Grey infrastructure is an integral part of the urban environment. Continuous modernization of architecture, construction, routes or services in that region leads to more and more new grey infrastructure appearing. The reason for this are constant migrations of people, dissemination of a healthy lifestyle or improvement of its level. Its growth is particularly noticeable in agglomerations where keeping the balance between sealed and vegetated area is very much concerned. Therefore, it is necessary to constantly monitor changes over time and thus update the databases containing information on land cover such as the Topographical Database. For this purpose VHR images were processed and analysed in terms of detection efficiency of topographical objects defined as grey infrastructure. This study presents the results of an analysis of the possibility of updating the land cover classes in the Topographical Database based on WorldView-2 satellite images.The methods used to detect grey infrastructure come from a machine learning approach such as Random Forests and parametric Maximum Likelihood classifier, resulting at a 90% level of accuracy.The other aim of the work was to analyse changes in the grey infrastructure on the basis of the Topographic Database at scale 1:10000 using a VHR satellite image. The analysis of its changes was carried out on the dynamically developing city of Warsaw.