Comparative analysis of UAV-based LiDAR and photogrammetric systems for the detection of terrain anomalies in a historical conflict landscape

Abstract

The documentation of historical artefacts and cultural heritage using high-resolution data obtained from unmanned aerial vehicles (UAVs) is of paramount importance in the preservation of historical knowledge. This study compares three UAV-based systems for the detection of historically relevant terrain anomalies in a conflict landscape. Two laser scanners, a high-end (RIEGL miniVUX-1UAV) and a lower priced model (DJI Zenmuse L1), along with a cost-effective optical camera system (photogrammetry using Structure from Motion, SfM) were employed in two study sites with different densities of vegetation. In the study area with deciduous trees and little low vegetation, the DJI Zenmuse L1 system performs comparably to the RIEGL miniVUX-1UAV, with higher completeness but lower correctness. The SfM method demonstrated inferior performance with respect to correctness and the F1-score, yet achieved comparable or higher completeness values compared to the laser scanners (maximum 1.0, median 0.84). In the study area characterized by dense near-ground vegetation, the detection results are less optimal. However, the RIEGL miniVUX-1UAV system still demonstrates superior results in anomaly detection (F1-score maximum 0.61, median 0.53) compared to the other systems. The DJI Zenmuse L1 data showed lower performance (F1-score maximum 0.56, median 0.46). Both laser scanners exhibited enhanced results in comparison to the SfM approach, with a maximum F1-score of 0.12. Hence, the SfM method is viable under specific conditions, such as defoliated trees without dense low vegetation. Therefore, lower-cost systems can offer cost-effective alternatives to the high-end LiDAR system in suitable environments. However, limitations persist in densely vegetated areas.