A fully automated model for land use classification from historical maps using machine learning

Abstract

Digital land use data before the age of satellites is scarce. Here, we build a machine learning model, using Extreme Gradient Boosting, that can automatically detect land use classes from an orthophoto map of Sweden (economic maps, 1:10 000 and 1:20 000) constructed from 1942 to 1988. Overall, the machine learning model demonstrated robust performance, with Cohen's Kappa and Matthews Correlation Coefficient of 0.86. The F1 values of the individual classes were 0.98, 0.95, 0.84, and 0.87 for graphics, arable land, forest, and open land, respectively. While the model can be used to detect land use changes in arable land, higher uncertainties associated with forest and open land necessitate further investigation at regional scales or exploration of improved mapping techniques. The code is publicly available to enable easy adaptation for classifying other historical maps.

Keywords

Automation; Economic map; Extreme gradient boosting; Historical maps; Land use; Machine learning

Published in

Remote Sensing Applications: Society and Environment
2024, volume: 36, article number: 101349
Publisher: ELSEVIER

SLU Authors

• Ågren, Anneli

  • Department of Forest Ecology and Management, Swedish University of Agricultural Sciences
    

• Lin, Yiqi

  • Department of Forest Ecology and Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Remote Sensing

Publication identifier

• DOI: https://doi.org/10.1016/j.rsase.2024.101349

Permanent link to this page (URI)

https://res.slu.se/id/publ/140153