Abstract. Automated recognition of terrain structures is a major research problem in many application areas. These structures can be investigated in raster products such as Digital Elevation Models (DEMs) generated from Airborne Laser Scanning (ALS) data. Following the success of deep learning and computer vision techniques on color images, researchers have focused on the application of such techniques in their respective fields. One example is detection of structures in DEM data. DEM data can be used to train deep learning models, but recently, Du et al. (2019) proposed a multi-modal deep learning approach (hereafter referred to as MM) proving that combination of geomorphological information help improve the performance of deep learning models. They reported that combining DEM, slope, and RGB-shaded relief gives the best result among other combinations consisting of curvature, flow accumulation, topographic wetness index, and grey-shaded relief. In this work, we approve and build on top of this approach. First, we use MM and show that combinations of other information such as sky view factors, (simple) local relief models, openness, and local dominance improve model performance even further. Secondly, based on the recently proposed HR-Net (Sun et al., 2019), we build a tinier, Multi-Modal High Resolution network called MM-HR, that outperforms MM. MM-HR learns with fewer parameters (4 millions), and gives an accuracy of 84:2 percent on ZISM50m data compared to 79:2 percent accuracy by MM which learns with more parameters (11 millions). On the dataset of archaeological mining structures from Harz, the top accuracy by MM-HR is 91:7 percent compared to 90:2 by MM.
<p><strong>Abstract.</strong> We explore the use of semantic segmentation in Digital Terrain Models (DTMS) for detecting manmade landscape structures in archaeological sites. DTM data are stored and processed as large matrices of depth 1 as opposed to depth 3 in RGB images. The matrices usually contain continuous real-valued information upper bound of which is not fixed, such as distance or height from a reference surface. This is different from RGB images that contain integer values in a fixed range of 0 to 255. Additionally, RGB images are usually stored in smaller multidimensional matrices, and are more suitable as inputs for a neural network while the large DTMs are necessary to be split into smaller sub-matrices to be used by neural networks. Thus, while the spatial information of pixels in RGB images are important only locally within a single image, for DTM data, they are important locally, within a single sub-matrix processed for neural network, and also globally, in relation to the neighboring sub-matrices. To cope with the two differences, we apply min-max normalization to each input matrix fed to the neural network, and use a slightly modified version of DeepLabv3+ model for semantic segmentation. We show that with the architecture change, and the preprocessing, better results are achieved.</p>
Abstract. This paper explores the role deep convolutional neural networks play in automated extraction of linear structures using semantic segmentation techniques in Digital Terrain Models (DTMs). DTM is a regularly gridded raster created from laser scanning point clouds and represents elevations of the bare earth surface with respect to a reference. Recent advances in Deep Learning (DL) have made it possible to explore the use of semantic segmentation for detection of terrain structures in DTMs. This research examines two novel and practical deep convolutional neural network architectures i.e. an encoder-decoder network named as SegNet and the recent state-of-the-art high-resolution network (HRNet). This paper initially focuses on the pixel-wise binary classification in order to validate the applicability of the proposed approaches. The networks are trained to distinguish between points belonging to linear structures and those belonging to background. In the second step, multi-class segmentation is carried out on the same DTM dataset. The model is trained to not only detect a linear feature, but also to categorize it as one of the classes: hollow ways, roads, forest paths, historical paths, and streams. Results of the experiment in addition to the quantitative and qualitative analysis show the applicability of deep neural networks for detection of terrain structures in DTMs. From the deep learning models utilized, HRNet gives better results.
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