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This work addresses the problem of mapping terrain features based on inertial and LiDAR measurements to estimate navigation cost, for an autonomous ground robot. The navigation cost quantifies the degree of how easy or difficult it is to navigate through different areas. Unlike most indoor applications, where surfaces are usually human-made, flat, and structured, external environments may be unpredictable as to the types and conditions of the travel surfaces, such as traction characteristics and inclination. Attaining full autonomy in outdoor environments requires a mobile ground robot to perform the fundamental localization and mapping tasks in unfamiliar environments, but with the added challenge of unknown terrain conditions. Autonomous motion in uneven terrain has been widely explored by the research community focusing on one or more of the several factors involved aiming at both safety and efficient displacement. A fuller representation of the environment is fundamental to increase confidence and to reduce navigation costs. To this end we propose a methodology composed of five main steps: (i) speed-invariant inertial transformation; (ii) roughness level classification; (iii) navigation cost estimation; (iv) sensor fusion through Deep Learning; and (v) estimation of navigation costs for untraveled regions. To validate the methodology, we carried out experiments using ground robots in different outdoor environments with different terrain characteristics. Results show that the inertial data transformation reduces the dispersion of signal magnitude for different speeds and scenarios. Meanwhile, the roughness level classification achieved a mean accuracy of 95.4%, for the speed of 0.6 m / s . Finally, the obtained terrain maps are a faithful representation of outdoor environments allowing accurate and reliable path planning.

Based on the RIEGL 3D scanning system, this paper studies the key technical methods of 3D laser scanning technology in the whole process of data acquisition, data processing and topographic mapping, and forms an effective technical route of 3D laser scanning technology in large scale topographic mapping.The transformation relationship among the coordinate system of the scanner itself, the engineering coordinate system, the global coordinate system and the image machine coordinate system is explained.The point cloud data registration, “non-topographic point” filtering technology, arbitrary point cloud data network construction technology and other technical methods are introduced.Better results can be achieved through multi-station acquisition and the combination of GPS and ground 3D laser scanning system.

Multibeam Bathymetry is an advanced underwater terrain mapping technology that has gained widespread application in recent years for searching and locating underwater targets. This article introduces the basic principles and system composition of multibeam bathymetry, discusses the current research status of related technologies, and looks forward to the future trends and challenges of multibeam bathymetry technology, including technological innovation, improvements in data processing algorithms, and integration with other technologies. This article aims to provide reference and guidance for the application of multibeam bathymetry technology in searching and locating underwater targets and to promote further development and application of this technology.

Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.

This paper presents a novel methodology for localization and terrain mapping along a defined course such as narrow tunnels and pipes, using a redundant unmanned ground vehicle kinematic design. The vehicle is designed to work in unknown environments without the use of external sensors. The design consists of two platforms, connected by a passive, semi-rigid three-bar mechanism. Each platform includes separate sets of local sensors and a controller. In addition, a central controller logs the data and synchronizes the platforms’ motion. According to the dynamic patterns of the redundant information, a fusion algorithm, based on a centralized Kalman filter , receives data from the different sets of inputs (mapping techniques), and produces an elevation map along the traversed route in the x - z sagittal plane. The method is tested in various scenarios using simulated and real-world setups. The experimental results show high degree of accuracy on different terrains. The proposed system is suitable for mapping terrains in confined spaces such as underground tunnels and wrecks where standard mapping devices such as GPS, laser scanners and cameras are not applicable.

The Earth’s terrain is linked to many physical processes, and gaining the most accurate representation is key to work in many sectors from engineering to natural hazards modeling and ecology. Existing global digital elevation models (DEMs) are widely used, however often suffer from systematic biases caused by trees, buildings and instrumentation error, ultimately limiting their effectiveness. We present here, FathomDEM, a new global 30 m DEM produced using a novel application of a hybrid vision transformer model. This model removes surface artifacts from a global radar DEM, Copernicus DEM, aligning it more closely with true topography. In addition to improving on other global DEMs, FathomDEM also has reduced error compared to coastal-focussed DEMs such as the recent DeltaDTM. This demonstrates its impressive capacity to perform for specific landscapes, while being trained globally to model a wide range of terrain types. FathomDEM has been tested on the downstream task of flood modeling, showing increased accuracy compared to those run with the previous best global DEM, FABDEM, approaching the performance of LiDAR based flood modeling. This improvement is attributed to FathomDEM’s smaller error and substantial reduction in artifacts. This shows the suitability of FathomDEM for applied tasks and strengthens our evaluation compared to one based on vertical error alone.

The Canadian Space Agency (CSA) has contributed to the Origins Spectral Interpretation Resource Identification Security-Regolith Explorer (OSIRIS-REx) spacecraft the OSIRIS-REx Laser Altimeter (OLA). The OSIRIS-REx mission will sample asteroid 101955 Bennu, the first B-type asteroid to be visited by a spacecraft. Bennu is thought to be primitive, carbonaceous, and spectrally most closely related to CI and/or CM meteorites. As a scanning laser altimeter, the OLA instrument will measure the range between the OSIRIS-REx spacecraft and the surface of Bennu to produce digital terrain maps of unprecedented spatial scales for a planetary mission. The digital terrain maps produced will measure ∼ 7 cm per pixel globally, and ∼ 3 cm per pixel at specific sample sites. In addition, OLA data will be used to constrain and refine the spacecraft trajectories. Global maps and highly accurate spacecraft trajectory estimates are critical to infer the internal structure of the asteroid. The global and regional maps also are key to gain new insights into the surface processes acting across Bennu, which inform the selection of the OSIRIS-REx sample site. These, in turn, are essential for understanding the provenance of the regolith sample collected by the OSIRIS-REx spacecraft. The OLA data also are important for quantifying any hazards near the selected OSIRIS-REx sample site and for evaluating the range of tilts at the sampling site for comparison against the capabilities of the sample acquisition device.

To investigate the effect of the loading rate on the tensile strength of rock material and the morphology of the resulting split fracture surfaces, three types of rock specimens, namely, granite, basalt and limestone, were collected and tested with Brazilian testing under different loading rates. The tensile strength was measured, and the effect of the loading rate on the tensile strength of the rock material was studied. Digital terrain map models of the split fracture surface were obtained with an optical 3D scanning technique, and the effects of the loading rate on the geometry and morphology of the fracture surface were studied. The influence of the loading rate and tensile strength on the roughness was studied quantitatively by calculating the roughness indices of a fracture surface for all three kinds of rock. The research results show that the rock tensile strength increases with the loading rate. A linear relationship was established in double-logarithmic coordinates to describe the relationship between the tensile strength and the loading rate. Four different roughness indices were used to describe the morphology of the split fracture surface. The analysis results show that the magnitudes of all the roughness indices increase with the loading rate. Additionally, the roughness indices for all three types of rock linearly increase with the tensile strength. This linear trend indicates that it is possible to utilize fracture surface roughness indices to estimate rock tensile strength. The current study may motivate further research on the relationship between the morphology indices of rock fractures and mechanical parameters of the rock.

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system’s potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy.

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.