Smartphone-based LiDAR for generating Digital Outcrop Models (DOMs) with field validation

Abstract

Rock outcrops are crucial for geological studies, providing insights through direct observation and sampling. However, some outcrops are temporary, especially in tropical regions and dynamic environments like mining sites. Digital Outcrop Models (DOMs) address this challenge by offering 3D representations for remote analysis, typically generated via LiDAR and photogrammetry. However, these methods often require costly equipment and extensive processing. Recent advances in mobile technology, particularly the integration of LiDAR sensors in smartphones, present a cost-effective alternative for geoscientific data collection. Despite its potential, concerns remain about the accuracy of smartphone-derived LiDAR compared to traditional methods, particularly regarding scanning configurations and range settings. This study compared different acquisition parameters across scales and assessed iPhone-based LiDAR accuracy in a mining environment. Additionally, an optimized workflow for DOM acquisition was proposed. Among the free applications tested, Scaniverse yielded the best results in terms of measurement precision and user experience. The study found that accuracy depends significantly on acquisition range. The 0.3-meter range provided the highest accuracy but required longer acquisition times. The 2.5-meter range offered the best balance between accuracy and efficiency, making it ideal for fieldwork. The 5-meter range, however, did not provide substantial advantages. For detailed analyses, traditional post-processing software like CloudCompare is recommended. These findings highlight the potential of smartphone LiDAR for geoscience, emphasizing the need to optimize acquisition parameters and use efficient post-processing tools to enhance precision and field efficiency.