Terrain Mapping Research Articles

Research on Multi-Beam Sounding Based on Local Optimal Model

Multi-beam sounding technology can provide higher spatial resolution than single-beam sounding by transmitting fan-shaped acoustic signals from the transducer of a survey ship along the normal plane of the survey line to measure the sea depth. It is of great significance to study the line design scheme of multi-beam sounding for uneven seabed topography to improve the efficiency of underwater terrain mapping. This paper mainly studies the optimization of multi-beam sounding line under ideal slope. Firstly, a single angle model of ideal slope is established, and the flat seabed overlap ratio formula is extended to slope seabed to calculate the coverage width of beam sounding. Secondly, an angle model of ideal slope is established to calculate the corresponding coverage width under different positions of the slope sea area. Finally, the optimal routing model of ideal slope is established. The design scheme of the local optimal survey line is obtained through comprehensive analysis. In engineering practice, the optimal routing of real terrain can be solved by using the ideal slope optimal routing model through the idea of plane approximation, which provides a new idea for the optimal route selection of underwater terrain mapping.

Geopatial Technologies in Civil Engineering : A Critical Literature

This study investigates the role of geospatial technologies in enhancing the efficiency and effectiveness of civil engineering projects. Geospatial technologies, including Geographic Information Systems (GIS), remote sensing, and spatial analysis, have revolutionized the field of civil engineering by providing critical tools for planning, design, construction, and management of infrastructure projects. The integration of these technologies enables accurate terrain mapping, real-time monitoring, and data-driven decision-making, which are crucial for ensuring the safety, sustainability, and resilience of modern infrastructure. This research aims to explore the applications of geospatial technologies in various civil engineering projects, including highway planning, urban development, and environmental monitoring. The study also examines the potential benefits and limitations of these technologies and provides recommendations for their effective integration into civil engineering practices. By understanding the impact of geospatial technologies on civil engineering projects, this research aims to contribute to the development of more efficient, sustainable, and resilient infrastructure that supports the needs of modern society.

Lithological discrimination and mapping in part of the upper Benue Trough using Landsat 8

This study focused on evaluating the efficiency and complementarity of image enhancement techniques for lithological discrimination and mapping in the upper parts of Benue Trough using Landsat 8 data. To achieve this, image enhancement techniques such optimum index factor, band ratio, principal component analysis, and minimum noise fraction were applied on Landsat 8 OLI data covering parts of the Upper Benue Trough. False colour composites (FCC) were created from three most informative derivatives of these image enhancements. The FCCs were compared in terms of their capability to differentiate different lithological units in the study area by comparing the FCCs with existing geological map. The results show that the FCC created from band ratios provided the highest amount of information, while those created based on optimum index factor provided the least amount of information with respect to lithological discrimination. Additionally, it was observed that the FCCs created from the derivatives of band ratio, principal component analysis and minimum noise fraction provide complementarity. Thus, the three FCCs from these three enhancement methods were fused into a single image, which further aided lithological discrimination. By combining the outputs from three image enhancement techniques, most of the sedimentary and crystalline rocks in the study area were successfully delineated. The method adopted in this study particularly the fusion of outputs from band ratio, principal component analysis and minimum noise fraction represents a novel approach that is suitable for lithological mapping in remote areas, particularly arid and semi-arid terrains with considerable rock exposure.

Tree parameter extraction method based on new remote sensing technology and terrestrial laser scanning technology

Ground LiDAR is a terrestrial LiDAR system that is often used for terrain and geomorphic mapping. Ground-based LiDAR can be used to collect more local and short-range data, making it ideal for mapping smaller areas with high precision. In order to solve the rapid extraction of tree parameters in the national public welfare forest survey, the ground-based LIDAR was used to obtain the point cloud of trees, and the point cloud data was registered, denoised, normalized, sliced, parameter extracted, etc., and the parameters of individual trees in the forest were obtained. The Bland-Altman consistency test is used to test whether the method of extracting tree parameters from point clouds is consistent with the traditional measurement method. The experimental results show that the point cloud data obtained by the ground-based LIDAR can quickly, conveniently and accurately extract the tree parameters, which is consistent with the traditional tree parameter extraction method, and has the advantages than the traditional tree parameter measurement, such as point cloud, image and traceability. It has a unique advantage in establishing a tree database. It is suggested that LIDAR should be used for forest survey in the future.

