Satellite crop detection technologies are focused on the detection of different types of crops in fields. The information of crop-type area is more useful for food security than the earlier phenology stage is. Currently, data obtained from remote sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops; additionally, modern technologies using AI methods are desired in the postprocessing stage. In this paper, we develop a methodology for the supervised classification of time series of Sentinel-2 and Sentinel-1 data, compare the accuracies based on different input datasets and find how the accuracy of classification develops during the season. In the EU, a unified Land Parcel Identification System (LPIS) is available to provide essential field borders. To increase usability, we also provide a classification of the entire field. This field classification also improves overall accuracy.

As people grow accustomed to effortless outdoor navigation, there is a rising demand for similar possibilities indoors as well. Unfortunately, indoor localization, being one of the requirements for navigation, continues to be a problem without a clear solution. In this article, we are proposing a method for an indoor positioning system using a single image. This is made possible using a small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with the SIFT (Scale Invariant Feature Transform) algorithm, we can look through the database and find an image that is the most similar to the image taken by a user. Such a pair of images is then used to find coordinates of a database of images using the PnP problem. Furthermore, projection and essential matrices are determined to calculate the user image localization—determining the position of the user in the indoor environment. The benefits of this approach lie in the single image being the only input from a user and the lack of requirements for new onsite infrastructure. Thus, our approach enables a more straightforward realization for building management.

Digital farming holds enormous potential for agricultural development, and giving farmers the tools to boost productivity and profitability. Although the benefits of digitalization are numerous, farmers feel they are not the ones benefiting from the value of data collected on their farms. Several issues were identified as factors restricting farmers from benefiting from data-driven agriculture. From the farmers’ perspective, there is a distinct lack of awareness of the issues surrounding farm data, and the complexity of these issues. This feeds into the imbalance that exists between individual farmers and larger agribusinesses wherein the former lack enough resources to address and analyse the significance of data, and so cannot take advantage of the value in it. There is also limited legislation for the generation, flow, exchange and use of data; where legislation does exist, it is not well understood by farmer organisations. From a policy perspective, moreover, there is very little guidance as to which agricultural data can be considered personal data, and therefore protected by privacy laws. This paper analyses the interactions and effects of the 5 Concepts: Open Agricultural Data, Open-Source Software, Citizen Science, privacy and legal and ethical issues that are assumed to advance the digitalization of African Food System (AFS and the enabling Digital Innovation Hub (DIH) - SmartAfriHub (https://www.smartafrihub.com/home).

The EO4Agri Strategic Research Agenda (SRA) is a set of recommendations for future research activities in the area of Earth observation for agriculture. The EO4AGRI project provides support to all agri-food sectors based on new uses of COPERNICUS data. At first, part of the deliverable collected user needs from previous work are summarised including gaps in data, delivery platforms and knowledge management. Another input was an analysis of the current political framework and its influence on future agriculture. The implementation of the European Green Deal and the UN Sustainable Development Goals will require future collaboration of the public and private sectors. The main part of the SRA is a list of recommendations for future activities in the Group on Earth Observations (GEO), Horizon Europe (Annex 4 and Annex 6) and the Digital Europe programmes. It is not a revision of these programmes, but additional recommendations or tasks which are important to consider in updating the future programmes.