Terrain Structure Research Articles

RGB-ICP Method to Calculate Ground Three-Dimensional Deformation Based on Point Cloud from Airborne LiDAR

With the rapid development of LiDAR technology in recent years, high-resolution LiDAR data possess a great capability to describe fine surface morphology in detail; thus, differencing multi-temporal datasets becomes a powerful tool to explain the surface deformation process. Compared with other differencing methods, ICP algorithms can directly estimate 3D displacements and rotations; thus, surface deformation parameters can be obtained by aligning window point clouds. However, the traditional ICP algorithm usually requires a good initial pose of the point cloud and relies on calculating the spatial distance to match the corresponding points, which can easily lead the algorithm to the local optimum. To address the above problems, we introduced the color information of the point cloud and proposed an improved ICP method that fuses RGB (RGB-ICP) to reduce the probability of matching errors by filtering color-associated point pairs, thus improving the alignment accuracy. Through simulated experiments, the ability of the two algorithms to estimate 3D deformation was compared, and the RGB-ICP algorithm could significantly reduce the deformation deviation (30–95%) in the three-dimensional direction. In addition, the RGB-ICP algorithm was applicable to different terrain structures, especially for smooth terrain, where the improvement was the most effective in the horizontal direction. Finally, it is worth believing that the RGB-ICP algorithm can play a unique role in surface change detection and provide a reliable basis for explaining the surface motion process.

Terrain Features and Architecture of Wolverine (Gulo gulo) Resting Burrows and Reproductive Dens on Arctic Tundra

Burrowing species rely on subterranean and subnivean sites to fulfill important life-history and behavioral processes, including predator avoidance, thermoregulation, resting, and reproduction. For these species, burrow architecture can affect the quality and success of such processes, since characteristics like tunnel width and chamber depth influence access by predators, thermal insulation, and energy spent digging. Wolverines (Gulo gulo) living in Arctic tundra environments dig burrows in snow during winter for resting sites and reproductive dens, but there are few published descriptions of such burrows. We visited 114 resting burrows and describe associated architectural characteristics and non-snow structure. Additionally, we describe characteristics of 15 reproductive den sites that we visited during winter and summer. Although many resting burrows were solely excavated in snow, most incorporated terrain structures including cliffs, talus, river shelf ice, thermokarst caves, and stream cutbanks. Burrows typically consisted of a single tunnel leading to a single chamber, though some burrows had multiple entrances, branching tunnels, or both. Tunnels in resting burrows were shorter than those in reproductive dens, and resting chambers were typically located at the deepest part of the burrow. Reproductive dens were associated with snowdrift-forming terrain features such as streambeds, cutbanks on lake edges, thermokarst caves, and boulders. Understanding such characteristics of Arctic wolverine resting and reproductive structures is critical for assessing anthropogenic impacts as snowpack undergoes climate-driven shifts.

Efficient and multifidelity terrain modeling for 3D large‐scale and unstructured environments

AbstractThe vast amount of data captured by robots in large‐scale unstructured environments brings computation and storage problems to terrain modeling methods. Considering the practical terrain geometry structures, we design a large‐scale terrain modeling framework for efficiently generating the compact and accurate terrain models from incomplete and uncertain measurements. The multiresolution elevation filtering is developed to generate terrain surfaces that preserve terrain details in the bumpy areas while achieving a sparse representation over the flat areas, which simultaneously reduces the computation time, storage space, and inference error. To further reduce the high computational complexity, we propose a computation decomposition strategy by adopting the Gaussian mixture model to partition the whole terrain surface into several subsurfaces, enabling the method to scale to much larger environments. For each subsurface, we train a Gaussian process model using the local data to adapt to the local terrain structure, concurrently resolving the data incompleteness and uncertainty. Furthermore, to provide a more informative terrain model, we efficiently derive terrain gradient through a closed‐form solution by treating the generated terrain models as continuous functions, enabling more robotics applications, such as the path planning. We perform extensive experiments to evaluate our framework, including the use of two medium‐sized terrain data sets to evaluate the overall and local performance, and two large‐scale glacier terrain and mountain terrain in the area of up to 9 km2 to validate the practical applications.

