Resumen
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.