This article presents a novel deep learning method for semi-automated detection of historic mining pits using aerial LiDAR data. The recent emergence of national scale remotely sensed datasets has created the potential to greatly increase the rate of analysis and recording of cultural heritage sites. However, the time and resources required to process these datasets in traditional desktop surveys presents a near insurmountable challenge. The use of artificial intelligence to carry out preliminary processing of vast areas could enable experts to prioritize their prospection focus; however, success so far has been hindered by the lack of large training datasets in this field. This study develops an innovative transfer learning approach, utilizing a deep convolutional neural network initially trained on Lunar LiDAR datasets and reapplied here in an archaeological context. Recall rates of 80% and 83% were obtained on the 0.5 m and 0.25 m resolution datasets respectively, with false positive rates maintained below 20%. These results are state of the art and demonstrate that this model is an efficient, effective tool for semi-automated object detection for this type of archaeological objects. Further tests indicated strong potential for detection of other types of archaeological objects when trained accordingly.

In recent years data acquisition from remote sensing has become readily available to the quarry sector. This study demonstrates how such data may be used to evaluate and back analyse rockfall potential of a legacy slope in a blocky rock mass. Use of data obtained from several aerial LiDAR (Light Detection and Ranging) and photogrammetric campaigns taken over a number of years (2011 to date) provides evidence for potential rockfall evolution from a slope within an active quarry operation in Cornwall, UK. Further investigation, through analysis of point cloud data obtained from terrestrial laser scanning, was undertaken to characterise the orientation of discontinuities present within the rock slope. Aerial and terrestrial LiDAR data were subsequently used for kinematic analysis, production of surface topography models and rockfall trajectory analyses using both 2D and 3D numerical simulations. The results of an Unmanned Aerial Vehicle (UAV)-based 3D photogrammetric analysis enabled the reconstruction of high resolution topography, allowing one to not only determine geometrical properties of the slope surface and geo-mechanical characterisation but provide data for validation of numerical simulations. The analysis undertaken shows the effectiveness of the existing rockfall barrier, while demonstrating how photogrammetric data can be used to inform back analyses of the underlying failure mechanism and investigate potential runout.

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Understanding changes in slope geometry and knowledge of underlying engineering properties of the rock mass are essential for the safe design of man-made slopes and to reduce the significant risks associated with slope failure. Recent advances in the geomatics industry have provided the capability to obtain accurate, fully geo-referenced three-dimensional datasets that can be subsequently interrogated to provide engineering-based solutions for monitoring of deformation processes, rock mass characterization and additional insight into any underlying failure mechanisms. Importantly, data can also be used to spatially locate and map geological features and provide displacement or deformation rate information relating to movement of critical sections or regions of a slope.\ud \ud This paper explores the benefits that can be obtained by incorporating different remote sensing techniques and conventional measurement devices to provide a comprehensive database required for development of an effective slope monitoring and risk management program. The integration of different techniques, such as high accuracy discrete point measurement at critical locations, which can be used to complement larger scale less dense three-dimensional survey will be explored. Case studies using a combination of aerial and terrestrial laser scanning, unmanned aerial vehicle and hand-held scanning devices will demonstrate their ability to provide spatial data for informing decision making processes and ensuring compliance with Regulations