Landing Site Mapping and Selection with Quadtree Map Using Planar Elements

This work proposes a filter-based combined terrain mapping and landing site selection system for planetary landing. The authors consider a spacecraft acquiring flash lidar measurements of the surface during terminal descent from which it must select a landing site. A quadtree is used to create a variable-resolution map of the terrain in which each element contains a planar estimate. This greatly simplifies the landing site selection system by providing a direct estimate of terrain slope. The authors' system is validated with flight-test data gathered by a rocket-powered vehicle approaching an artificial lunar environment.

Automated Lunar Crater Identification with Chandrayaan-2 TMC-2 Images using Deep Convolutional Neural Networks.

Terrestrial planets and their moons have impact craters, contributing significantly to the complex geomorphology of planetary bodies in our Solar System. Traditional crater identification methods struggle with accuracy because of the diverse forms, locations, and sizes of the craters. Our main aim is to locate lunar craters using images from Terrain Mapping Camera-2 (TMC-2) onboard the Chandrayaan-II satellite. The crater-based U-Net model, a convolutional neural network frequently used in image segmentation tasks, is a deep learning method presented in this study. The task of crater detection was accomplished with the proposed model in two steps: initially, it was trained using Resnet18 as the backbone and U-Net based on Image Net as weights. Secondly, TMC-2 images from Chandrayaan-2 were used to detect craters based on the trained model. The model proposed in this study comprises a neural network, feature extractor, and optimization technique for lunar crater detection. The model achieves 80.95% accuracy using unannotated data and precision and recall are much better with annotated data with an accuracy of 86.91% in object detection with TMC-2 ortho images. 2000 images have been considered for the present work as manual annotation is a time-consuming process and the inclusion of more images can enhance the performance score of the model proposed.

Analysis of faults and pit chains in Noctis Labyrinthus: Implications for early extension and possible magmatic plumbing

Noctis Labyrinthus has been a region of disputed origin due to its complexity and poor understanding of how various processes and mechanisms may have combined to form it. The surface is an integrated record of intensive tectonic activity expressed by a multiple extended sets of dip-slip faults oriented in different directions, and thought to have acted on this region over its history. These faults are always coalescent to pits and pit chains displaying a complicated geological history in the region. To understand this geological history, we mapped the surface features in Noctis Labyrinthus using the High-Resolution Stereo Camera (HRSC) onboard Mars Express ND2 nadir channel basemaps, and we adapted the Digital Terrain Map (DTM) from the Mission Experiment Gridded Data Record (MEGDR) of Mars Orbiter Laser Altimeter (MOLA) onboard Mars Global Surveyor (MGS) for the topography. We have investigated the spatial distribution and trend of fault systems, the pit chains' morphology, and the correlation between these two types of features. Our results show three fault systems: i) NS and NNE-SSW, ii) EW and ENE-WSW, and iii) NNW-SSE and NW. The analysis of the faults trending, cross-cutting correlation and the superimposition led to identify multiple intersections between these faults that have been alongside with the reactivations of some inherited faults. We interpreted the first system of fault to be related to coeval lateral extension, generated by regional stress tensor, which is probably related to the slight bending of Valles Marineris within two phases of bidirectional extension. The second system of faults has been generated by the radial oblate stress tensor related to the formation of the small shield volcanoes in Syria Planum. However, the third system is likely related to the external driving process, probably in the Tharsis province. We classified pits in four evolutionary stages based on their morphometric attributes. We believe that the formation of the pit chains in Noctis Labyrinthus is related to a surface collapse after a pressure drop related to the magma chamber deflation associated with Syria Planum volcanic province. We propose a deformational model based on early extension and magmatic plumbing as driving processes for the formation of Noctis Labyrinthus.

