## 1 Introduction
Thus, it remains unknown how such methods perform for large-scale information extraction from large volumes of historical maps, covering large spatial extents, stretching across different time periods, cartographic designs, or map scales. As a consequence, researchers have begun to develop historical map processing frameworks for large-scale data mining and extraction of heterogeneous information in a robust, feasible, and efficient manner [33]. Herein, we propose such a framework, applied to the extraction of historical urban extents. More specifically, we use urban extents from the Global Human Settlement Layer (GHSL) [69] to narrow down the regions in which urban areas in the historical maps likely occur. The GHSL data product contains the first, high-resolution settlement layer (i.e., on a 30 \(\times\) 30 m grid) consistently enumerated at a global scale for the time period from 1975 to 2014 [70]. The framework presented herein combines historical maps with remote-sensing-derived settlement layers in order to extend the GHSL retrospectively (i.e., to time periods before 1975) and is, to our knowledge, the first study that analytically combines signals obtained from historical topographic maps with remote-sensing-derived data for urban change analysis. Herein, we aim to answer the following research questions:  
1. Is the integrated use of signals from historical maps and from remote sensing data beneficial for the field of urban analysis?What are the challenges and the requirements for an analytical framework in order to be used for the joint analysis of historical maps and remote sensing data? How do the extracted urban areas from historical maps agree with other spatial-historical datasets? Our method makes use of a back-casting strategy. While the term back-casting often refers to a specific planning strategy working backwards from a desired future state [71], the term back-casting (or hind-casting) is also used for models that generally aim to re-create past conditions [72]. The back-casting strategy employed herein spatially constrains extraction results from historical maps (from the early 1900s) to be within built-up areas from the GHSL in 1975 (see Section 2.2.2). This strategy of constraining earlier urban extents to be within the more recent and presumably more reliable area depictions [73] is commonly used in multi-temporal urban monitoring [74; 75; 76; 77; 70], as opposed to temporally independent strategies, where data for each point in time is analyzed independently, allowing for the detection of bi-directional changes (\"post-classification comparison\") [78]. Thus, back-casting approaches only evaluate urban growth, not shrinkage, and may yield biased results in areas affected by urban shrinkage. However, urban shrinkage occurs relatively rarely, and typically relates to population decline and housing vacancies [79], rather than manifesting in land conversion from urban to non-urban land. Hence, we expect this bias to be marginal for the presented work. We applied our method to historical maps from six cities in four continents dated between 1890 and 1960, using historical built-up property data from the Historical Settlement Data Compilation for the US (HISDAC-US) [8, 80] as well as urban area estimates from the History Database of the Global Environment (HYDE) [6] to evaluate and cross-compare our results.