The Arctic sea ice deforms constantly due to stresses imposed by winds, ocean currents and interaction with coastlines. The most dominant features produced by this deformation in the ice cover are leads and pressure ridges that are often referred to as Linear Kinematic Features (LKFs). With increasing resolution of classical (viscous-plastic) sea ice models, or using new rheological frameworks (e.g. Maxwell elasto-brittle), sea-ice models start to resolve this small-scale deformation. So far, scaling properties of sea-ice deformation are commonly used to evaluate the modelled LKFs, besides other measures like lead area density. These metrics evade the problem of detecting individual LKFs by taking statistics over continuous fields like sea ice deformation or concentration. This way, they can provide specific information, but lack a comprehensive description of LKFs. We detect individual LKFs in sea ice deformation fields from satellite observations with an object detection algorithm. Combining this information with the sea ice drift fields used to derive the deformation fields, the LKFs are tracked in time. In doing so, the spatial characteristics (density, length, orientation, intersection angle, curvature) as well as the temporal evolution can be extracted from the same data-set. This algorithm can be applied to modelled sea-ice deformation and drift to enable a consistent comparison and thorough evaluation of simulated sea-ice deformation. We present preliminary results of LKFs detected in the RGPS data set and give examples of possible applications.