Page | 1 Quick Mapping of historical urbanization in France, case study of Scan Histo 1950 using spatial pixel based approach Walid Rabehi*1, Rémi Lemoy†1, Marion Le Texier‡2 1UMR IDEES 6266/CNRS, University of Rouen 2University of Montpellier 3 GISRUK 2023 Summary Automatic detection of human settlement and cities forms on historical maps remains a challenging topic regarding the complexity of the maps semiology, but also because of the general conditions of maps (ink degradation, color change…). The aim of this contribution is to establish an urban layer of France based on historical maps of 1950 using pixel-based approach and multitemporal landcover map. This work will use the produced layers to test whether large cities are more or less parsimonious in terms of land use than smaller cities, at different periods. KEYWORDS: Pixel based analysis, Remote sensing, Historical maps, Urban areas, France 1. Introduction Spatial analysis of city size and urban forms is a very popular topic, widely addressed due to the availability of open-access databases such as GHSL (Global Human Settlement Layer), Urban atlas, Copernicus. etc., produced from satellite imagery, available for the last five decades. In this context, the challenge is to produce urban layers older/prior to 1975, using historical maps (starting with Scan histo 1950 data (IGN, 2014)), in order to obtain a deeper spatiotemporal view for the 19th and even 18th centuries, and especially on national scale in France. The complexity of automatic detection on these maps, is related to several factors, such as color degradation (effect of maps’ bad conservation or just old age causing ink degradation), or certain technical hazards such as discrepancies between maps (”the band completeness linked to projections issues on a national scale” (Lenc et al., 2022)). In France, very few studies have focused on detection of urban areas using historical support (Drapeau et al., 2017), in particular because of the difficulty of automatic classifications on this type of data, where traditional remote sensing algorithms are quite inefficient (Ignjatić et al., 2018). (Combes, Gobillon and Zylberberg, 2022) use machine learning techniques combined with a “Random Forest” algorithm/classifier to classify urban areas. They also point out some limits of this method: a tedious time of manual sampling (model learning and validating), and regarding the results a confusion with the text (nomenclature inside the map and linear networks lie roads or railways). The aim of this work is to provide a cartography of urban forms at medium resolution (20m) covering the French territory based on a “Scan histo 1950” mosaic, using a combination of classic remote-sensing classifiers and morphological filters, as well as an automatic cleaning method to detect confusion based on recent land cover map (Corine Land Cover 2012). This is the first attempt, to our knowledge, to detect urban areas automatically on this national 1950 map. This quick mapping approach is indeed a correct quality/time compromise, yielding cartographic support comparable in quality with GHSL urban layers (Florczyk et al., 2019). Indeed, we obtain a confusion rate of 75%, which is very acceptable on a national scale, sparing time-consuming approaches like manual vectorization or machine learning manual sampling processes. The produced urban layers will be analysed as part of the project using a combination of radial analysis * walid.rabehi@univ-rouen.fr † remi.lemoy@univ-rouen.fr ‡ marion.le-texier@univ-montp3.fr