Saliency and semantic processing: Extracting forest cover from historical topographic maps

Abstract

A multi-step recognition process is developed for extracting compound forest cover information from manually produced scanned historical topographic maps of the 19th century. This information is a unique data source for GIS-based land cover change modeling. Based on salient features in the image the steps to be carried out are character recognition, line detection and structural analysis of forest symbols. Semantic expansion implying the meanings of objects is applied for final forest cover extraction. The procedure resulted in high accuracies of 94% indicating a potential for automatic and robust extraction of forest cover from larger areas