1. Towards the operational service of crop classification
We are about to finish an R&D project, where we developed and tested crop classification methodology specifically suited for Estonian agricultural, ecological and climatic conditions. We relied mostly on Sentinel-1 and -2 data and used neural network machine learning approach to distinguish 28 different crop types. Results are promising and the methodology is ready for operational service to automate another part of agricultural monitoring.
Read more: https://kappazeta.ee/blog/towards-the-operational-service-of-crop-classification
Mihkel Järveoja, GIS developer
2. Data splitting challenge In machine learning applications, labelled training data is usually split into training, validation and test sets. The splitting is often performed by random sampling the input dataset according to a pre-defined ratio, such as 60% / 30% / 10%, for example. In this example, 60% of the shuffled input samples would be used for training, 30% of them for validation and the remaining 10% for the test set.
However, different samples might have different labelling confidence. For multiclass datasets it can be quite a challenge to ensure properly balanced classes throughout the datasets. To tackle these issues, we use a custom splitting logic for each project. The splitting logic supports configurable splitting ratios as well as configurable filters per dataset. It is also possible to configure the upper threshold for under-sampling and lower threshold for an alternative splitting ratio. Regardless of the configurability there tend to be nuances which make it difficult to use a common splitting logic for all projects.
From our perspective, there is a need for a generic framework which would simplify data splitting and make it easy to apply it to projects with different requirements and different types of datasets.
Read more: https://kappazeta.ee/blog/data-splitting-challenge
Indrek Sünter, software developer
Marharyta Domnich, machine learning engineer
3. New cloud mask In September 2020 we signed a contract with European Space Agency to develop an open source AI-based cloud mask processor for Sentinel-2. We are exploiting CNN architecture and the goal is ambitious – to develop the most accurate open source cloud mask for Sentinel-2. To limit the scope we are concentrating on Northern European summer season terrestrial conditions. To speed up the labelling we are applying active learning and exploitation of already labelled data sets. The resulting cloud mask will be free and open. If you wish to contribute contact us.
A longer cloud mask article will soon be published in our blog: https://kappazeta.ee/blog
Kaupo Voormansik, CEO
