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.
Subject
General Earth and Planetary Sciences
Reference23 articles.
1. Revealing historical landscapes by using airborne laser scanning. A 3-D modell of Ridge and furrow in forests near Rastatt (Germany);Sittler,2004
2. LiDAR surveys of ancient landscapes in SW Germany: Assessment of archaeological features under forests and attempts for automatic pattern recognition;Heinzel,2010
3. Using pattern recognition to search LIDAR data for archeological sites;De Boer,2007
4. Automatic Detection of Pit Structures in Airborne Laser Scanning Data
Cited by
16 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献