Structure‐from‐motion photogrammetry demonstrates that fine‐scale seascape heterogeneity is essential in shaping mesophotic fish assemblages

AbstractBenthic components of tropical mesophotic coral ecosystems (MCEs) are home to diverse fish assemblages, but the effect of multiscale spatial benthic characteristics on MCE fish is not well understood. To investigate the influence of fine‐scale benthic seascape structure and broad‐scale environmental characteristics on MCE fish, we surveyed fish assemblages in Seychelles at 30, 60 and 120 m depth using submersible video transects. Spatial pattern metrics from seascape ecology were applied to quantify fine‐scale benthic seascape composition, configuration and terrain morphology from structure‐from‐motion photogrammetry and multibeam echosounder bathymetry and to explore seascape–fish associations. Hierarchical clustering using fish abundance and biomass data identified four distinct assemblages separated by the depth and geographic location, but also significantly influenced by variations in fine‐scale seascape structure. Results further revealed variable responses of assemblage characteristics (fish biomass, abundance, trophic group richness, Shannon diversity) to seascape heterogeneity at different depths. Sites with steep slopes and high terrain complexity hosted higher fish abundance and biomass, with shallower fish assemblages (30–60 m) positively associated with aggregated patch mixtures of coral, rubble, sediment and macroalgae with variable patch shapes. Deeper fish assemblages (120 m) were positively associated with relief and structural complexity and local variability in the substratum and benthic cover. Our study demonstrates the potential of spatial pattern metrics quantifying benthic composition, configuration and terrain structure to delineate mesophotic fish–habitat associations. Furthermore, incorporating a finer‐scale perspective proved valuable to explain the compositional patterns of MCE fish assemblages. As developments in marine surveying and monitoring of MCEs continue, we suggest that future studies incorporating spatial pattern metrics with multiscale remotely sensed data can provide insights will that are both ecologically meaningful to fish and operationally relevant to conservation strategies.

Lithospheric Structure of the South India Precambrian Terrains From Surface Wave Tomography

AbstractWe have developed a 3‐D shear velocity (Vsv) model of Precambrian terrains of South India with a lateral resolution of 55 km to a depth of 250 km by inversion of fundamental mode Rayleigh wave phase velocity dispersion data in the period of 30–140s, combined with a well‐constrained crustal velocity model from an earlier study. The dispersion data were computed from 748 earthquakes recorded at 85 homogeneously distributed seismographs. Our velocity model shows 150–200 km thick lithosphere in most of the Archean Dharwar craton where Vsv is >4.7 km/s in the depth of 50–100 km and progressively decreases to 4.6 km/s at 120–150 km followed by a constant velocity of 4.5 km/s beyond 150–200 km depth. It correlates well with the results of the petrological studies of kimberlite xenoliths. An extraordinary high shear velocity (up to 4.8 km/s) and thick lithosphere (150 km) are observed beneath the Proterozoic Carbonatite complex, located at the south‐eastern edge of the Dharwar craton. We infer compositional modification of lower lithosphere at the south‐western margin of the Dharwar craton and lithospheric erosion at the Granulite terrain both possibly due to interaction with the Marion mantle plume at ∼90 Ma. The region is underlain by a 2%–3% lower velocity channel at ∼180–220 km depth in the asthenosphere, uncorrelated with the overlying lithosphere, possibly due to relative motion between them. It may be attributed to deep Earth‐processes such as asthenospheric upwelling.

A NEW FRAMEWORK FOR ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL ELEVATION MODELS

Abstract. Existing approaches to accuracy assessment of LiDAR-derived DEM are typically based on statistical methodology and focus on vertical accuracy. This paper presents a new framework which calls for assessment of not only a DEM’s vertical accuracy, but also its ability to preserve a point’s elevation rank in the bare earth topographic surface. New methods to assess each aspect are presented. For DEM’s vertical accuracy, approximation theory from numerical analysis is used to quantify the total error at each DEM point as well as its three error components – sensor error, ground error, and interpolation error. For DEM’s elevation order which is critical to model terrain structure, the concept of isomorphism in set theory is drawn as the mathematical rationale and Kendall’s rank correlation efficient is used to quantify the accuracy. The new framework is illustrated using a DEM derived from LiDAR for sea-level rise vulnerability assessment of a tidal salt marsh. Compared to conventional methods based on statistics, the new framework and methods produce detailed mapping of error distribution thus enable the identification of the main sources of error and where improvement is needed most. Results call for re-evaluation of the current practice of assessing filtering accuracy in LiDAR data processing and further research on relative elevation and isomorphism.

