Earth & Space Science

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https://hdl.handle.net/10315/27626

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Open Access
Resident Space Object Tracking for Space Situational Awareness
(2024-11-07) Kunalakantha, Perushan; Lee, Regina S. K.
This research presents several contributions aimed at improving the current state of Space Situational Awareness (SSA) using optical imagery. The first component involved the development of an image capture algorithm which was successfully used to acquire over 90000 optical images from the stratosphere, with hundreds of visually verified Resident Space Objects (RSOs). The second component involved the development of a novel RSO tracking algorithm which was able to detect 87% of the RSOs in an 878-image dataset at least once. The third component proposed an automated annotation framework and corresponding four-tool annotation suite to develop a 500-image dataset to train and test another RSO tracking algorithm. The final component involved the demonstration of a dual-purpose payload, performing RSO detection alongside an existing Attitude Determination (AD) algorithm which detected 11 unique RSOs in real-time during another stratospheric balloon mission.
Open Access
New Generation Stochastic Data-driven Calibration of the Accelerometers and Modelling of the Non-gravitational Accelerations in GRACE missions
(2024-11-07) Tzamali, Evangelia Myrto; Pagiatakis, Spiros
The Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-On (GRACE-FO), have significantly advanced our understanding of Earth's gravity field and its temporal changes by measuring subtle gravity variations caused by mass redistribution on and beneath Earth's surface. A key component of these missions is the use of geodetic-quality GPS receivers for precise orbit determination and 3D accelerometers, which measure non-gravitational forces like atmospheric drag and solar radiation pressure. However, challenges in using accelerometers must be addressed to ensure data and modeling accuracy. A primary challenge is the calibration of accelerometers, as inaccuracies can lead to significant errors in gravity field modeling and thermospheric density estimates of up to 10%, depending on the levels of solar activity. This study introduces a calibration method using matched filter techniques applied to accelerometer measurements and GPS-derived accelerations. The estimated calibration parameters show high stability with values close to 1, as expected from the ultra-sensitive electrostatic space accelerometers and proposed by other studies. Error assessment of GRACE and future mass change missions must consider accelerometer measurements as stochastic quantities with realistic error estimates. This involves identifying and quantifying measurement errors stemming from instrument noise, thermal effects, correlations and transient effects. This study generates a stochastic (weighted) 1B accelerometer dataset to investigate systematic errors from various sources like geomagnetic storms, temperature changes, and terminator crossings. Contrastingly to the official accuracy which is 〖0.1 nm/s〗^2 for the along-track and radial direction and 〖1 nm/s〗^2 in the cross-track, the proposed dataset being clean from spikes has an accuracy of 〖 10^(-3) nm/s〗^2. Accurate modeling of non-gravitational forces is essential for isolating gravitational signals and estimating drag, which is crucial for determining thermospheric densities. This study proposes a data-driven model for the dominant forces of solar and thermal radiation pressure and drag on the GRACE-C satellite, using the new 1B dataset produced in this study. A comprehensive analysis of residuals which are up to 〖2 nm/s〗^2 in the along-track, 〖0.5 nm/s〗^2 in the cross-track and up to 〖5 nm/s〗^2 in the radial direction, reveals disturbances with latitudinal dependency, especially during high geomagnetic activity.
Open Access
On the Impact of Land Use & Landcover Change on GHG Emissions using Advanced Remote Sensing Technology
(2024-11-07) Ituen, Ima-Obong; Hu, Baoxin
There is an interesting effect of the changing climate: it has been observed in Northern Ontario that the warming weather has lengthened the growing season. Consequently, there has been increased land use conversion from natural forests to agriculture in Northern Ontario to capitalise on the new economic opportunities resulting from longer growing seasons. However, the long-term effects of land conversion on soil carbon, nutrients, and greenhouse gas emissions (GHGs) are unknown. This research study leveraged advanced remote sensing technology to detect changes in land cover and land use. A major problem addressed in this work was the lack of landcover classification data which is current, extends over the entire study area, and is of high spatial and temporal resolution with which to analyse the study region properly. This study proposed a new change detection framework which uses the best available data for landcover classification and disturbance information, even in the event of scarce input or training data. Thus, an analyst can progressively adapt the available data sources to present a more accurate assessment of the disturbances. More accurate input data for the carbon models was shown in this study to decrease carbon and GHG estimates by between 15% and 32%. The study concludes with tools that an everyday user can utilize to examine their region. The applications can run for free on their devices, permitting users to discover the landcover changes in their region, and estimate forest loss or gain. The applications could serve as a bridge to discussions concerning the areas that are best suited to concentrate carbon sequestration efforts.
