modest computational resources.

In this work, we investigate embedding-based GFM workflows for forest disturbance monitoring, where timely inference and regional customisation are often more critical than maximising absolute predictive accuracy. Forest disturbances such as logging, windthrows, fires, pests, and diseases can occur abruptly and require rapid detection to support conservation, policy-making, and risk-management efforts.

Machine learning methods for forest disturbance detection from EO data are well established and have shown strong performance on regional benchmarks. However, much of this work remains confined to academic demonstrations and is rarely translated into operational monitoring systems. Existing forest monitoring tools, including those aggregated by Global Forest Watch, typically rely on region- and sensor-specific models with limited feature expressivity. These systems may benefit from the rich multi-modal and spatio-temporal representations learned by GFMs, provided such embeddings can be accessed through scalable and practical deployment pipelines.

We build on a pipeline designed to deliver on-demand, location- and time-specific geospatial embeddings as a service. Embeddings are generated server-side from raw EO data, compressed, and distributed as lightweight representations. End users interact only with these embeddings, which can be analysed using simple models such as linear probes or small decoders. This approach removes the need for the user to manipulate raw EO data, download large multi-modal datasets, or train and deploy large deep learning models, enabling rapid adaptation to local contexts with limited annotations and modest computational resources.

We present preliminary results demonstrating the feasibility of this approach for forest disturbance detection and discuss its strengths and limitations relative to bespoke, fully supervised image-based models. While GFMs may not be optimal for applications with abundant annotations and stringent accuracy requirements, embedding-based services are particularly well suited to time-sensitive and regionally adaptive monitoring scenarios. Overall, this work illustrates how releasing geospatial embeddings as a product or service can lower barriers to EO-based forest monitoring and support faster, more inclusive environmental decision-making.