Earth Observation (EO) covers everything from drones to satellite imagery, and its applications have a huge impact on everyday life: predicting weather, tracking container ships, monitoring agriculture and so much more. These applications require the processing of an enormous amount of data, which can only be processed with automated methods.
Deep learning (DL), the most successful branch of artificial intelligence (AI), relies on both a high amount of carefully curated data and specialised models. This leads to a problem: experts are required both for data collection and for designing appropriate DL models. Therefore, if a small company would like to utilise AI for customised mapping, that would require significant expertise and cost.
The goal of the ATELIER-EO (Automated machine learning framework tailored to Earth Observation) project is to develop an automated artificial intelligence solution for EO. More effective learning schemes will reduce the amount of required data, while the automated machine learning will reduce the necessary expertise.
As an example, the automatic mapping of forest roads is currently very challenging: roads are small, and while a human knows they are connected, the tree cover often hides parts of the road. ATELIER-EO will be able to automatically handle this connectivity, as well as incorporating terrain models and mitigating other issues like clouds in the images. The only input required will be training images where example roads are present.
In conclusion, ATELIER-EO will be able to automatically create customized maps from satellite or drone images with limited data and minimal human interaction. This will broaden the domains where AI can be applied, reducing the cost of customised products.
Science and Technology AS (ST) is a young company specialised in the development of services using Artificial Intelligence (AI) applied to EO and onboard processing systems for satellites. Most notably, we focus on the efficient use of satellite data for sustainable EO services. The share of AI is continuously growing in ST’s portfolio. The company has ample experience in the execution of large scientific data processing and intelligent software application projects for both ESA, EU, and national clients within, but not limited to, the domains of forestry (e.g. disease detection, deforestation, tree species) and ice sheet monitoring (e.g. calving front lines, mapping of supraglacial lakes, snowpack properties).
DL has already shown great success in the areas mentioned above, despite two major bottlenecks: limited EO-specific domain knowledge within the DL field (and vice versa, limited DL knowledge in the EO field), and a long time to market. Together with the large amount of data often required, these bottlenecks currently limit ST’s commercial opportunities and the adoption of AI within the EO field both in Norway and internationally.
This R&D project aims at extending and streamlining ST’s current DL technology stack, transforming it into an EO-specific, AutoML solution. Within this context, AutoML automates the tasks of applying ML to real-world problems, covering a complete pipeline from the raw dataset to the deployable machine learning model.
Critical milestones of this project are related to the research required to reach the goals described above and incorporated in the progress plan.