Open Access
Deep Learning-Enhanced Autonomous Aerial and Ground Robotics Using UWB and Lidar in GNSS-Denied Environments
(2024-11-07) Arjmandi, Zahra; Sohn, Gunho; Armenakis, Costas
Over the last decade, advancements in Unmanned Aerial Vehicle (UAV) technology and Artificial Intelligence (AI) have led to significant improvements in navigation and positioning, yet widespread adoption remains limited due to challenges in integrating various technologies and ensuring reliable real-time data processing. This thesis addresses these issues by developing a comprehensive framework that merges advanced data collection platforms, deep learning algorithms, and novel fusion methods to enhance UAV positioning accuracy and reliability. A central contribution of this research is the creation of the Q-Drone Ultra-Wideband (UWB) benchmark dataset. This dataset, generated from a UAV equipped with five UWB sensors across five diverse environments (indoor, outdoor, and semi-outdoor) over a distance of 4 km, provides a standardized benchmark for testing UAV positioning systems. It enables researchers to develop and validate algorithms under varied conditions, supporting advancements in UAV navigation and positioning research. The thesis also introduces an incremental smoothing approach, integrating high-rate and low-rate UWB measurements with inertial data within a unified pose graph framework. This method, using an "add-after-eliminating" strategy, reduces Mean Absolute Error (MAE) by 0.2 meters compared to baseline multilateration methods and achieves a 0.3-meter MAE reduction compared to two-factor pose graph methods. Further, the DeepCovPG framework is developed, combining a Variational Autoencoder (VAE) with a Long Short-Term Memory (LSTM) network to predict and incorporate dynamic covariances into the pose graph. This approach results in a 48% reduction in Root Mean Square Error (RMSE) and a 51% reduction in Range Covariance RMSE, with notable improvements of 0.41 meters in tunnels and 0.23 meters in fields. The framework also achieves a 26% reduction in multilateration RMSE and a 32% reduction in multilateration Covariance RMSE. Additionally, the thesis explores Light Detection and Ranging (LiDAR)-based positioning and proposes the INAF fusion method. This method dynamically selects relevant information from geometric and AI-based odometry techniques, improving accuracy by 3.90% over direct fusion methods and 0.25% over attention-based fusion methods. The INAF fusion method demonstrates enhanced adaptability to various driving conditions, improving accuracy in both straight and dynamic environments.
Open Access
Sub-metre Positioning with Smartphone Global Navigation Satellite System Measurements in User Environments
(2024-11-07) Hu, Jiahuan; Bisnath, Sunil
The ubiquity of smartphones catalyzes various smartphone-based Internet of Things (IoT) applications, among which smartphone positioning that uses Global Navigation Satellite System (GNSS) observations to provide user position plays an important role. The noisy smartphone GNSS measurements from signal obstructed environments and embedded antennas prevent users from obtaining reliable and accurate positioning solutions. With a correct understanding of the measurement characteristics and advanced positioning techniques such as Real-Time Kinematic (RTK), Precise Point Positioning (PPP), and PPP-RTK, this dissertation aims to achieve sub-metre- or decimetre-level positioning accuracy with smartphone GNSS measurements in realistic environments. In this dissertation, a novel smartphone range error derivation method is proposed, as well as an improved cycle slip (CS) detection method. New stochastic modelling and measurement outlier detection methods are developed. And smartphone ambiguity resolution (AR) is conducted with a newly proposed candidate selecting strategy. Smartphone measurements exhibit significantly higher noise levels as compared to those from geodetic hardware, in which the range errors are more correlated with the carrier-to-noise density ratio (C/N0). Moreover, range errors are environment-dependent, and they behave differently when, e.g., the smartphone is mounted on the vehicle roof versus the dashboard. Considering that the conventional Doppler CS detection method is sometimes not applicable due to inconsistent time-differenced carrier phase (TDCP) and Doppler clocks, a novel modified Doppler method is proposed and validated with simulated cycle slips. With proposed Doppler cycle slip detection method, all simulated cycle slip combinations are detected. In terms of quality control methods, the improved stochastic model and novel outlier detection method outperform the conventional C/N0-based weighting and a fixed residual rejection threshold, showing a percentage improvement from 38% to 54% for PPP horizontal positioning errors within 1 metre. A novel smartphone RTK wide-lane (WL) partial AR strategy is proposed, for which the static results show an improvement of 83% in horizontal when WL AR is conducted compared to the underlying float solution, and the positioning accuracies can reach 6.8, 2.9, and 11.5 cm in E, N, and U, respectively. The proposed algorithm is validated with kinematic datasets collected in real-life environments, in which the time series of horizontal positioning errors exhibit less variation than the float solutions, represented by smaller positioning errors. Moreover, with fixing WL ambiguities, the horizontal positioning performance has improvements ranging from several centimetres to up to 8 decimetres depending on the related environments. the biggest improvement of 0.79 m is noticed for 95th percentile horizontal positioning errors under suburban environments. Improvements on the above-mentioned aspects show potential on achieving decimetre-level positioning performance with smartphone embedded antennas in realistic environments.
