2020
DOI: 10.5194/isprs-archives-xliii-b2-2020-1561-2020
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Using Deep Learning and Hough Transformations to Infer Mineralised Veins From Lidar Data Over Historic Mining Areas

Abstract: Abstract. This paper presents a novel technique to improve geological understanding in regions of historic mining activity. This is achieved through inferring the orientations of geological structures from the imprints left on the landscape by past mining activities. Open source high resolution LiDAR datasets are used to fine-tune a deep convolutional neural network designed initially for Lunar LiDAR crater identification. By using a transfer learning approach between these two very similar domains, high accur… Show more

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“…Therefore, only archaeological features that have an invariable morphology and are abundant can be targeted. Until now, the focus has been limited to a handful: mound structures (burial mounds [59,[62][63][64][65][66][67][68], charcoal kilns [69,70], and shell-rings [71]), pit structures (hunting system [72]; ore extraction pits [72,73], and bomb craters [74]), and linear sunken structures (paths [75,76], ditches [77], and mining shafts [73]). There is a recent trend of targeting complex features [78] and multiple feature types (multi-class archaeological object detection [77,[79][80][81][82][83][84]), but complex archaeological landscapes imbedded in a complex terrain with ample anthropogenic influence remains challenging [80,81].…”
Section: Archaeological Interpretation (31-35)mentioning
confidence: 99%
“…Therefore, only archaeological features that have an invariable morphology and are abundant can be targeted. Until now, the focus has been limited to a handful: mound structures (burial mounds [59,[62][63][64][65][66][67][68], charcoal kilns [69,70], and shell-rings [71]), pit structures (hunting system [72]; ore extraction pits [72,73], and bomb craters [74]), and linear sunken structures (paths [75,76], ditches [77], and mining shafts [73]). There is a recent trend of targeting complex features [78] and multiple feature types (multi-class archaeological object detection [77,[79][80][81][82][83][84]), but complex archaeological landscapes imbedded in a complex terrain with ample anthropogenic influence remains challenging [80,81].…”
Section: Archaeological Interpretation (31-35)mentioning
confidence: 99%