PickShift: A user-friendly Python tool to assess the surficial uncertainties associated with polygons extracted from historical planimetric data
Timothée Jautzy,
Pierrick Freys,
Valentin Chardon,
Romain Wenger,
Gilles Rixhon,
Laurent Schmitt,
Pierre-Alexis Herrault
Affiliations
Timothée Jautzy
Laboratoire Image, Ville, Environnement (LIVE UMR 7362), Université de Strasbourg, CNRS, 3 rue de l'Argonne, Strasbourg 67083, France; Corresponding author.
Pierrick Freys
Laboratoire Image, Ville, Environnement (LIVE UMR 7362), Université de Strasbourg, CNRS, 3 rue de l'Argonne, Strasbourg 67083, France; Eurométropole de Strasbourg, Service Géomatique et Connaissance du territoire, 1 parc de l'Etoile, Strasbourg 67076, France
Valentin Chardon
Laboratoire Image, Ville, Environnement (LIVE UMR 7362), Université de Strasbourg, CNRS, 3 rue de l'Argonne, Strasbourg 67083, France
Romain Wenger
Laboratoire Image, Ville, Environnement (LIVE UMR 7362), Université de Strasbourg, CNRS, 3 rue de l'Argonne, Strasbourg 67083, France
Gilles Rixhon
Laboratoire Image, Ville, Environnement (LIVE UMR 7362), Université de Strasbourg, CNRS, 3 rue de l'Argonne, Strasbourg 67083, France
Laurent Schmitt
Laboratoire Image, Ville, Environnement (LIVE UMR 7362), Université de Strasbourg, CNRS, 3 rue de l'Argonne, Strasbourg 67083, France
Pierre-Alexis Herrault
Laboratoire Image, Ville, Environnement (LIVE UMR 7362), Université de Strasbourg, CNRS, 3 rue de l'Argonne, Strasbourg 67083, France
With the increasing use of GIS software's, historical planimetric data such as orthophotos and old maps represent key data sources to analyze spatio-temporal landscape evolution. However, geometric error inherent to these data are too often overlooked, possibly leading to confusing misinterpretation of measured surficial changes. The user-friendly Python tool 'PickShift', based on a Monte-Carlo approach, addresses this critical issue by quantifying the surficial uncertainty associated with any features digitized from historical planimetric data. This software provides a valuable framework for a more accurate assessment of landscape dynamics and associated uncertainties.