Open Access
Analyzing Mars' Polar Anomalies: Computer Vision Techniques for Seasonal Changes and Polar Dynamics
(2024-11-07) Acharya, Pruthviraj Jaydipsinh; Smith, Isaac B.
This dissertation offers a detailed analysis of seasonal ice cap dynamics and surface anomalies on Mars, utilizing autonomous tracking techniques with polar stereographic images from the Mars Color Imager (MARCI) spanning multiple Mars Years (MY). This dissertation investigates the recession of the Northern Polar Seasonal Cap (NPSC) from MY 29 to MY 35. Employing Python for automation, this analysis tracks the recession with high temporal fidelity, uncovering intraseasonal variability in the recession rate in addition to significant interannual variability. This variability is coincident with specific events influenced by off-polar winds and Global Dust Storm (GDS) events. The chapter notably examines the divergent effects of GDS events on the size of the NPSC and its recession rates, emphasizing the influence of storm timing and duration. Additionally, the dissertation explores the recession of the Southern Polar Seasonal Cap (SPSC) from MY 28 to MY 31, characterized by significant discontinuity and variability in recession rates. This study highlights the impacts of the GDS events on SPSC dynamics, demonstrating accelerated sublimation rates and a reduction in cap size after the storm onset. Finally, the dissertation delves into seasonal phenomena known as Cold and Bright Anomalies (CABAs) and Warm and Dark Anomalies (WADAs) on the North Polar Residual Cap (NPRC). Extensive analysis from MY 29 to MY 35 examines the evolution of temperature and albedo at anomaly sites, revealing a strong correlation with local topography and atmospheric conditions, including katabatic winds and transient eddies. Collectively, this dissertation provides a nuanced understanding of Martian polar dynamics, offering insights into the interactions between atmospheric phenomena and surface conditions. The adoption of automated tracking technologies significantly enhances the precision and efficiency of these analyses, contributing to our broader understanding of Martian climatology and its seasonal cycles.
Open Access
Examining Particulate Matter Emissions from Vehicular Traffic in an Urban Environment
(2024-11-07) Reet, Rhythm; Gordon, Mark
This research investigates the complexities of particulate matter (PM) emissions in urban environments, with a focus on non-exhaust sources like brake and tire wear, primarily caused by vehicular traffic in an urban area. By examining the variance in PM size, specifically particles sizes between 50 nm and 1000 nm, the study assesses the impact of traffic volume and patterns on PM emissions in Toronto, Ontario. The approach used is based on vertical fluxes measured with eddy-covariance, counting vehicles and estimation of the footprint. In this study variations in vehicle traffic were linked to fluctuations in particle concentrations and turbulent fluxes. Higher vehicle rates in weekday evenings did not correspond with increased particle numbers, whereas weekday mornings experienced higher concentrations, possibly due to overnight pollutant accumulation and the rising atmospheric boundary layer. Larger particles were more likely to originate from background sources than the road itself, particularly in areas affected by stop-and-go traffic. Over a specific week in March, however, road emissions significantly contributed to particle concentrations, a deviation from the norm. Weekends presented consistent particle deposition, suggesting that roads act more as sinks than sources, especially during periods with fewer vehicles. The complexities of urban particulate dynamics were highlighted, indicating that larger particles are prone to settle on the road surface, and changes in traffic patterns can transform typical emission sources into particle sinks. This study lays the groundwork for future research, emphasizing the need for detailed traffic data to better understand emission sources and implications for urban air quality and public health.
