PickShift: A user-friendly Python tool to assess the surficial uncertainties associated with polygons extracted from historical planimetric data

Abstract

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.

Keywords

• Historical maps
• Orthophotos
• Surficial uncertainty
• Geometric error
• Monte-Carlo