In an increasingly interconnected global economy, resilience in transport chains is becoming increasingly important. Unforeseen events such as infrastructure failures or extreme weather conditions can cause significant delays and add to the already high complexity of transport planning. At the same time, insufficient data availability and a lack of comprehensive monitoring capabilities for transport networks pose additional challenges for transport logistics.

This is where the project steps in, exploring the potential of satellite imagery for transport planning and monitoring of transport networks. By combining expertise in the fields of remote sensing, image analysis, and logistics, the project analyzes how planning-relevant information can be extracted from RGB and SAR satellite images. The aim is to increase the resilience of freight transport planning, enable more informed decisions in transport planning, and address the “in-transit visibility problem.”

During the project, remote sensing data will be systematically evaluated, processed using artificial intelligence based on a case study of truck parking spaces at transport network hubs, and then integrated into transport planning decision-making processes.