PTRM: Perceived Terrain Realism Metric

Terrains are visually prominent and commonly needed objects in many computer graphics applications. While there are many algorithms for synthetic terrain generation, it is rather difficult to assess the realism of a generated output. This article presents a first step toward the direction of perceptual evaluation for terrain models. We gathered and categorized several classes of real terrains, and we generated synthetic terrain models using computer graphics methods. The terrain geometries were rendered by using the same texturing, lighting, and camera position. Two studies on these image sets were conducted, ranking the terrains perceptually, and showing that the synthetic terrains are perceived as lacking realism compared to the real ones. We provide insight into the features that affect the perceived realism by a quantitative evaluation based on localized geomorphology-based landform features (geomorphons) that categorize terrain structures such as valleys, ridges, hollows, and so forth. We show that the presence or absence of certain features has a significant perceptual effect. The importance and presence of the terrain features were confirmed by using a generative deep neural network that transferred the features between the geometric models of the real terrains and the synthetic ones. The feature transfer was followed by another perceptual experiment that further showed their importance and effect on perceived realism. We then introduce Perceived Terrain Realism Metrics (PTRM), which estimates human-perceived realism of a terrain represented as a digital elevation map by relating the distribution of terrain features with their perceived realism. This metric can be used on a synthetic terrain, and it will output an estimated level of perceived realism. We validated the proposed metrics on real and synthetic data and compared them to the perceptual studies.

Variable storage behavior controlled by rainfall intensity and profile structure upon saturation excess overland flow generation

The effects of rainfall intensity and terrain structure on hillslope storage behavior upon saturation excess overland flow (SOF) generation are not yet fully understood. Analytical solution techniques that provide direct insights into the hillslope storage behavior were adopted in this study. The Threshold of StOrage (TSO) at critical rainfall intensity N0 (the minimum rainfall intensity to generate SOF) and the storage upon SOF generation (SO) at rainfall intensity N (>N0) were derived using analytical solutions of the kinematic wave equation over unit-width shallow sloping aquifers. The derived analytical solutions match the results of the numerical models of the Boussinesq equation for 25 idealized hillslopes under various rainfall intensities. It was found that as N = N0, TSO is determined by hillslope structures, i.e., TSO on the convex hillslopes is always greater than that on concave hillslopes with the same gradient, and TSO on convex and concave hillslopes gradually approaches as the gradient increases. As N > N0, hillslope storage dynamics is characterised by variable storage behavior as rainfall intensity increasing, i.e., SO increases with N. An inflection point of rainfall intensity N1, below which the curvature of the bedrock profile has a limited effect on SO, was found. Although N1 was greater on steeper hillslope, the corresponding values of SO1 on hillslopes under different gradients are close, which means that the effects of rainfall intensity and profile structure on SO is balanced at N = N1. This study helps to deepen our understanding of the quantitative role of rainfall intensity and profile structure in the process of runoff generation, and has the potential to improve the free storage capacity parameter in conceptual models.

О повышении эффективности геологического изучения Каспийского региона на базе уточненной картографической основы

This article aims to improve the methodology and approaches in substantiation of a unified cartographic base for prospective study, planning, and placement of research, forecasting of new oil and gas accumulations. In this case, the basic objectives are the refinement of the regional structure of the Caspian region by taking into account the main geological events during the formation and development, a more precise stratification of the section, specifically, the possible higher detailing of the Devonian and Cenozoic part. To this end, the main “tools” are considered to be the analysis of the paleogeographic environment, and peculiarities of sediment accumulation, which substantiate the formation of centers of maximum filling with fragmentary material, and anomalous objects (large carbon-bearing platforms and atollshaped massifs, mega-elevations, areas with non-conventional conditions for the oil-and-gas-formation processes). According to the preliminary results, their formation was preceded by the development of accumulative sedimentary forms of diverse genesis and paleobathymetric zoning of the sedimentation basin. The structural plan of the region and local anomalies are evaluated with due regard for such non-conventional features for the exploration geology as the structure of the daylight surface terrain, and the pattern of manifestation of anomalies of potential physical fields. Finally, a more detailed cartographic base allows refining some previously substantiated regional regularities in the manifestations of various types of minerals (hydrocarbons, solid commercial minerals, groundwater, ore, and valuable components), unique processes associated with the genesis of anomalous geological features, from the perspective of exploring, forecasting and detection of new oil and gas accumulations. A unified cartographic base constitutes a key factor for the expansion and active integration of geological organizations of all the five Caspian littoral states.

