Abstract
While Light Detection and Ranging (LiDAR) revolutionized archaeological prospection and different visualizations were developed, an automated detection of cultural heritage still poses a significant challenge. Therefore, geographers and archaeologists from Westphalia, Germany are developing automated workflows for classifying field monuments from special terrain models. For this project, a combination of GIS, Python, and Object-Based Image Analysis (OBIA) is used. It focuses on three common types of monuments: Ridge and Furrow areas, Burial Mounds, and Motte-and-Bailey castles. The latter two are not classified binary, but in multiple classes, depending on their degree of erosion. This simplifies interpretation by highlighting the most interesting structures without losing the others. The results confirm that OBIA is suitable for detecting field monuments with hit rates of ~90%. A drawback is its dependency on the use of special terrain models like the Difference Map. Further limitations arise in complex terrain situations.
Highlights
The use of airborne laser scanning in archaeology started almost 20 years ago
One of the first archaeological applications of Light Detection and Ranging (LiDAR) in Germany was performed by Sittler in Rastatt [1]
It was demonstrated that all three presented monuments quality Westphalian LiDAR dataset
Summary
The use of airborne laser scanning in archaeology started almost 20 years ago. LiDAR revolutionized archaeological prospection and new visualizations were invented to ease interpretation of field monuments. The need for automated workflows and classifications became evident to handle the growing number of datasets. One of the first archaeological applications of LiDAR in Germany was performed by Sittler in Rastatt [1]. He successfully searched for Ridge and Furrow structures, digitized some of them manually, and carefully predicted that algorithms would be able to detect structures automatically. Heinzel and Sittler [2] presented an automated approach using Pattern Recognition for the detection and delineation of single Ridge and Furrow structures. The evaluation with reference data produced accuracies up to 84%, depending on the complexity of the terrain
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