## 1 Introduction  
By 2050, 68% of the human population is projected to live in urban areas [1]. The increasing urbanization and related processes such as rural-urban migration, socio-economic changes, and land consumption are drivers of issues such as transportation congestion and increasing pollution, posing unprecedented challenges for urban planners and policymakers. In order to make our cities more sustainable, efficient, and resilient to increasingly occurring extreme weather events, natural hazards, and climate-change-related phenomena, a thorough understanding of the long-term development trajectories of urban areas is indispensable for urban planners and policymakers. However, spatially explicit data on the size and structure of urban areas (and their changes over time) are typically derived from remote-sensing-based earth observation data, and thus rarely available prior to the 1970s. This shortcoming severely limits our knowledge of historical urban-spatial development, and forces researchers to rely on an observational window of approximately 40 years for retrospective assessments [2; 3; 4] and to establish future projections of urban land [5]. Hence, researchers studying long-term historical (urban) land development either rely on model-based approaches (e.g., [6; 7]), on alternative data sources such as property data [8; 9; 10; 11], or on the use of historical maps [12; 13; 14; 15; 16] that are mostly constrained to relatively small areas, such as specific cities, regions, or countries, often involving manual digitization work. This research explores the use of historical maps more specifically, as many countries have some form of historical map series, which can be used to derive knowledge related to urban areas prior to the 1970s. Integrating such spatial data with existing global remote-sensing-based settlement layers facilitates building temporally extended depictions of historical urban development for any region in the world for which such map archives have been established. This kind of integration framework is presented herein. Recent advances in automated georeferencing [17; 18], cloud-based data storage technologies, and map acquisition methods have catalyzed the availability of large historical topographic map collections to the public, often holding thousands of (georeferenced) digital raster datasets, such as in the US [19; 20], the UK [21], Switzerland [22], or international collections [23; 24; 25; 26]. Moreover, advances in the automated processing of historical maps have opened new avenues for the efficient acquisition [27] and mining of large volumes of historical maps, and for the detection, recognition, and conversion of historical map content into digital, machine-readable data formats [28; 29; 30; 31]. Such recent efforts include the mining of (historical) map collections by their content or associated metadata [32; 33; 34; 35; 36; 37], automated georeferencing [18; 38; 39; 40] and alignment [41; 42], text detection and recognition [43; 44; 45], and the extraction of thematic map content, often involving (deep) machine learning methods, focusing on specific geographic features such as forest [46], railroads [33; 47], road network intersections [48; 49] and road types [50], archeological content [51] and mining features [52], cadastral parcels boundaries [53; 54], wetlands and other hydrographic features [55; 56], linear features in general [57], land cover/land use [58], urban street networks and city blocks [34], building footprints [13; 59; 60], and historical human settlement patterns [61; 62; 63]. Other approaches use deep-learning-based computer vision for generic segmentation of historical maps [64; 65], generative machine learning approaches for map style transfer [66; 67], or attempt to mimic historical overhead imagery based on historical maps [68]. Many of these approaches have been tested on maps dating back to the late 1800s or even earlier but are commonly evaluated on relatively small spatial extents only.