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Open AccessArticle

Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning

Camborne School of Mines, University of Exeter, Tremough Campus, Penryn TR10 9EZ, UK
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Remote Sens. 2019, 11(17), 1994; https://doi.org/10.3390/rs11171994
Received: 31 July 2019 / Revised: 15 August 2019 / Accepted: 21 August 2019 / Published: 23 August 2019
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. View Full-Text
Keywords: deep learning; transfer learning; historic mining; heritage management; LiDAR deep learning; transfer learning; historic mining; heritage management; LiDAR
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MDPI and ACS Style

Gallwey, J.; Eyre, M.; Tonkins, M.; Coggan, J. Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning. Remote Sens. 2019, 11, 1994.

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