Is the Tupul (Makhuam) Landslide of 29-30 June 2022 in Manipur, India Recognized as one of the Rainfall Induced Massive Landslide?

The present study deals with Tupul landslide in Manipur which triggered on the intervening night of 29th -30th June 2022 in Noney district of Manipur. The death toll was 61 persons in total from Territorial Army, NF Railway’s employees, Villagers and labours from various contractors in the construction of railway line from Jiribam to Imphal. We have conducted a drone survey on the landslide site for preparation of large-scale terrain map and compared 2009 CARTOSAT image to pick up the slip surfaces. The detailed fieldwork of the study area deciphers highly jointed, fractured, faulted rock formations and display neo-tectonic movements. The slope failure analysis was conducted using the Phase-2 software and the failure was compared with the Drone made DEM, which is used to estimate the volume of debris falling on the railway formations. The excessive precipitation (375.6mm) during the May 2022 was recorded near the landslide site which was 60% more than the normal precipitation. In order to maintain the safety of the man and materials for the future, a number of suggestions were proposed for future monitoring of slope along coming up railway lines of Northeast Frontier Railways.

A new height contouring method for severity prediction in cam-type hip joints: 20 subject-specific cases

Cam-type femoroacetabular impingement syndrome (FAIS) is characterised by a non-spherical continuation of the femoral head into the femoral head-neck junction and is associated with damage to the acetabular cartilage. Diagnostic methods based on cam shape are progressing from 2D radiographic measurements to 3D CT measures, providing greater insight. There is currently no consensus on how to describe the 3D cam lesion shape and subject-specific impingement mechanisms are difficult to determine.A novel 3D ‘contour’ method was used to describe the proximal femur of 20 cam-type hips. Five contours, analogous to height contours on a terrain map, were used to describe the femoral head-neck junction, capturing the progressive height of the cam lesion. From that description, the cam apex (a subject's largest alpha angle), cam extent (spread around the femoral head), cam location (position around the femoral head) and average acetabular coverage, were recorded. A previously developed hip impingement model was used to apply 126 activity-based motions to each subject-specific hip shape and predict impingement occurrence and depth of incursion past the acetabular rim. Correlations between shape measures and impingement occurrence were investigated.The two contours representing the lowest heights (close to the head best fit sphere and 1 mm greater than that) generated cam alpha angle and cam extent measurements which contained the typical clinical measures (Alpha: close to best fit 47°-98°, at 1 mm 45°–77°; Extent: close to best fit 0°–129°, at 1 mm 0°–100°). The remaining contours described the progressive height of the cam lesion up to 4 mm greater than the head radius. Impingement was predicted predominantly from the first 1 mm height of the cam, with only two subjects impinging at a cam height greater than 2 mm. Therefore, it is possible that adequate resection of the first 1 mm of cam height is the most critical in reducing a subject's impingement severity.Impingement occurrence was positively correlated with the cam apex (ρ = 0.84 close to best fit, ρ = 0.70 at 1 mm height), the cam extent (ρ = 0.68 close to best fit, ρ = 0.80 at 1 mm height) and the acetabular coverage (ρ = 0.50, at 1 mm height). However, in line with other work on cam impingement, correlations between any single shape measure and the risk of impingement were not strong enough to be used with confidence as predictive tools. This supports the further development of modelling tools which sufficiently capture the complex shape and can generate an impingement risk metric which accounts for joint motion.

Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model’s Generalizability in Permafrost Mapping

This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models and their defining characteristics. Built upon the tremendous success achieved by Large Language Models (LLMs) as the foundation models for language tasks, this paper discusses the challenges of building foundation models for geospatial artificial intelligence (GeoAI) vision tasks. To evaluate the performance of large AI vision models, especially Meta’s Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model. A series of prompt strategies were developed to test SAM’s performance regarding its theoretical upper bound of predictive accuracy, zero-shot performance, and domain adaptability through fine-tuning. The analysis used two permafrost feature datasets, ice-wedge polygons and retrogressive thaw slumps because (1) these landform features are more challenging to segment than man-made features due to their complicated formation mechanisms, diverse forms, and vague boundaries; (2) their presence and changes are important indicators for Arctic warming and climate change. The results show that although promising, SAM still has room for improvement to support AI-augmented terrain mapping. The spatial and domain generalizability of this finding is further validated using a more general dataset EuroCrops for agricultural field mapping. Finally, we discuss future research directions that strengthen SAM’s applicability in challenging geospatial domains.

Exploring the Use of Terrestrial Robots for Atmospheric Electricity Measurement

Measuring the electric field is a central goal in electrostatics research, such as the study of electrostatics in atmospheric processes. It serves as a key indicator for various atmospheric phenomena, including the presence of lightning, dust or charged clouds. Traditionally, electric field measurements have been conducted from static platforms, with limited mobile measurements from airborne platforms such as balloons or, more recently, Unmanned Aerial Vehicles (UAVs). Here, we explore the potential of terrestrial robots to measure the electric field with some level of autonomy, such as during supervised navigation between user-defined waypoints. We mount a field mill on a four-wheeled rover and use a Global Navigation Satellite System (GNSS) to track the position of its measurements during an outdoor survey. The robot has a depth camera for 3D terrain mapping to contextualise local field measurements. We present plans for future research, including the use of semi-autonomous identification and exploration of electrostatic ‘hotspots’; and deployment of multiple robots (e.g., six) in a ‘sparse swarm’ configuration. We consider opportunities to employ such a robot system in environmental science or space research, for instance on Mars or the moon, where an understanding of electrostatic processes could be significant for future space missions.

DETERMINATION OF THE OPTIMAL TRAJECTORY OF THE MOVEMENT OF AIRCRAFT IN AREAS WITH COMPLEX TERRAIN UNDER THE CONTROL OF THE ENEMY

One of the main issues in the controlling of aircraft in difficult terrain during wartime is to ensure normal movement, but also to fulfill the requirements of evading enemy control. This paper proposes an improved ant swarm algorithm that makes it possible to pre-determine and optimize the trajectory of aircraft in such areas. When applying this method, a special parameter is included in the probability of choosing a movement trajectory – the height of the terrain above sea level, so that each ant does not enter territory controlled by the enemy. Using a 2D-H digital elevation map, the rectangular area under study is divided into 90 m × 90 m squares. To take into account the variability of the terrain, the heuristic function of the ant swarm algorithm takes into account the parameters of distance, height and smooth surface. Additionally, to reduce the number of iterations and computations, the ants are divided in half by number and released from the start and end points simultaneously. As a result, it allows you to choose the shortest and minimum trajectory among various calculated trajectories. To verify the effectiveness of the proposed scheme, a number of computational experiments were conducted. Experimental results on various simulated and real terrain maps show that this algorithm can be used to select an initial reference trajectory in difficult terrain.

Real-Time Walk Error Compensation Method Using Echo Signal Magnitude Measurement in ToF Laser Scanners.

The rapid advancement of mobile laser scanner technology used for terrain mapping, among other things, imposes increasing requirements for scanning frequency and distance measurement accuracy. To meet these requirements, rangefinder modules are expected to operate with high echo signal dynamics and to allow accurate distance measurement even based on single-laser-pulse echo detection. Such performance can be potentially achieved using pulsed time-of-flight (ToF) laser rangefinders (LRF). In conventional ToF modules, however, the STOP signal (for time counter interruption) is generated using a straightforward fixed-threshold comparator method. Unfortunately, it corresponds to the so-called walk error, i.e., the dependence of the measured time of flight on the magnitude of the echo signal. In most ranging applications, however, the LRF detection channel can be exposed to an extremely large span of received echo power levels, which depend on the distance measured, type of target surface, atmospheric transmission, etc. Thus, the walk error is an inseparable element of the conventional ToF technique and creates a fundamental limit for its precision. This article presents a novel method of walk error compensation in real time. By using our authorial electronic circuit for measuring the magnitude of the echo signal, it is possible to effectively compensate for the walk error even when the echo signal brings the detection channel amplifiers into saturation. In addition, the paper presents a laboratory method for calibrating the walk error compensation curve.

