In recent years, machine learning has become crucial in remote sensing analysis, particularly in the domain of Land-use/Land-cover (LULC). The synergy of machine learning and satellite imagery analysis has demonstrated significant productivity in this field, as evidenced by several studies. A notable challenge within this area is the semantic segmentation mapping of land usage over extensive territories, where the accessibility of accurate land-use data and the reliability of ground truth land-use labels pose significant difficulties. For example, providing a detailed and accurate pixel-wise labeled dataset of the Flanders region, a first-level administrative division of Belgium, can be particularly insightful. Yet there is a notable lack of regulated, formalized datasets and workflows for such studies in many regions globally. This paper introduces a comprehensive approach to addressing these gaps. We present a densely labeled ground truth map of Flanders paired with Sentinel-2 satellite imagery. Our methodology includes a formalized dataset division and sampling method, utilizing the topographic map layout 'Kaartbladversnijdingen,' and a detailed semantic segmentation model training pipeline. Preliminary benchmarking results are also provided to demonstrate the efficacy of our approach.

In this work, we propose FLANDERS, a novel federated learning (FL) aggregation scheme robust to Byzantine attacks. FLANDERS considers the local model updates sent by clients at each FL round as a matrix-valued time series. Then, it identifies malicious clients as outliers of this time series by comparing actual observations with those estimated by a matrix autoregressive forecasting model. Experiments conducted on several datasets under different FL settings demonstrate that FLANDERS matches the robustness of the most powerful baselines against Byzantine clients. Furthermore, FLANDERS remains highly effective even under extremely severe attack scenarios, as opposed to existing defense strategies.

Jan Buytaert is chief information officer at GO!, the public body for state schools in the Flanders region of Belgium. His role is to initiate new IT projects and prove their value to the business, with the hope that business decision makers and policymakers give them the green light. The projects can have huge implications for education in Belgium, as the region has around 750 schools and institutions, and 210,000 students. "There wasn't always a lot of digital innovation so I had to work hard trying to convince management and policymakers that we should invest in tech and digital education, and change the way of teaching and learning," Buytaert tells NS Tech. In 2016, Buytaert and his team analysed the way teaching was carried out in several schools, working alongside teachers, students and principals.