Open Access
Modeling trace elements over Athabasca oil sands region in Alberta, Canada using WRF-Chem
(2024-11-07) Hao, Jingliang; Chen, Yongsheng
The Athabasca Oil Sands Region (AOSR) in northern Alberta, Canada serves as a significant source of trace elements. In this study, the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is modified to predict the transport and deposition of eight elements (Al, Ca, Fe, K, Mn, Si, Ti, and Zn) in the AOSR in 2016 and 2017. The model has a good performance on the air temperature, wind at surface, and precipitation. The model-measurement percentage differences in the annual concentrations of the eight elements at three sites are in the range of -6.9% to 76% at AMS1, -48% to 72% at AMS17, and -165% to 5.8% at AMS18. Modeled annual concentrations and atmospheric deposition of individual element range from 0.016 to 2.67 µg m-3 and 2.62 to 385 mg m-2yr-1 in the central region of the oil sands industry, respectively. Modeled element concentrations and deposition show a rapid decline by around three orders of magnitude from the central region to the remote region in a distance of around 150 km. The modeled total concentrations of the eight elements at three sites are overestimated by 82% in the cold season but underestimated by 38% in the warm season. In the first sensitivity test, the annual emission is reallocated to 30% in the cold season and 70% in the warm season. This leads to a reduction in the bias for the modeled total concentrations of eight elements from 45% to 13% in the cold season and from 45% to 24% in the warm season. In the second sensitivity test, the original dry&wet deposition schemes in WRF-Chem are replaced by other schemes. The modeled annual total dry and wet depositions of all elements are decreased by 56% and increased by 33%, respectively. The total dry&wet deposition is decreased by 31%.
Open Access
Design and Additive Manufacture of a High-speed Piezoelectrically Actuated Compliant Mesoscale Parallel Robot
(2024-11-07) Tabak, Ariel; Orszulik, Ryan
This work presents the design of an additively manufactured mesoscale parallel robot that is actuated by piezoelectric bimorphs through a compliant transmission mechanism. The piezoelectric bimorph actuators are modeled via finite element and experimentally tested to verify their performance. A transmission mechanism is designed using finite element analysis to determine the critical parameters for the conversion of the piezoelectric actuators small linear displacements to large rotational motion. Multiple transmissions are fabricated using additive manufacturing and are tested experimentally. The transmission mechanism is then used to create a six-bar parallel robot that is driven by the piezoelectric bimorphs, which is simulated and tested experimentally. The planar parallel robot is 37.88 by 20.54 millimeters in size and has a workspace of 14.36 by 8.66 millimeters for a total area of 65.6 millimeters squared. The robot is capable of following trajectories within the workspace at a frequency of up to 10 Hz.
Open Access
Image Processing Techniques for Space Situational Awareness
(2024-07-22) Stewart, Michael Ian; Lee, Regina
With a renewed interest in space, it is paramount to understand the space environment; an act commonly referred to as Space Situational Awareness (SSA). Tracking and performing measurements on nearby orbiting objects; known as RSOs helps us better understand SSA. Image processing is a significant step in improving SSA capabilities, aiding in space object detection, tracking, and characterization. This thesis addresses gaps in RSO image processing, with a focus placed on developing specialized pipelines for the production of photometric measurements of Geosynchronous Equatorial Orbit (GEO) RSOs and the James Webb Space Telescope (JWST). Despite challenges like the unique Earth-Sun L2 Orbit of JWST and high noise issues, this study successfully produced calibrated photometric light curves of three targeted RSOs, advancing our understanding of photometric SSA methodologies.
Open Access
BIM based Energy Consumption Estimation using Data-driven model
(2024-07-18) Kiavarz Moghaddam, Hamid; Jadidi, Mojgan
Building Information Modeling (BIM) is undergoing rapid technological evolution in the building construction industry. Recently, employing BIM as a building 3D digital model in Building Energy Consumption Estimation (BECE) has gained momentum because of the enriched geometric and semantic information. Indeed, indoor BECE notably depends on the semantics, geometry (building elements and shapes), and topology information of the building's elements to recognize the spaces in a building with high energy demand. However, despite extensive studies on applying the BIM and Industry Foundation Classes (IFC) as an open standard data model for BIM in BECE analysis, employing the full potential of the BIM remains poor due to its data model complexity and incompatibility with BECE data-driven algorithms. There is a significant lack of building energy modeling in using the detailed geometry, semantic, and 3D topology information in BECE data-driven models. The objective of this dissertation is to develop an innovative and comprehensive framework called space-based precise building energy consumption estimation using BIM. In this research, a framework is developed to convert the IFC model into a space-based graph, including the geometry, semantic, and topology information on the proposed graph nodes and edges. The graph is compatible with the machine learning algorithm. A graph-based classification algorithm is suggested in this research to find critical spaces in the building for energy consumption. This research proposed a prescriptive model by integrating building energy simulation with optimization techniques, using BIM data and a Genetic Algorithm (GA) to develop a prescriptive model for indoor building design. The study focuses on space-based BECE analysis, leveraging BIM interoperability to recommend optimal solutions. The proposed model employs the value engineering method to balance energy consumption, functionality, and cost, providing engineers and designers with insights to optimize building performance effectively. This approach enhances energy efficiency and offers substantial design optimization solutions, bridging the gap between energy prediction and practical application in the architectural, engineering, and construction (AEC) industry. The outcomes of this study are conducive to contemporary data-driven models in BIM and indoor BECE analysis. This provides a comprehensive perspective on both present and prospective requirements for BIM in the estimation of building energy consumption. The study integrates various sectors, including architecture, construction, machine learning, ad 3D geospatial analysis, aiming to derive comprehensive and optimal solutions. Furthermore, it underscores the necessity for future multidisciplinary research by unfolding existing gaps and limitations.
Open Access
Neural Network Based Sliding Mode Control for Robotic Manipulator
(2024-07-18) Gomes Carmo, Ingredy Gabriela; Shan, Jinjun
Robotic manipulators are used in many applications. However, robotic arms are complex systems due to external disturbances, perturbations, and their coupled non-linear dynamics. This thesis aims to propose a robust control strategy for autonomous robotic manipulation. First, the trajectory tracking problem was introduced and an approach to overcome this issue using a sliding mode controller combined with a neural network is proposed. Then, the proposed approach is compared to classical and modern control methods including controllers from the literature to demonstrate the performance of the proposed controller. The proposed controller was then integrated with a grasp detection algorithm for an autonomous manipulation application. Simulations and hardware experiments were conducted to validate the performance of the proposed method.
Open Access
A Study on Depth Estimation and Digital Terrain Model Reconstruction for Mobile Mapping Systems
(2024-07-18) Naeini, Amin Alizadeh; Sohn, Gunho
Mobile Mapping Systems (MMSs) are advanced platforms for collecting precise geospatial data. They use technologies like LiDAR and digital imaging to gather detailed environmental information. MMSs have two main components: georeferencing and mapping. Georeferencing aligns digital data with coordinate systems using Global Navigation Satellite Systems (GNSS) for high accuracy. In areas where GNSS is unavailable, Simultaneous Localization and Mapping (SLAM) technology is used to map unknown environments and track MMS locations. The accuracy and density of depth maps are crucial for SLAM performance, affecting the system's ability to create maps and track positions. This dissertation addresses depth estimation challenges in MMSs by introducing the Double-stage Adaptive Refinement Scheme (DARS). DARS is designed to improve depth estimation in dynamic environments and can be integrated with pre-trained networks. It can also be extended to Panoramic DARS (PanoDARS) for SLAM applications using panoramic images, improving the HDPV-SLAM system's performance by addressing LiDAR depth sparsity and depth association issues. The thesis also explores reconstructing Digital Terrain Models (DTMs) from Digital Surface Models (DSMs), essential for accurate mapping within MMSs. DTMs provide elevation data for creating geospatial products like orthophotos, topographic maps, and 3D urban models. To achieve precise DTM reconstruction, a geospatially induced autoencoder called SB-SUBNET is proposed. This autoencoder uses two geospatial inductive biases that leverage the relationship DTM = DSM - nDSM. The first bias reconstructs the DTM by subtracting the network's output (nDSM) from its input (DSM). The second bias is a subtractive skip connection that integrates this geospatial information directly into the network, enhancing performance using inherent geospatial relationships in the data. While SB-SUBNET shows promising results, it alters all DSM values, including terrain values, affecting DTM accuracy. To address this, a new multi-task learning approach named DB-SUBNET was developed. This approach segments non-ground points and performs regression specifically for these points while preserving ground points. This method improves DTM reconstruction accuracy and ensures the resulting DTMs provide a reliable foundation for high-precision geospatial products. Enhanced DTM reconstruction is vital for creating detailed and accurate maps, supporting urban planning, infrastructure development, environmental monitoring, and disaster management.
Open Access
Intelligent Decision-Making for Autonomous Driving in Dynamic and Interactive Environments
(2024-07-18) Yuan, Mingfeng; Shan, Jinjun
The decision-making module is crucial for safe and efficient driving in autonomous vehicles (AVs). However, AVs face significant challenges in coexisting with human road users, making fast and optimal driving decisions, and operating in unknown traffic environments with only incomplete information. Unsignalized intersection and lane-changing scenarios are particularly representative of such challenges, which involves complex dynamic interactions. AVs need to assess and predict the driving preferences of nearby vehicles to optimize and adaptively adjust their own driving policies while considering uncertainties arising from incomplete observations. This dissertation investigates game-theoretic and learning-based methods to address these challenges. In this dissertation, a novel approach for integrating game-theoretic decision-making with deep reinforcement learning (DRL) is proposed to enable AVs to navigate unsignalized intersections using an onboard sensor. The game model predicts the surrounding vehicles' reactions to the ego-vehicle's movements without relying on coordination or vehicle-to-vehicle communication. The proposed algorithm employs cognitive hierarchy reasoning and a DRL algorithm to achieve a self-play training mode for getting a near-optimal driving policy in a realistic simulator before transferring to the real world. Second, this dissertation introduces a practical algorithm based on DRL for enhancing lane-changing decision-making, addressing low sample efficiency in DRL, and improving the generalization capability in Imitation Learning (IL). To narrow the gap between simulation and reality (sim-to-real gap), a digital twin platform is developed for simulating LiDAR sensing, model training, and algorithm evaluations. To tackle multi-objective optimization and imbalanced data concerns, a hierarchical decision-making framework is proposed, breaking down the complex decision-making problem into subtasks for improved convergence of driving policies. Third, a robust adaptive game-theoretic decision-making algorithm by utilizing receding horizon optimization and level-k game theory is presented. To reduce the potential safety risk arising from an inaccurate motion prediction of surrounding vehicles, the proposed approach can estimate driving aggressiveness of surrounding vehicles online. Then, the generated trustworthiness is used to formulate a safe, efficient, and robust adaptive driving policy. Additionally, a switching interaction graph is introduced into the adaptive level-k framework to reduce the computational complexity. Validation on both a high-fidelity simulator and hardware confirms the feasibility, effectiveness, and real-time performance of the proposed methods. Overall, this dissertation contributes novel approaches to address decision-making challenges in AVs. The integration of game theory and model-based/model-free optimization showcases the potential for improving safety and efficiency of AVs' operation in dynamic traffic environments.
Open Access
Smartphone precise positioning in urban environments using internal GNSS and IMU sensors
(2024-07-18) Yi, Ding; Bisnath, Sunil
The high level of signal noise and multipath effect errors cannot be neglected when it comes to smartphone-grade GNSS receivers and antennas, and along with frequent carrier-phase measurement discontinuities and losses, pose a challenge for advanced GNSS positioning techniques. In response to these challenges, this work is dedicated to addressing these smartphone positioning obstacles and aims to provide accurate, continuous, and resilient Positioning, Navigation, and Timing (PNT) solutions with a developed smartphone processing software. This research commences with the investigation of smartphone Precise Point Positioning (PPP) performance with external ionospheric constraints. To effectively utilize all satellite measurements in the absence of phase measurements, this work proposes a pseudorange-only measurement enhanced PPP method with single- and dual-frequency combinations. Moreover, to overcome the limitations of both PPP and Real-Time Kinematic (RTK) techniques, a novel PPP/RTK hybridization algorithm with smartphone Inertial Measurement Unit (IMU) integration is proposed. Additionally, this research explores the utilization of the available Galileo HAS corrections for smartphone navigation. Validated through a series of vehicle experiments conducted in realistic driving environments, the results demonstrate a substantial improvement in reducing horizontal rms, improving it from approximately 10 metres to less than 1.5 metres compared to traditional Single Point Positioning (SPP) solutions, showing a noteworthy improvement in smartphone-based mobile positioning for mass-market applications.
Open Access
Architectural Heritage Exploration and Visualization Using Interactive and Immersive Technologies
(2024-07-18) Albourae, Abdullah Taha; Armenakis, Costas
The Historic District of Jeddah (HDJ) of the Kingdom of Saudi Arabia, is facing many challenges to attain conservation and sustainability, due to natural disasters and the absence of appropriate digital recordings and documentation. Previous studies on world heritage conservation attempted to address these challenges while adopting the immersive and visual technologies separately or via merging one of them with another to conserve the heritage sites (e.g. BIM/GIS integration or generating GIS/BIM, integrating VR with BIM, or GIS). However, few of these studies handled the case of the historic heritage of KSA. This research aims to fill this gap by utilizing all these technologies together to create an immersive visualization system for heritage sites. The researcher proposes a novel method using immersive technologies (VR, AR, MR) and visualization methods (3D modeling) combined with Heritage Building Information Modelling (HBIM) and Heritage Geographic Information Systems (HGIS) for comprehensive conservation of cultural heritage sites in Saudi Arabia. The proposed method involves three phases: a) HBIM and HGIS generation: This involves creating 3D architectural models and spatial data using industry-standard collection tools, b) HBIM and HGIS Integration: The data from both phases is merged into a single georeferenced system for unified analysis, and c) Interactive User Experience: An interactive user environment is developed using a game engine, allowing users to explore the 3D model and access historical information. This research offers a multi-disciplinary approach to cultural heritage conservation, combining innovative informatics with technology for documentation, communication, and user engagement. The findings will enable users to virtually explore the heritage sites and track changes over time. This immersive experience fosters a deeper understanding and appreciation of the architectural heritage. The proposed method can be applied to other cultural heritage sites globally. Future studies could explore using 3D viewers for information retrieval and potentially using CityGML, a rich 3D geospatial data format, for even more comprehensive HBIM-HGIS integration. This would significantly contribute to the advancement of world heritage conservation efforts.
Open Access
Design, development and verification of a UAV-based GNSS payload for reflectometry
(2024-07-18) Talebi, Sogand; Bisnath, Sunil B.
This thesis focuses on the design, development, and verification of a Global Navigation Satellite System-Reflectometry (GNSS-R) payload tailored for Unmanned Aerial Vehicles (UAVs) utilizing Commercial Off-The-Shelf (COTS) components. UAVs serve as versatile aerial platforms capable of operating at diverse altitudes, thereby offering varying levels of spatial resolution suitable for localized remote sensing applications. A customized GNSSR payload was created and tested in a comprehensive flight campaign over diverse terrains. The collected data underwent post-processing to generate reflectometry products, such as Delayed Doppler Maps (DDM) and Delayed Waveforms (DWF). Comparative analyses were conducted on GNSS-R reflections from different surfaces to confirm the payload’s reflectometry capabilities. The research results offer valuable insights into the practical applications of GNSS-R technology in remote sensing, emphasizing its potential viability for various applications in this field.
Open Access
Image Classification and Initial Orbit Determination of Resident Space Objects (RSO)
(2024-07-18) Vallecillo Baires, Andrea Maria; Lee, Regina
The importance of Space Situational Awareness (SSA) research is steadily growing due to continuous launches of new technologies, leading to increased congestion and potential collisions in Earth’s orbit. Optical imagers, based in both ground and space, serve as important tools for observing, detecting, and studying resident space objects (RSOs). Optical imagers capture vast amounts of data that can be used for different applications in SSA research. However, manually inspecting and classifying these images for specific purposes is a time-consuming task. Implementing an automated image classification can streamline the labelling process for optical databases to expedite SSA research. Another significant aspect of SSA research and RSO tracking using optical images involves reliable object identification and Orbit Determination (OD). Angles-only Initial Orbit Determination (IOD) methods are often employed as a starting point to optimize the OD process. These advancements play a pivotal role in enhancing and contributing to SSA research.
