Large remote sensing data sets are available through publicly funded earth observation programs. Given the increasing demands on the timeliness and quality of the digital landscape model of the Authorative Topographic-Cartographic Information System (ATKIS® Basis-DLM), it is necessary to develop new processes that enable an automated analysis of remote sensing data. By setting up a powerful AI computing infrastructure, the Hessian Administration for Land Management and Geoinformation (HVBG) has layn the foundations for automatically detecting changes in the landscape based on remote sensing data and AI. Within this talk, processes are presented that aim at increasing the quality and timeliness of the ATKIS® Basis-DLM of Hesse.