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Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy. 

While UAV-based imaging methods such as drone lidar scanning (DLS) and Structure from Motion (SfM) are now widely used in geographic research, accurate water surface elevation (WSE) measurement remains a difficult problem, as water absorbs wavelengths commonly used for lidar and SfM feature matching fails on these dynamic surfaces. We present a methodology for measuring WSE in a particularly challenging environment, the Yucatán Peninsula, where cenotes – exposed, water-filled sinkholes – provide an observation point into the critically important regional groundwater supply. In the northeastern Yucatán, elevations are very close to sea level, the area is of low relief, and the near-vertical edges of the walls of the cenotes complicate the use of the so-called “water edge” technique for WSE measurement. We demonstrate how post-processing kinematic (PPK) correction of even a single Real Time Kinematic (RTK) Global Positioning System (GPS) unit can be used to finely register the SfM-derived point cloud, and present evidence from both simulations and an empirical study that quantify the effect of “dip” in SfM-based environmental reconstructions. Finally, we present a statistical analysis of the problem of “thick” or “fuzzy” point clouds derived from SfM, with particular emphasis on their interactions with WSE measurement.