Morphing and Sampling Network for Dense Point Cloud Completion

3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).Comment: 8pages, 7 figures, AAAI202

Distributed Load Balancing Algorithm in Wireless Networks

As communication networks scale up in size, complexity and demand, effective distribution of the traffic load throughout the network is a matter of great importance. Load balancing will enhance the network throughput and enables us to utilize both communication and energy resources more evenly through an efficient redistribution of traffic load across the network. This thesis provides an algorithm for balancing the traffic load in a general network setting. Unlike most of state-of-the-art algorithms in load balancing context, the proposed method is fully distributed, eliminating the need to collect information at a central node and thereby improving network reliability. The effective distribution of load is realized through solving a convex optimization problem where the p-norm of network load is minimized subject to network physical constraints. The optimization solution relies on the Alternating Direction Method of Multipliers (ADMM), which is a powerful tool for solving distributed convex optimization problems. A three-step ADMM-based iterative scheme is derived from suitably reformulated form of p-norm problem. The distributed implementation of the proposed algorithm is further elaborated by introducing a projection step and an initialization setup. The projection step involves an inner-loop iterative scheme to solve linear subproblems. In a distributed setting, each iteration step requires communication among all neighboring nodes. Due to high energy consumption of node-to-node communication, it is most appealing to devise a fast and computationally efficient iterative scheme which can converge to optimal solution within a desired accuracy by using as few iteration steps as possible. A fast convergence iterative scheme is presented which shows superior convergence performance compared to conventional methods. Inspired by fast propagation of waves in physical media, this iterative scheme is derived from partial differential equations for propagation of electrical voltages and currents in a transmission line. To perform these iterations, all nodes should have access to an acceleration parameter which relies on the network topology. The initialization stage is developed in order to overcome the last challenging obstacle toward achieving a fully distributed algorithm

BDS GNSS for Earth Observation

For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

Geo-Information Technology and Its Applications

Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

Aspects of Bayesian inference, classification and anomaly detection

The primary objective of this thesis is to develop rigorous Bayesian tools for common statistical challenges arising in modern science where there is a heightened demand for precise inference in the presence of large, known uncertainties. This thesis explores in detail two arenas where this manifests. The first is the development and testing of a unified Bayesian anomaly detection and classification framework (BADAC) which allows principled anomaly detection in the presence of measurement uncertainties, which are rarely incorporated into machine learning algorithms. BADAC deals with uncertainties by marginalising over the unknown, true value of the data. Using simulated data with Gaussian noise as an example, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. BADAC is therefore ideal where computational cost is not a limiting factor and statistical rigour is important. We discuss approximations to speed up BADAC, such as the use of Gaussian processes, and finally introduce a new metric, the Rank-Weighted Score (RWS), that is particularly suited to evaluating an algorithm's ability to detect anomalies. The second major exploration in this thesis presents methods for rigorous statistical inference in the presence of classification uncertainties and errors. Although this is explored specifically through supernova cosmology, the context is general. Supernova cosmology without spectra will be an important component of future surveys due to massive increases in data volumes in next-generation surveys such as from the Vera C. Rubin Observatory. This lack of supernova spectra results both in uncertainty in the redshifts and type of the supernova, which if ignored, leads to significantly biased estimates of cosmological parameters. We present a hierarchical Bayesian formalism, zBEAMS, which addresses this problem by marginalising over the unknown or uncertain supernova redshifts and types to produce unbiased cosmological estimates that are competitive with supernova data with fully spectroscopically confirmed redshifts. zBEAMS thus provides a unified treatment of both photometric redshifts, classification uncertainty and host galaxy misidentification, effectively correcting the inevitable contamination in the Hubble diagram with little or no loss of statistical power

Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

A robust solving strategy for the vehicle routing problem with multiple depots and multiple objectives

This document presents the development of a robust solving strategy for the Vehicle Routing Problem with Multiple Depots and Multiple Objectives (MO-MDVRP). The problem tackeled in this work is the problem to minimize the total cost and the load imbalance in vehicle routing plan for distribution of goods. This thesis presents a MILP mathematical model and a solution strategy based on a Hybrid Multi- Objective Scatter Search Algorithm. Several experiments using simulated instances were run proving that the proposed method is quite robust, this is shown in execution times (less than 4 minutes for an instance with 8 depots and 300 customers); also, the proposed method showed good results compared to the results found with the MILP model for small instances (up to 20 clients and 2 depots).MaestríaMagister en Ingeniería Industria

UAV photogrammetry ground control reductions using GNSS

Ph. D. Thesis.Unmanned aerial vehicles (UAVs) are now well-established as platforms for photogrammetric data acquisition. Their advantages, particularly over conventional manned aerial platforms, relate to their low cost, ease of use, rapid deployability and low-level flying for the collection of centimetre-level spatial resolution imagery. Coupled with recent innovations in photogrammetry and computer vision, UAVs equipped with consumer grade digital cameras are now frequently used to generate centimetre-resolution and accuracy mapping products, such as dense point clouds, digital elevation models and orthomosaics. Despite the efficiency of UAV data acquisition, the continued need for ground control implementation for photogrammetric image orientation remains a substantial workflow constraint. In addition to the associated costs, ground control must be implemented strategically, and usually extensively, to ensure photogrammetric products meet the accuracy requirements of large scale mapping, which may or may not be possible given constraints of the intended application. This research uses high precision, UAV-based GNSS (Global Navigation Satellite System) positioning techniques to substantially reduce ground control requirements by directly determining UAV image positions with centimetre-level accuracy and precision. The Precise Point Positioning (PPP) technique is applied and can yield centimetre-level planimetric and decimetre height accuracy photogrammetric mapping without GCPs, whilst the height accuracy can be improved to the centimetre-level using a single GCP. Unlike the standard relative GNSS positioning technique, PPP alleviates all spatial operating constraints associated with the installation and use of a local ground-based GNSS reference station, or the need to operate within the bounds of a permanent GNSS reference station network. Such a workflow simplifies operational logistics, and enables large-scale photogrammetric mapping from UAVs in even the most remote and challenging geographic locations globally. The approach was tested on 11 fixed wing UAV datasets, acquired at two sites in Northumberland, north-east England, which had varying ground control configurations. UAV flight durations, meaning time between launch and landing, were 12-42 minutes. It is shown that the main limitation of UAV-based PPP application is the inherent possibility of GNSS cycle slips and limited observation spans that inhibit the convergence of float ambiguity estimates. Although PPP camera position estimates were biased in such cases, GCPs were still minimised due to the retained precision of the PPP camera position estimates and constraints on the image block.EPSR