Geomorphology of the Durmitor Mountains and surrounding plateau Jezerska Površ (Montenegro)

The geomorphological map of the northeastern Durmitor Mountains and the plateau Jezerska Povrs. (1: 10,000, 47 km(2), Montenegro, Dinaric Alps) was prepared from an intensive fieldwork campaign and remote sensing analysis, and was compiled within a GIS. The basic components of the legend are (i) processes/genesis, (ii) materials, (iii) morphometry/morphography, (iv) hydrography, (v) vegetation and (vi) anthropogenic features. The geomorphological setting of the area consists of Mesozoic limestones which are physically deformed by Quaternary glacial and periglacial activity and chemically affected during interglacials. Glacial deposits on the plateau of three middle-to-late Pleistocene glacial phases are intersected by a well-developed network of palaeo meltwater channels. In the mountains, Holocene glacier retreat left behind a series of well-preserved recessional moraines. The map serves as a valuable tool for Quaternary research in the Durmitor Mountains, and also in other mountains of the Western Balkans

Surface networks

© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou

A high-precision liDAR-based method for surveying and classifying coastal notches

Formation of notches is an important process in the erosion of seaside cliffs. Monitoring of coastal notch erosion rate and processes has become a prime research focus for many coastal geomorphologists. Observation of notch erosion rate considers a number of characteristics, including cliff collapse risk, distinction of historical sea levels, and recognition of ongoing erosional mechanisms. This study presents new approaches for surveying and classifying marine notches based on a high-precision light detection and ranging (LiDAR)-based experiment performed on a small region of a coastal cliff in southern Portugal. A terrestrial LiDAR scanner was used to measure geometrical parameters and surface roughness of selected notches, enabling their classification according to shape and origin. The implemented methodology proved to be a highly effective tool for providing an unbiased analysis of marine morphodynamic processes acting on the seaside cliffs. In the analyzed population of voids carved into Miocene calcarenites in a coastal cliff section, two types of notch morphology were distinguished, namely U-shaped and V-shaped. The method presented here provides valuable data for landscape evaluation, sea-level changes, and any other types of analyses that rely on the accurate interpretation of cliff morphological features.National Science Centre [UMO-2015/17/D/ST10/02191

CASENet: Deep Category-Aware Semantic Edge Detection

Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks such as semantic segmentation, object recognition, stereo, and object proposal generation. Recently, the problem of edge detection has been revisited and significant progress has been made with deep learning. While classical edge detection is a challenging binary problem in itself, the category-aware semantic edge detection by nature is an even more challenging multi-label problem. We model the problem such that each edge pixel can be associated with more than one class as they appear in contours or junctions belonging to two or more semantic classes. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features. We then propose a multi-label loss function to supervise the fused activations. We show that our proposed architecture benefits this problem with better performance, and we outperform the current state-of-the-art semantic edge detection methods by a large margin on standard data sets such as SBD and Cityscapes.Comment: Accepted to CVPR 201

The ancient Digital Terrain Model and the infrastructure of the Etruscan city of Kainua

This paper aims to explain the creation of the Digital Terrain Model (DTM) of Kainua, an Etruscan city founded, following a rigorous urban plan, at the beginning of the 5th century BCE. This DTM was used as the basis for the virtual reconstruction of Kainua landscape from an urban to an architectural scale in a three-dimensional digital model, visualized in an interactive and immersive approach. The DTM was developed using different sources of elevation data, in order to take into account the geo-morphological transformations occurred in that area from the Etruscan period to the present day. The causes of these changes were natural (due to erosion phenomena) and anthropic (due to excavations for construction of transport infrastructure as well as those which occurred partly due to improvements made by landowners and partly to archaeologists who first began a systematic campaign of site studies). On positioning on the DTM, an analysis of the metrology and of the infrastructure of the ancient city (streets and sewers) made it possible to create a renewed vision and to propose a hypothesis for reconstructing the incomplete, or as yet unstudied, parts of the city, which only further excavations will confirm

Depth mapping of integral images through viewpoint image extraction with a hybrid disparity analysis algorithm

Integral imaging is a technique capable of displaying 3–D images with continuous parallax in full natural color. It is one of the most promising methods for producing smooth 3–D images. Extracting depth information from integral image has various applications ranging from remote inspection, robotic vision, medical imaging, virtual reality, to content-based image coding and manipulation for integral imaging based 3–D TV. This paper presents a method of generating a depth map from unidirectional integral images through viewpoint image extraction and using a hybrid disparity analysis algorithm combining multi-baseline, neighbourhood constraint and relaxation strategies. It is shown that a depth map having few areas of uncertainty can be obtained from both computer and photographically generated integral images using this approach. The acceptable depth maps can be achieved from photographic captured integral images containing complicated object scene

Generation of High Spatial Resolution Terrestrial Surface from Low Spatial Resolution Elevation Contour Maps via Hierarchical Computation of Median Elevation Regions

We proposed a simple yet effective morphological approach to convert a sparse Digital Elevation Model (DEM) to a dense Digital Elevation Model. The conversion is similar to that of the generation of high-resolution DEM from its low-resolution DEM. The approach involves the generation of median contours to achieve the purpose. It is a sequential step of the I) decomposition of the existing sparse Contour map into the maximum possible Threshold Elevation Region (TERs). II) Computing all possible non-negative and non-weighted Median Elevation Region (MER) hierarchically between the successive TER decomposed from a sparse contour map. III) Computing the gradient of all TER, and MER computed from previous steps would yield the predicted intermediate elevation contour at a higher spatial resolution. We presented this approach initially with some self-made synthetic data to show how the contour prediction works and then experimented with the available contour map of Washington, NH to justify its usefulness. This approach considers the geometric information of existing contours and interpolates the elevation contour at a new spatial region of a topographic surface until no elevation contours are necessary to generate. This novel approach is also very low-cost and robust as it uses elevation contours.Comment: 11 pages, 6 figures,1 table, 1 algorith