Optimising the data path for Earth-observation inference at scale

Whether it’s for monitoring floods, assessing agricultural conditions, or detecting wildfires, every Earth-Observation AI system based on satellite remote-sensing data shares a common task: given the area of interest and a time window, find the products that cover it and turn them into model inputs.

Intuitively, a live discovery approach appears attractive: products are discovered, retrieved, and prepared as part of each model run, avoiding the complexity of maintaining a separate data layer.

News and Events

Satellite-Based Water Change Detection: Monitoring Anomalies

Water change detection is one of the most critical applications of modern remote sensing. As climate volatility increases, the ability to accurately quantify surface water extent and soil moisture levels has become a priority for hydrologists, agricultural practitioners, policymakers and urban planners alike. Utilizing multi-spectral (Sentinel-2) and Synthetic Aperture Radar (Sentinel-1) data, we can now map hydrologic anomalies (droughts or floods) with unprecedented precision.

In the DaFab project, the water change detection workflow is used to enrich satellite data with additional parameters that indicate flood or drought in the scenes. It helps in optimizing the search, automating the selection of the past flood or drought events visible in Sentinel-1 / Sentinel-2 images and finally monitoring, analysing the affected areas from the generated water masks.

News and Events

State-of-the-art review for crop field boundaries delineation from satellite data

Introduction

The delineation of agricultural field boundaries from satellite imagery is crucial for precision agriculture, land management, policymaking and crop monitoring.

It can be a stand-alone solution or as the first stage for crop classification, advanced soil moisture analysis, yield forecast, the estimation of damages to crop yield due to natural (e.g. drought, floods) and more.

In the DaFab project, field delineation, which involves outlining agricultural parcels with polygons, offers a solution to several difficulties encountered in yield forecasting and crop classification tasks. This approach is more effective than traditional pixel-by-pixel classification maps because of the reasons: