Multi-Sensor Terrain Reconstruction for High-Resolution Urban Flood Modelling

The volcanic Trindade Island is a remote Brazilian offshore territory in the South Atlantic, located ca. 1.140 kilometers east of the southeast coast of Brazil. The island’s permanent exposure to geological hazards requires assessment. However, the lack of erosion and landslides temporal data impedes predictive geohazard analyses. Therefore, we compiled pre-existing data from nautical charts and surveyed the surface terrain on Trindade Island to generate Digital Terrain Models (DTMs) and comparative accuracy analyses. The DTM based on pre-existing data shows the lowest accuracy (root mean square error - RMSE: 12.3 m) yet is adequate for regional studies. In contrast, the DTM developed from real-time kinematic global navigation satellite systems (RTK-GNSS) has the highest vertical accuracy (RMSE: 0.48 m), but spatial variability of ground elements was underestimated and limited to meter-sized (and larger) elements. The DTM obtained using the unmanned aerial vehicle (UAV) with ground control points (GCP), on the other hand, presented lower accuracy (RMSE: 2.37 m) than the RTK-GNSS model but still allowed observation of centimetric (and larger) ground features. For geohazard assessment on Trindade Island, models that allow fine-scale studies are needed. A UAV with GCP provides such standards and proved to be the most viable option in remote and complex sites as well. Hence, this study, the first to allow multi-temporal analysis of geohazard assessment on Trindade Island, offers a viable solution for similar analyses in other remote locations.

The launch of the very high resolution (VHR) sensor satellites has paved the way for further exploitation of the capabilities of stereo satellite imagery for many applications and most importantly in the extraction of topographical details. Advances in digital technologies in the photogrammetric technique provide more accurate and up to date topographic representation, while traditional methods of topographic mapping are time-consuming and require large technical support. This paper explores the methods of production and an analysis of the capabilities of the high-resolution stereo imaging system for the extraction of features (topographic map) and generation of digital elevation model (DEM). This study uses high-resolution GeoEye-1 stereo pair images. For qualitative assessment, topographic features were extracted by digitizing the georeferenced stereo pair images and overlapped to form a DEM and orthophoto within the Summit Evolution software. Georeferencing was carried out using the existing Ground Control Points (GCPs) and Check Points (CPs) established using Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) technique. For quantitative assessment, a total of 7 existing GCPs were considered suitable for use and 5 CPs were established as a check for accuracy of the output. The accuracy of the CPs was evaluated by computing Root Mean Square Error (RMSE). The average horizontal RMSE for the left image and right image were 1.17150098m and 0.819334672m respectively and the average vertical RMSE was 1.605898565m. Additionally, the standard deviation for elevation data from generated DEM using photogrammetric technique and existing elevation data of RTK-GNSS technique were computed, which resulted in 15.91286667 and 16.59529228 respectively. The accuracy was evaluated by comparing the results of the data extracted using stereo satellite images and those extracted from ground measurement techniques (Total station and RTK-GNSS). The comparative analysis showed that stereo pair images provide a viable alternative to other ground mapping techniques providing results within the acceptable range. Based on error estimation and analysis, it concludes that, if strict photogrammetric processing model and ground control points are employed, high-resolution satellite imagery can be used for the accurate generation and update of the topographic maps, DEM and digital orthophotos, which can be further be used as a reference for comparative analysis applications.

Dynamic monitoring of an offshore jacket platform based on RTK-GNSS measurement by CF-CEEMDAN method

In order to quantitatively analyze the roughness of the bench floor during open-pit mine blasting, this study proposes a real-time measuring method for the three-dimensional terrain of the bench floor during the excavation process. Real-time monitoring is conducted at the boundary and discrete internal points of the workbench floor during electric shovel operation, utilizing real-time kinematic global navigation satellite system (RTK-GNSS) positioning technology. An improved convex hull algorithm is introduced to automatically extract the optimal boundary of discrete point clouds based on their spatial distribution characteristics. This study establishes a digital elevation model (DEM) using five interpolation algorithms for 3D terrain visualization simulation. Through cross-validation, a comparative analysis of the DEM accuracy, the simulation results of the ordinary kriging interpolation algorithm were found to be optimized. The optimized interpolation algorithm is applied to simulate the 3D terrain in the Dexing open-pit copper mine, and the relevant terrain parameters were calculated. This dataset can serve as a precise foundation for the real-time path planning of elevation blasting design and ground leveling operations.

This study presents an automated workflow for delineating research plot boundaries and creating multiple polygon shape files with unique identifiers for overlaying drone multi-spectral MS imagery with centimetre-level accuracy for obtaining zonal statistics. The study used a high-accuracy real-time kinematic global navigation satellite System (RTK-GNSS) mounted on a planter, georeferenced unmanned aerial vehicle (UAV) imagery, and automated workflows that converted RTK-GNSS points into plot polygons, generated shapefiles, comma-separated values (CSV) files, and maps for downstream analysis. The method enabled efficient zonal statistics extraction with minimal manual adjustments and maintained consistent alignment across temporal datasets. This automated pipeline provided streamlined data processing, reduced labour, enhanced reproducibility, and a scalable open-source approach for high-throughput phenotyping and statistical analysis of breeding trials.

Better methods are needed for mapping agricultural drainage pipe systems. Prior research on small test plots indicates that ground penetrating radar (GPR) is oftentimes capable of detecting buried drainage pipes; however, the feasibility of employing this geophysical technique in larger field areas has not been adequately evaluated. Ground penetrating radar integrated with a Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) may be an effective and efficient means of mapping drain lines within agricultural fields. Therefore, GPR-RTK/GNSS was tested in three agricultural settings; with Site 1 and Site 2 located in Beltsville, MD, USA and Site 3 near Columbus, OH, USA. Soils at the three sites ranged from silty clay loam to loamy sand. A GPR unit with 250 MHz antennas was used to detect drainage pipes, and at Sites 1 and 2, a physical GNSS base station was utilized, while a virtual base station was employed at Site 3. The GPR-RTK/GNSS configurations used in this study delineated a complex rectangular drainage pipe system at Site 1, with one set of drainage pipes oriented southwest-northeast and a second oriented southeast-northwest. At Site 2, a herringbone drain line pattern was outlined, and at Site 3, random drain lines were found. When integrated with RTK/GNSS, spiral or serpentine GPR transects (or spiral/serpentine segments of a GPR transects) were utilized to provide insight on drain line directional trends. Consequently, given suitable field conditions, GPR integrated with RTK/GNSS can be a valuable tool for farmers and drainage contractors needing to map subsurface drainage systems.

Coastal erosion poses a significant risk to India’s 11,098.81 km shoreline, particularly in regions where coastal economies rely heavily on fishing. Karaikal, Puducherry, has experienced erosion along 37% of its coastline (NCCR, 2018), underscoring the need for robust sediment volume and shoreline change assessments. This study quantifies sediment dynamics by integrating Unmanned Aerial Vehicle (UAV) photogrammetry with Real-Time Kinematic Global Navigation Satellite System (RTK GNSS) surveys across TR Pattinam and Vanjiur beaches. Shoreline positions were mapped using RTK GNSS by delineating the centre line between high-water (HWL) and low-water (LWL) marks, while UAV-derived edges were extracted via the Canny detection algorithm. UAV flights were conducted at 50 m altitude with 85% image overlap. The Canny method achieved an average SSIM of ~0.8 and positional accuracy within one pixel.Sediment volumes were estimated by generating UAV-based digital elevation models (DEMs) using Structure from Motion and comparing them with centimetre-accurate GNSS transects spaced at 30 m intervals. Elevation differences were integrated alongshore to compute volumetric changes, with UAV elevations cross-checked against GNSS profiles for bias assessment. Results indicate strong agreement between UAV and GNSS datasets, demonstrating the reliability of this integrate.Key words: Shoreline change, Erosion, GNSS, Edge detection ,Sediment volume

Autonomous landing system is an important part of unmanned aerial vehicle (UAV) autonomous flight. Large fixed‐wing UAVs often consume more onboard space and computing resources. With the improvement of system integration and the reduction of high‐performance global navigation satellite system (GNSS) costs, real‐time kinematic (RTK) GNSS has been widely used in small‐scale and low‐cost fixed‐wing UAVs. This article uses less onboard hardware and ground facility requirements to design an autonomous taking off and landing system suitable for general fixed‐wing UAVs. A modified gradient descent attitude estimator, based on microelectromechanical system (MEMS) inertial measurement unit (IMU) and RTK GNSS, and a guidance method based on a tracking differentiator for using in a high dynamic environment are proposed, and circular path following and vertical transient simulations are performed. The simulation results show that this method can improve attitude estimation accuracy, motion control accuracy, and altitudinal transient quality. Finally, the actual flight test verifies its feasibility in the actual environment. The flight test results prove that it is feasible to implement the autonomous landing process of fixed‐wing UAV with the proposed system and method.

This study evaluated the applicability of the DJI Phantom4 RTK Unmanned Aerial Vehicle (UAV) with a built-in Real Time Kinematic-Global Navigation Satellite System (RTK-GNSS) for field management. The accuracies of the positional data on the x-, y-, and z-axes were 78, 73, and 292 mm, respectively, on average. The precisions of the positional data were 134, 171, and 89 mm, respectively, on average. The labor-saving effect of the RTK-UAV was 81% compared to the conventional method. RTK-UAV estimates of snow depth distribution corresponded with actual measured values, although there was an average error of 76 mm.

Commercial Global Positioning System (GPS) receivers, such as the ones in cell phones, usually can provide meter‐level accuracy in the best case. However, this is not enough for autonomous vehicles, which require centimeter‐level localization. One technology to achieve centimeter accuracy is the real‐time kinematic (RTK) Global Navigation Satellite System (GNSS). This chapter introduces localization with RTK GNSS and its role in an autonomous driving technology stack. It provides an overview of GNSS technologies and presents a step‐to‐step detailed guide on setting up one's own RTK GNSS system for high‐precision localization. The chapter discusses RTK‐GNSS architecture and demonstrates how to setup a cloud server to broadcast base station correction signal, or the Radio Technical Commission for Maritime Services (RTCM) signal. When a cloud server is not a good option, it also demonstrates how to set up FreeWave radio to broadcast RTCM signals.

This paper proposes a method for generating highly accurate point cloud maps of orchards using an unmanned aerial vehicle (UAV) equipped with light detection and ranging (LiDAR). The point cloud captured by the UAV-LiDAR was converted to a geographic coordinate system using a global navigation satellite system / inertial measurement unit (GNSS/IMU). The converted point cloud is then aligned with the simultaneous localization and mapping (SLAM) technique. As a result, a 3D model of an orchard is generated in a low-cost and easy-to-use manner for pesticide application with precision. The method of direct point cloud alignment with real-time kinematic-global navigation satellite system (RTK-GNSS) had a root mean square error (RMSE) of 42 cm between the predicted and true crop height values, primarily due to the effects of GNSS multipath and vibration of automated vehicles. Contrastingly, our method demonstrated better results, with RMSE of 5.43 cm and 2.14 cm in the vertical and horizontal axes, respectively. The proposed method for predicting crop location successfully achieved the required accuracy of less than 1 m with errors not exceeding 30 cm in the geographic coordinate system.

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

Research on the forest structure classification is essential, as it plays an important role in assessing the vitality and diversity of vegetation. However, classifying forest structure involves in situ surveying, which requires considerable time and money, and cannot be conducted directly in some instances; also, the update cycle of the classification data is very late. To overcome these drawbacks, feasibility studies on mapping the forest vertical structure from aerial images using machine learning techniques were conducted. In this study, we investigated (1) the performance improvement of the forest structure classification, using a high-resolution LiDAR-derived digital surface model (DSM) acquired from an unmanned aerial vehicle (UAV) platform and (2) the performance comparison of results obtained from the single-seasonal and two-seasonal data, using random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). For the performance comparison, the UAV optic and LiDAR data were divided into three cases: (1) only used autumn data, (2) only used winter data, and (3) used both autumn and winter data. From the results, the best model was XGBoost, and the F1 scores achieved using this method were approximately 0.92 in the autumn and winter cases. A remarkable improvement was achieved when both two-seasonal images were used. The F1 score improved by 35.3% from 0.68 to 0.92. This implies that (1) the seasonal variation in the forest vertical structure can be more important than the spatial resolution, and (2) the classification performance achieved from the two-seasonal UAV optic images and LiDAR-derived DSMs can reach 0.9 with the application of an optimal machine learning approach.

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

In this study, we generated Digital Elevation Model (DEM) from Unmanned Aerial Vehicle (UAV) to confirm the suitability of UAV to the tidal flat study. The DEMs were generated from aerial triangulation method using rotary-wing UAV. For the accurate generation of mosaic images and DEM, the distorted images occurred by interior and exterior orientation were corrected using camera calibration. In addition, we set up a Ground Control Points (GCPs) in order to correct of the UAV position error. Therefore, the DEM was obtained with geometric error less than 30 cm. The height of generated DEM by UAV was compared with the levelled elevation by RTK-GPS and TanDEM-X radar satellite DEM. From this study, we could confirm that accurate DEM of the tidal flat can be generated using UAV and these detailed spatial information about tidal flat will be widely used for tidal flat management.