The Efficacy and Analytical Importance of Manual Feature Extraction Using Lidar Datasets

The availability of lidar datasets has led to several advances in archaeology, notably in the process of site prospection. Some remote sensing practitioners have aimed to create automated feature extraction (AFE) techniques that increase the efficiency and efficacy of identification and analysis. While these advances have been successful, many archaeological professionals who might have an interest in lidar-derived products do not have the technical experience to modify or create AFE techniques for particular regions or environments. Additionally, some features are not appropriate for AFE. Instead, the most widely used technique is still likely to be visually based manual feature identification. Using authors of different experience levels, we seek to evaluate the use of manual techniques for feature identification and subsequent analysis by implementing a publicly available lidar-derived digital elevation model (DEM). We demonstrate that manual feature extraction (MFE) can be accurate when more than one researcher is involved in a sort of checks and balances process. We also show that the use of confidence ratings can be an important part of this process if those ratings have some systematic and clearly defined underpinning. Finally, we argue, using a case study from American Samoa, that manually identified features can be analytically important as part of larger landscape studies.