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Considering how difficult it is for a pin in the ocean to be found, painstaking searches among historical documents and eyewitness accounts often end up with more unknowns and questions. We developed a three-tier geo-spatial tech-based approach to discover and unfold the lost WWII heritage features in the countryside of Hong Kong that can be applied in other contexts. It started with an analysis of historical texts, old maps, aerial photos, and military plans in the historical geographic information system (HGIS) Project ‘The Battle of Hong Kong 1941: a Spatial History Project’ by Hong Kong Baptist University to define regions/points of interest. Then, 3D point clouds extracted from the government’s airborne LiDAR were migrated to form a digital terrain model (DTM) for geo-registration in GIS. All point clouds were geo-referenced in HK1980 Grid via accurate positioning using the global navigation satellite system—real-time kinematics (GNSS-RTK). A red relief image map (RRIM) was then used to image the tunnels, trenches, and pillboxes in great detail by calculating the topographical openness. The last tier of the tech work was field work involving ground validation of the findings from the previous two tiers and on-site imaging using terrestrial LiDAR. The ground 3D LiDAR model of the heritage feature was then built and integrated into the DTM. The three-tier tech-based approach developed in this paper is standardised and adopted to streamline the workflow of historical and archaeological studies not only in Hong Kong but also elsewhere.

The three-dimensional (3D) ground-penetrating radar (GPR) has been widely applied in subsurface surveys and imaging, and the quality of the resulting C-scan images is determined by the spatial resolution and visualisation contrast. Previous studies have standardised the suitable spatial resolution of GPR C-scans; however, their measurement normalisation remains arbitrary. Human bias is inevitable in C-scan interpretation because different visualisation algorithms lead to different interpretation results. Therefore, an objective scheme for mapping GPR signals after standard processing to the visualisation contrast should be established. Focusing on two typical scenarios, a reinforced concrete structure and an urban underground, this study illustrated that the essential parameters were greyscale thresholding and transformation mapping. By quantifying the normalisation performance with the integration of image segmentation and structural similarity index measure, a greyscale threshold was developed in which the normalised standard deviation of the unit intensity of any surveyed object was two. A transformation function named “bipolar” was also shown to balance the maintenance of real reflections at the target objects. By providing academia/industry with an object-based approach, this study contributes to solving the final unresolved issue of 3D GPR imaging (i.e., image contrast) to better eliminate the interfering noise and better mitigate human bias for any one-off/touch-based imaging and temporal change detection.

Three-dimensional GPR imaging requires evenly and densely distributed measurements, ideally collected without the need for ground surface markings, which is difficult to achieve in large-scale surveys. In this study, a guidance system was developed to guide the GPR operator to walk along a predesigned traverse, analogous to the flight path design of an airborne drone. The guidance system integrates an auto-track total station unit (ATTS), and by estimating the real-time offset angle and distance, guidance corrections can be provided to the operator in real time. There are two advantages: (1) reduced survey time as grid marking on the ground is no longer needed and (2) accurate positioning of each traverse. Lab and field experiments were conducted in order to validate the guidance system. The results show that with the guidance system, the survey paths were better defined and followed in terms of feature connectivity and resolution of images, and the C-scans generated were closer to the real subsurface world.