Abstract
Many global and continental scale mapping projects today have to solve the same problems: finding an infrastructure with the required datasets, setting up a scalable processing system to produce results, configuring preprocessing workflows to generate Analysis Ready Data (ARD), and then scaling from test runs to the final continental or global map. After that appropriate metadata needs to be generated followed by a dissemination phase where products have to be made ready for viewing. OpenEO platform solves most of these problems, but how does that work out in a real-world scenario?
To demonstrate the efficiency and useability of OpenEO platform (https://openeo.cloud) we have generated a European crop type map at 10m resolution. The map is based on both Sentinel-1 and Sentinel-2 data, which makes it a suitable blueprint for many other mapping projects. The feature engineering capabilities of the platform transform this data into a set of training features, which are then sampled across Europe to build a training and validation dataset. The built-in classification processes are finally used to train a model, which is used subsequently to generate the final map, which is delivered as a set of cloud optimized geotiffs with SpatioTemporal Asset Catalog (STAC) metadata. The final product will be validated using a harmonized LPIS dataset based on the availability at country level.
In this presentation we will give an overview of the full product R&D and production lifecycle. We report on efficiency gains and potential remaining bottlenecks for users. This gives a good overview of platform capabilities, but also of the overall maturity with respect to being production-ready for continental scale mapping efforts. For the large scale production of the map, processing capacity available at VITO, CreoDIAS and EODC will be used containing large and local, data archives extended with data on Sentinel Hub accessed via Euro Data Cube. This federation of data and processing capacity is made transparent by openEO platform. A built-in large scale data production component is responsible for distributing the workload across the available infrastructure. This aims to show that OpenEO platform has reached a maturity level that allows users to engineer an entire ML pipeline from data to final classification on a large scale.