Unveil the unseen: Using LiDAR to capture time‐lag dynamics in the herbaceous layer of European temperate forests

Abstract To understand time‐lag dynamics in the response of biodiversity to contemporary environmental changes (e.g. macroclimate warming and atmospheric pollution), we need to consider former anthropogenic forcing factors such as past land uses and management practices that can have both compounding and confounding effects. This is especially true in European temperate forests, where legacies from past human activities have left strong imprints on today’s understorey plant species composition, generating long‐term lagging effects which can be mistakenly attributed to more recent macro‐environmental changes. By combining the expertise of plant, soil and historical ecologists together with remote sensing scientists, we review the potential of light detection and ranging (LiDAR) to unveil ghosts from the past in terms of former land uses and management practices. We show that imprints from past land uses and management practices can still be captured today through well‐chosen LiDAR‐derived variables describing, at sub‐decimetre scale, the vertical and horizontal micro‐variations of vegetation and terrain structure hidden below treetops. Synthesis. We encourage plant and soil ecologists to use LiDAR data and to work with historians, archaeologists and remote sensing scientists in order to select meaningful LiDAR‐derived variables to account for time‐lagged biotic responses to long‐term macro‐environmental changes.

Dredging fundamentally reshapes the ecological significance of 3D terrain features for fish in estuarine seascapes

ContextLandscape modification alters the condition of ecosystems and the structure of terrain, with widespread impacts on biodiversity and ecosystem functioning. Seafloor dredging impacts a diversity of flora and fauna in many coastal landscapes, and these processes also transform three-dimensional terrain features. The potential ecological significance of these terrain changes in urban seascapes has, however, not been investigated.ObjectivesWe examined the effects of terrain variation on fish assemblages in 29 estuaries in eastern Australia, and tested whether dredging changes how fish associate with terrain features.MethodsWe surveyed fish assemblages with baited remote underwater video stations and quantified terrain variation with nine complementary metrics (e.g. depth, aspect, curvature, slope, roughness), extracted from bathymetry maps created with multi-beam sonar.ResultsFish diversity and abundance were strongly linked to seafloor terrain in both natural and dredged estuaries, and were highest in shallow waters and near features with high curvature. Dredging, however, significantly altered the terrain of dredged estuaries and transformed the significance of terrain features for fish assemblages. Abundance and diversity switched from being correlated with lower roughness and steeper slopes in natural estuaries to being linked to features with higher roughness and gentler slopes in dredged estuaries.ConclusionsContrasting fish-terrain relationships highlight previously unrecognised ecological impacts of dredging, but indicate that plasticity in terrain use might be characteristic of assemblages in urban landscapes. Incorporating terrain features into spatial conservation planning might help to improve management outcomes, but we suggest that different approaches would be needed in natural and modified landscapes.

Қазақстанның оңтүстік шығысында өсетін эфир майлы Artemisia schrenkiana Ledeb өсімдігі қауымдастығының ерекшелігі

The article describes the features of the plant community of the territory where the plant Artemisia schrenkiana Ledeb, which grows in the Urzhar District of Abai Kazakhstan region, is found. The research work was carried out by Universal geobotanical methods. As a result of the study, the authors, with a comprehensive analysis of the microclimatic state, terrain structure and flora composition of the territory where the Artemisia schrenkiana Ledeb plant grows, described 163 plant species belonging to 116 genera in 31 families from the plant community. In the areas where the plant Artemisia schrenkiana Ledeb grows, the following five families predominate in terms of the number of species: the family of Poaceae or Gramineae, a family of Compositae Giseke, A family of Chenopodiaceae Vent, a family of Leguminosae Juss, a family of Boraginaceae Juss. Plants in the studied community were classified according to their useful properties as follows: 157 species of cattle, 38 species of medicinal plants, 5 species of poisonous plants, 10 species of essential oil plants, 3 species of rotting plants, 3 species of ornamental plants. Currently, no external factors have a strong negative impact on the plant community in the teritorry of the Urzhar district, where the medicinal plant Artemisia schrenkiana Ledeb grows. In the future, it will be possible to collect reserves of useful and medicinal plants from this region.

CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving

In the context of autonomous driving, the existing semantic segmentation concept strongly supports on-road driving where hard inter-class boundaries are enforced and objects can be categorized based on their visible structures with high confidence. Due to the well-structured nature of typical on-road scenes, current road extraction processes are largely successful and most types of vehicles are able to traverse through the area that is detected as road. However, the off-road driving domain has many additional uncertainties such as uneven terrain structure, positive and negative obstacles, ditches, quagmires, hidden objects, etc. making it very unstructured. Traversing through such unstructured area is constrained by a vehicle’s type and its capability. Therefore, an alternative approach to segmentation of the off-road driving trail is required that supports consideration of the vehicle type in a way that is not considered in state-of-the-art on-road segmentation approaches. To overcome this limitation and facilitate the path extraction in the off-road driving domain, we propose traversability concept and corresponding dataset which is based on the notion that the driving trails should be finely resolved into different sub-trails and areas corresponding to the capability of different vehicle classes in order to achieve safe traversal. Based on this, we consider three different classes of vehicles (sedan, pickup, and off-road) and label the images corresponding to the traversing capability of those vehicles. So the proposed dataset facilitates the segmentation of off-road driving trail into three regions based on the nature of the driving area and vehicle capability. We call this dataset as CaT (CAVS Traversability, where CAVS stands for Center for Advanced Vehicular Systems) dataset and is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.cavs.msstate.edu/resources/downloads/CaT/CaT.tar.gz</uri> .

Socio-hydrological modeling and its issues in Japan: a case study in Naganuma District, Nagano City

The Asian monsoon and Japan’s steep terrain structure make it a flood-prone country. These natural, flood-prone features have prompted Japan to develop unique social norms for flood risk management. Over the last decade, human-flood interaction models have been developed in socio-hydrology (SH), being applied and validated in various countries. This study applied the SH model for the Naganuma District of Nagano City, Japan, an area that was affected by Typhoon Hagibis in 2019. Additionally, the SH model was examined for its applicability in Japan using sensitivity analysis. To the best of our knowledge, this is the first study to apply the SH model in a real-life scenario in Japan. The results suggest that there are differences between the output from the existing SH model and the actual human-flood interactions in Japanese society. This paper also provides recommendations to improve the Japanese SH model and inform associated future research agendas in Japan.

A New Hybrid Sigma-Pressure Vertical Coordinate with Smoothed Coordinate Surfaces

Abstract An alternative hybrid sigma-pressure terrain-following coordinate is presented here that provides smoother coordinate surfaces over terrain by allowing a more rapid decay of the influence of smaller-scale topographic structures with height. This is accomplished by first defining a reference surface pressure that includes the influence of the underlying topography. A smoothed version of this reference surface pressure is then created that represents the larger-scale features of the topography, while the deviations from the smoothed profile contain the smaller-scale terrain structures. In the hybrid-sigma coordinate formulation presented here, the influences of these deviations in the reference surface pressure from their smoothed values are removed more rapidly with increasing height, thereby producing smoother coordinate surfaces. Testing this approach using several idealized simulations demonstrates a significant reduction in the artificial circulations compared to those arising with the basic sigma or the conventional hybrid sigma coordinate, confirming the beneficial aspects of the smoothed hybrid coordinate surfaces. The smoothed hybrid sigma-pressure coordinate proposed here provides flexibility in reducing the influence of the terrain on the coordinate surfaces and can be easily substituted for the basic hybrid sigma-pressure coordinate. Significance Statement Terrain-following vertical coordinates are widely employed in atmospheric numerical models because of their advantages in implementing boundary conditions at the surface. However, it is well known that these coordinates can promote artificial circulations due to numerical errors in computing horizontal pressure gradients and advection along the transformed coordinate surfaces. The purpose of this study is to improve the performance of a pressure-based terrain-following (sigma) hybrid vertical coordinate by selectively removing the influences of smaller-scale terrain features, which results in smoother coordinate surfaces. We demonstrate that this enhancement is beneficial in reducing numerical errors that are inherent in computing with terrain-following vertical coordinates.

