Interferometric Synthetic Aperture Radar (InSAR) has become an established method for monitoring displacements of the Earth surface over wide areas. However, German land surveying authorities have not fully exploited its capabilities, due to the need for specialised InSAR time series software, computational power and expertise in data interpretation. To make ground displacement maps accessible to all potential users, the European Ground Motion Service (EGMS) offers InSAR products from Sentinel-1 images, such as mean velocity estimates and displacement time series. These products can help study various geophysical and anthropogenically induced processes in Europe and has a great potential for assessing and mitigating risks related to these processes. However, the EGMS products are based on methodologies that may not suit all applications. For example, monitoring linear infrastructures such as highways, railway tracks and power lines requires a tailored method. The SAR4Infra project fills this gap by developing a methodology and processing pipeline for reliable displacement estimation at linear infrastructures. With this project, we support German land surveying authorities by providing an automated risk map for traffic infrastructures which complements their in-situ measurements. To do so, we identified the challenges in InSAR processing in particular when deriving the displacement time series for linear infrastructures. Most of these challenges are related to the spatial sparseness of the linear infrastructures. Although they cover large areas in length, they are only a few pixels wide. As a result, linear infrastructures are often covered by a minimal number of pixels in medium spatial resolution images from Sentinel-1. To retrieve the displacement, scatterers with coherent signals have to be carefully selected. Additionally, atmospheric effects have to be mitigated properly and phase unwrapping needs to be adapted to the spatial sparsity to reduce the propagation of errors and noise. An open-source InSAR software has been developed to address these challenges. In this contribution, we compare the results from the SAR4Infra InSAR processing chain with the products from the EGMS and evaluate the performance of different methods based on case studies in Schleswig-Holstein and Nordrhein-Westfalen.