USING DEEP LEARNING AND HOUGH TRANSFORMATIONS TO INFER MINERALISED VEINS FROM LIDAR DATA OVER HISTORIC MINING AREAS
- Camborne School of Mines, University of Exeter, Penryn, Cornwall, UK
Keywords: Transfer Learning, Deep Learning, Mining, Geology, LiDAR, Lineament Detection
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 accuracy predictions of pit locations can be generated in the form of a raster mask of pit location probabilities. Taking the raster of the predicted pit location centres as an input, a Hough transformation is used to fit lines through the centres of the detected pits. The results demonstrate that these lines follow the patterns of known mineralised veins in the area, alongside highlighting veins which are below the scale of the published geological maps.