A Method of Optimizing Terrain Rendering Using Digital Terrain Analysis

Terrain rendering is an important issue in Geographic Information Systems and other fields. During large-scale, real-time terrain rendering, complex terrain structure and an increasing amount of data decrease the smoothness of terrain rendering. Existing rendering methods rarely use the features of terrain to optimize terrain rendering. This paper presents a method to increase rendering performance through precomputing roughness and self-occlusion information making use of GIS-based Digital Terrain Analysis. Our method is based on GPU tessellation. We use quadtrees to manage patches and take surface roughness in Digital Terrain Analysis as a factor of Levels of Detail (LOD) selection. Before rendering, we first regularly partition the terrain scene into view cells. Then, for each cell, we calculate its potential visible patch set (PVPS) using a visibility analysis algorithm. After that, A PVPS Image Pyramid is built, and each LOD level has its corresponding PVPS. The PVPS Image Pyramid is stored on a disk and is read into RAM before rendering. Based on the PVPS Image Pyramid and the viewpoint’s position, invisible terrain areas that are not culled through view frustum culling can be dynamically culled. We use Digital Elevation Model (DEM) elevation data of a square area in Henan Province to verify the effectiveness of this method. The experiments show that this method can increase the frame rate compared with other methods, especially for lower camera flight heights.

Response of extreme significant wave height to climate change in the South China Sea and northern Indian Ocean

Abstract The frequency of extreme wave events is increasing with climate change. The temporal and spatial variations of extreme wave height affect both human livelihood and the usage of ocean resources. The South China Sea and northern Indian Ocean both support coastal communities of high population density, with varied terrain structures and extreme wind and wave systems. This study focuses on the temporal and spatial variations of the extreme significant wave height in the South China Sea and northern Indian Ocean. Using nonstationary generalized extreme value analysis, trends for a 100-year return period of significant wave height were obtained for both. The most rapid increase in the 100-year return was found to be 0.015 m yr-1 in the northern South China Sea and in the Arabian Sea; however, the 100-year return significant wave height fell in the mouth of the Bay of Bengal. After analyzing the possible causes and influence factors, we found that the increase in significant wave height in the northern South China Sea was dominated by local wind-waves and similarly, the Arabian Sea was affected by swell. The NINO3.4 index shows good correlation with the significant wave height in the northern South China Sea because typhoons are related to NINO3.4 in this area. The trends of the extreme wave height in the Arabian Sea and southern Bay of Bengal have good correlations with the South Asian summer monsoon index.

Simulations in the Topography Effects of Tianshan Mountains on an Extreme Precipitation Event in the Ili River Valley, China

Xinjiang is located in an arid and semi-arid climate region in China, but Xinjiang Ili river valley is more humid, with higher precipitation intensity and precipitation, which is closely related to the role of the Tianshan Mountains. In this paper, through the NCRP 1° × 1° reanalysis data and the conventional observation data of the Ili River Valley in Xinjiang, the terrain sensitivity experiment conducted by the WRF model is used to analyze the short-term extreme precipitation event of the Ili River Valley from 18–19 of May 2017, to reveal the influence of Tianshan Mountains on the extreme precipitation event of the Ili River Valley. The results show that: (1) The reduction or removal of the terrain will cause a wide range of wind field changes, weaken the vertical upward movement of the windward slope, and the accumulation of water vapor before the windward slope will also be reduced; a large-scale change of the terrain will also affect the direction of water vapor transportation. These effects together lead to a decrease or increase in regional precipitation. (2) “Fuzzy” (smooth) terrain will affect the precipitation simulated by changing the local vertical movement and water vapor transport, which shows that the WRF model’s accurate description of the terrain structure characteristics of mountainous areas is beneficial to accurately simulate the precipitation process on the windward slope area.

Extraction of linear structures from digital terrain models using deep learning

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.

The role of electromagnetic induction in development planning: the case of the Grands Philambins enigma

ABSTRACTSpatial planning aims at constantly improving the distribution of people and activities within available space, especially near cities on ancient agricultural or industrial areas. However, a proper schedule of the development work necessitates the assessment, as precisely as possible, of the difficulties that would be encountered in terms of geotechnical abilities of the terrain and hazards resulting from previous use. In the Grands Philambins site, three different geophysical techniques were combined: multi‐depth resistivity; magnetic gradiometry; and electromagnetic induction. The aim of this study was to map, in a few days and with high measurement densities, all the electromagnetic properties of the terrain and to evidence metallic features. The acquired data were compared with prior documentation, mainly the aerial photos acquired since the middle of the 20th century. The terrain structure is complex, at the junction of geological formations composed of limestones and marls in different proportions, which were characterized by the geophysical survey. It also exhibits metallic features, among which an enigmatic 70 m diameter radially striped disc object, which was identified as non‐ferrous and corresponding to an ancient antenna earthed socket. This case study illustrates the relevance of the use of electromagnetic induction measurements in land planning studies as this method can map three independent underground properties and identify metallic features.