The Arctic sea ice deforms continuously 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. Typical measures for evaluating the modelled LKFs include scaling properties of sea-ice deformation or lead area density. These metrics avoid the problem of detecting individual LKFs by applying statistics over continuous fields such as sea ice deformation or concentration. In this way, these statistical metrics 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) and the temporal evolution can be extracted from the same data-set. Our algorithm can be applied to both observed and modelled sea-ice deformation and drift making possible a consistent comparison and thorough evaluation.