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.
The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication A combined field/remote sensing approach for characterizing landslide risk in coastal areas. Mirko Francioni (a*)(b), John Coggan (b), Matthew Eyre (b), Doug Stead (c),
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.
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