Probabilistic Height Grid Terrain Mapping for Mining Shovels using LiDAR

This paper explores the question of creating and maintaining terrain maps in environments where the terrain changes. The specific example explored is the construction of terrain maps from 3D LiDAR measurements on an electric rope shovel. The approach extends the height grid representation of terrain to include a Hidden Markov Model in each cell, enabling confidence-based mapping of constantly changing terrain. There are inherent difficulties in this problem, including semantic labelling of the LiDAR measurements and determining the pose of the sensor. The significance of this work lies in the need for accurate terrain mapping to support autonomous machine operation.

Synthetic aperture optical image restoration based on multi-scale feature enhancement

With the wide applications of high-resolution imaging technology in topographic mapping, astronomical observation, and military reconnaissance and other fields, the requirements for imaging resolution of optical system are becoming higher and higher . According to the diffraction limit and Rayleigh criterion, the imaging resolution of the optical system is proportional to the size of the aperture of the system, but affected by the material and the processing of the optical component: the single aperture of the optical system cannot be infinitely enlarged. Therefore the synthetic aperture technology is proposed to replace the single large aperture optical system. Owing to the effect of sub-aperture arrangement and light scattering, the imaging of synthetic aperture optical system will be degraded because of insufficient light area and phase distortion. The traditional imaging restoration algorithm of synthetic aperture optical system is sensitive to noise, overly relies on degraded model, requires a lot of manually designed models, and has poor adaptability. To solve this problem, a multi-scale feature enhancement method of restoring the synthetic aperture optical image is proposed in this work. U-Net is used to obtain multi-scale feature, and self-attention in mixed domain is used to improve the ability of of the network to extract the features in space and channel. Multi-scale feature fusion module and feature enhancement module are constructed to fuse the information between features on different scales. The information interaction mode of the codec layer is optimized, the attention of the whole network to the real structure of the original image is enhanced, and the artifact interference caused by ringing is avoided in the process of restoration. The final experimental results are 1.51%, 4.42% and 5.22% higher than those from the advanced deep learning algorithms in the evaluation indexes of peak signal-to-noise ratio, structural similarity and perceived similarity, respectively. In addition, the method presented in this work has a good restoration effect on the degraded images to different degrees of synthetic aperture, and can effectively restore the degraded images and the images with abnormal light, so as to solve the problem of imaging degradation of synthetic aperture optical system. The feasibility of deep learning method in synthetic aperture optical image restoration is proved.

PRELIMINARY ASSESSMENT OF GROUNDWATER POTENTIAL ZONES IN AYEDE-EKITI, EKITI STATE, SOUTHWESTERN NIGERIA USING ELECTRICAL METHODS

An electrical resistivity survey was conducted at different locations within Ayede Ekiti, Ekiti State, Southwestern Nigeria. This is aimed at evaluating groundwater potential zones and aquifer properties within the study area. Data were acquired at four (4) different locations using the vertical electrical sounding method by employing the Schlumberger array configuration. This study utilizes resistivity data processed with IPI2win software to analyze the subsurface geology of a basement complex region. The result of the study showed that qualitatively, three major curve types (A, HA, and H) were observed. Varying curve types, predominantly A curves, were observed across the research area, with notable groundwater potential indicated by H and HA curve types at select points. Through curve matching techniques, three to four distinct geo-electric layers were identified, while the quantitative interpretation resulted in deducing layers with different resistivity and thickness values for each VES(s) of the study area. It was deduced that the VES 2 and 3 zones are connected to a high output of groundwater. However, VES 1 and 4 are characterized by low groundwater potential. This research contributes valuable insights for geological mapping and groundwater resource assessment in complex basement terrains of the study area.

