## 4 Conclusions  
The work presented herein is a first attempt to create a framework that combines signals obtained from scanned, georeferenced historical maps with remote sensing data products in an integrated analytical environment. We applied this framework to the extraction and assessment of urban areas over long time periods and demonstrated how  
Figure 11: Hind-casted GHSL urban growth trajectories (extracted historical urban areas are GHSL-constraint, k = 4, averaged across any post-processing scenario) and their deviation, overlaid with HYDE 3.2 urban area trajectories. These trajectories are composed of four remote-sensing-based observations (from the GHSL) and an earlier observation obtained from the historical maps, allowing for hind-casting the GHSL-based trajectories to earlier points in time. Figure 10: Cross-comparison to HYDE urban areas. Box-and-whisker plots illustrating the distribution of built-up areas extracted from the historical maps for all clustering scenarios, per constraint and post-processing scenario, overlaid with the urban area extracted from the HYDE 3.2 database for the respective study areas (ATL = Atlanta, BOS = Boston, LON = London, BIR = Birmingham, SP = Sao Paulo, and LAH = Lahore). such an approach can create spatial-historical data that describe trends of long-term urban-spatial development prior to the era of remote sensing and enhance our understanding of the underlying urbanization processes. The spatial aggregation performed on the historical maps facilitates the seamless integration with other gridded surfaces in general, and effectively reduces the spatial data volume to be processed. Thus, given the availability of georeferenced historical maps in numerous countries, this framework could be applied to systematically hind-cast the Global Human Settlement Layer, or other settlement data products, at a country scale. The presented framework represents an effective way to harvest historical maps, and thus preserve valuable knowledge that can only be found in such archival documents. Revisiting the research questions posed in Section 1, we can conclude the following:  
(i) We demonstrated that signals obtained from scanned and georeferenced historical maps or whole map collections have the potential to enrich remote-sensing-based analyses (e.g., [93]) and to extend the observational window of remote-sensing-based change analysis further back in time. We applied our method to the study of long-term urban change, but similar frameworks could be employed for other applications, such as the analysis of long-term forest dynamics or other types of land cover. (ii) Unlike remote sensing data, which is typically collected within short repeat cycles (e.g., 16 days for the Landsat 8 satellite), signals obtained from historical maps over large spatial extents may suffer from heterogeneous temporal reference. Moreover, the discussed, inherent and largely unknown positional uncertainties in signals from historical maps need to be taken into account. Thus, the choice of an appropriate analytical unit is crucial, allowing one to capture the desired spatial detail while aggregating the map signals in a way that reduces the effects of positional inaccuracies and accounts for the vague definition of the features of interest (e.g., urban areas). (iii) Despite these challenges, the proposed analytical framework yields plausible results, that is, the extracted urban extents are largely in agreement with other spatial-historical information, and they are at a higher spatial resolution than existing spatially explicit historical data, such as HYDE or HISDAC-US. From the ROC analysis conducted for the US study areas (Figure 6), we observe slightly higher AUC values for the Boston maps than for the Atlanta maps. This is surprising, as the Boston maps are much older (1900) than the Atlanta maps (1960). The reason is likely the number of colors used: the Boston 1900 maps are 3-color prints, whereas the Atlanta 1960 maps are 5-color prints.