Open Access
Integrative and Multi-scale Deep Learning for 3D Point Cloud Transmission Corridor Scene Segmentation: Noise Filtering, Attention-Fused Feature Integration, and Panoptic Network
(2024-03-16) Jameela, Maryam; Sohn, Gunho
LiDAR technology plays a crucial role in mapping natural and built environments across various civic and military applications. It enables the acquisition of high-density 3D point clouds with pulse repetition frequencies ranging from 100Hz to 2MHz. However, the increased overlap with atmospheric points has posed challenges in noise filtering and 3D point cloud quality. This dissertation proposes the Noise Seeking Attention Network (NSANet), a novel solution integrating psychological theories of feature integration and attention engagement. NSANet achieves a 4.10% increase in F1-Score and a 7.30% improvement in recall by employing multiscale context, global physical priors, and local spatial attention for noise filtering, surpassing previous techniques. The study explores the relationship between vegetation height and plantation guidelines, identifying spatial layout consistency in utility layouts and transmission objects. This insight drives the development of three semantic analysis approaches: Semantic Utility Network (SUNet), Fusion-Semantic Utility Network (Fusion-SUNet), and Panoptic-Semantic Utility Network (Pan-SUNet). Encouraged by the performance improvements of SUNet and Fusion-SUNet, Pan-SUNet achieves outstanding results, boasting a 94% F1-Score for pylons, 99% for ground, vegetation, and powerlines, and demonstrating high precision in 3D object detection. Experiments conducted on Teledyne Optech's Galaxy T1000 dataset, which features diverse voltage transmission lines, validate the effectiveness of Pan-SUNet, particularly when combined with the RandLA baseline. Significant improvements are observed, including an increase from 80% to 85% in F1-Score for pylons, 98% to 99% for ground, 93% to 97% for powerline, 75% to 78.3% for other objects, 86% to 88% for buildings, while maintaining 98.2% for high vegetation and 93% for medium vegetation. The key contribution of this research is a significant advancement beyond basic object classification, as it not only identifies the class of an object but also distinguishes between different instances of the same class. This instance segmentation is critical for utility network modelling and simulation. The research emphasizes the importance of external cues such as contextual reasoning, spatial cognition, and physical priors for multiscale fusion in scene understanding systems. Moreover, the study's adaptability to integrate proposed contributions into existing networks enhances their overall performance, making them network-agnostic.
Open Access
Integration of Airborne Laser Scanner and Optical Data Using Image Tie Points and the Quasi-Log-Polar Analytical Fourier Mellin Transform
(2024-03-16) Baumgaertner , Michael S. P.; Armenakis,Costas; Wang, Jianguo
LiDAR and camera have complementary data characteristics. Their combined usage allows for achieving better accuracy and enhanced inference about the environment than through the isolated use of the data of one sensor only. Co-registration is the process of spatial alignment of data from different types of sensors. It enables cross-aided sensor- and data calibration and the combined usage of the data. Automatic co-registration of airborne optical imagery and LiDAR data from large-area surveys has not been addressed, unless the datasets come with metadata or some prior knowledge about the scenery or have been pre-processed and converted into data with similar density and structure. In this dissertation, an innovative co-registration concept that uses image tie points, projected into object space, and Lidar points, is proposed and researched. Since 3D tie points are generated automatically with centimeter-level precision within the photogrammetric block adjustment, this approach has the potential of providing high accuracy and full automation. However, it must address that large area photogrammetric tie points provide extremely low point density compared to laser point clouds, and they describe different aspects of the scanned surface, since tie points coincide with radiometric corner locations on the visual surface whereas laser points locate anywhere on surface objects and on the ground. This research provides the following contributions: Firstly, the use of 3D photogrammetric tie points for co-registration of large-format sensor data is proposed and analyzed. Secondly, new methods for coarse- and fine registration in space domain are presented and evaluated. Thirdly, five methods for co-registration in frequency domain are researched. Lastly, the novel Quasi Log-Polar Fourier Mellin Transform (QAFMT) method is suggested as tool for automatic coarse co-registration of large area datasets. The learnings and potential use for other applications are discussed in the end. The performance of the QAFMT and its competing methods is analyzed using one simulated and two real-world datasets. While existing algorithms estimated 45 of 144 co-registration parameter pairs correctly (31% success), the QAFMT yielded a 7% improvement (50/144 accurate pairs, 38% success).