3D Terrain Mapping and Filtering From Coarse-Resolution Data Cubes Extracted From Real-Aperture 94-GHz Radar

3D Terrain Mapping and Filtering From Coarse-Resolution Data Cubes Extracted From Real-Aperture 94-GHz Radar

АЛГОРИТМЫ ПРЕОБРАЗОВАНИЯ ЦИФРОВОЙ КАРТОГРАФИЧЕСКОЙ ИНФОРМАЦИИ ДЛЯ РЕШЕНИЯ ЗАДАЧ ВЫБОРА МЕСТОПОЛОЖЕНИЯ ЭЛЕМЕНТОВ ГРУППИРОВКИ СИЛ И СРЕДСТВ МЧС РОССИИ

Mathematical and information support of modern and promising control automation complexes in EMERCOM of Russia currently include mathematical models, methods and algorithms for processing a digital terrain map, as well as formats for using a digital map. 
 Presents the developed algorithms for converting digital cartographic information to solve the problems of choosing the location of elements of the grouping of forces and means of EMERCOM of Russia, which are based on the joint use of possibilities for processing digital maps of the area and methods of evolutionary modeling.

Day and night continuous high-resolution shallow-water depth detection with single-photon underwater lidar.

Single-photon lidar has emerged as a strong technology for bathymetric measurements. However, its heightened sensitivity additionally makes it susceptible to solar radiation noise, particularly in the green light wavelength where solar radiation is strong, posing challenges for its daytime operation. To address this issue, a single-photon underwater lidar system is proposed and demonstrated. This scheme has these features. 1) Underwater applications not only mitigate the impact of the air-water interface on laser transmission but also significantly attenuate solar radiation reaching the lidar due to the absorption and scattering properties of water. 2) The telescope is designed with a small aperture and narrow field of view to significantly suppress solar radiation. 3) A combination of a narrowband laser and narrowband filter technique is effectively employed to minimize residual solar radiation, thus enabling continuous bathymetric observation capabilities during both day and night. 4) After acquiring the backscattered signal from the bottom, a water depth extraction algorithm utilizing bi-Gaussian fitting is proposed. To demonstrate the robustness of the lidar and the effectiveness of the algorithm, the underwater single-photon lidar system is deployed on a ship to conduct cruise surveys of two bays in the nearshore area, as well as a full-day stationary observation experiment. The lidar measurements are highly consistent with the synchronized sonar observations. The full-day stationary observation experiment showcased its capability to deliver continuous measurements throughout the day and night. These results demonstrate the potential of the system in various applications, including high-precision underwater terrain mapping, obstacle avoidance for underwater platforms, and underwater target imaging.

PAN-SUNET: UTILITY CORRIDOR UNDERSTANDING USING SPATIAL LAYOUT CONSISTENCY

Abstract. The article addresses the need for a dependable and efficient computer vision system to examine utility networks with minimal human intervention, given the deteriorating state of these networks. To classify the dense and irregular point clouds obtained from the airborne laser terrain mapping (ALTM) system, which is used for data collection, we suggest a deep learning network named Panoptic-Semantic Utility Network (Pan-SUNet). The proposed network incorporates three networks to achieve voxel-based semantic segmentation and 3D object detection of the point clouds at various resolutions, including object categories in three dimensions, and predicts two-dimensional regional labels to differentiate utility and corridor regions from non-corridor regions. The network also ensures spatial layout consistency in the prediction of the voxel-based 3D network using regional segmentation. By testing the proposed approach on 67 km2 of utility corridor data with an average density of 5 pts/m2, the paper demonstrates the effectiveness of the technique. The proposed network outperforms the state-of-the-art baseline network, achieving an F1 score of 94% for the pylon class, 99% for the ground class, 99% for the vegetation class, and 99% for the powerline class. It also shows high performance for 3D object detection for pylon and span achieving average precision of 99% and 92% respectively.