Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing ^[1]. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface) ^[2]. Herein, authors propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL ^[3]) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. Large amounts of scanned and georeferenced historical maps (and map collections) are increasingly available to the public ^[4][5] and increasingly used as data sources for automated, retrospective landscape analyses ^[6][7]. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. Authors apply their method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US) ^[8][9], and against urban area estimates from the History Database of the Global Environment (HYDE) ^[10]. Authors' results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available ^[11].