Calculating and Assessing Mobile Mapping System Point Density for Roadside Infrastructure Surveys

The current generation of Mobile Mapping Systems (MMSs) capture increasingly larger amounts of data in a short time frame. Due to the relative novelty of this technology there is no concrete understanding of the point density that different scanner confgurations and scanner hardware settings will exhibit on objects at specific distances. Depending on the project requirements, obtaining the required point density impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. Insufficient knowledge of the factors in uencing MMS point density means that defning point density in project specifications is a complicated process. The objectives of this thesis are to calculate point density, to assess MMS laser scanner configuration and hardware settings and to benchmark a selection of MMSs in terms of their point density. The calculation methods involve a combination of algorithms applying 3D surface normals and 2D geometric formulae and outputs profile angle, profile spacing, point spacing and point density. Each of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings - all important features in asset management surveys. These algorithms are combined in a system called the Mobile Mapping Point Density Calculator (MIMIC). MIMIC is then applied in a series of tests identifying the recommended MMS laser scanner configuration and scanner hardware settings for near side infrastructure. The in uence that the scanner orientation and location on the MMS has on point density is quantified, resulting in a recommended MMS laser scanner configuration. A series of benchmarking tests assess the performance of one commercial and two theoretical MMSs in terms of their point density. The recommended configuration identified in the previous tests allows a low specification MMS to increase its performance in relation to a higher specification MMS. The benchmarking tests also highlight that a high pulse repetition rate is preferable to a high mirror frequency for maximising point density. The findings in this thesis enable a MMS to be configured to maximise point density for specific targets. Researchers can utilise MIMIC to tailor their automated algorithm's point density requirements for specific targets

Mobile mapping system performance - an analysis of the effect of laser scanner configuration and vehicle velocity on scan profiles

When a laser scanner is mounted on a moving platform and combined with a GNSS receiver and inertial navigation system, it is capable of producing millions of geo-referenced points which can then be used to create near-3D models. The development of processing algorithms for these point clouds has largely been the focus of the research community to date. However, given an arbitrary known static object positioned at a specific distance away from a mobile mapping system (MMS) the resolution of the resulting point cloud that will describe that object is unknown. This is the underlying limit of point cloud processing algorithms. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at specified distances. Our previous work has demonstrated our initial investigations into the effect that a scanner position, configuration and operating speed has on scan lines - both in profile spacing and scan line orientation at varying vehicle speeds. This paper focuses on the combined effect on profiles of both vertical and horizontal rotations of the scanner, explores in greater detail the effect on scan line orientation caused by vehicle motion and also incorporates point spacing as a function of range into our model. As with our previous work, we will develop a system to calculate this information and then verify our equations and analysis by comparing our simulated data to the point cloud data collected by our XP-1 mobile mapping system

MIMIC : Mobile Mapping Point Density Calculator

The current generation of Mobile Mapping Systems (MMSs) capture increasingly larger amounts of data in a short time frame. Due to the relative novelty of this technology there is no concrete understanding of the point density that differ- ent hardware configurations and operating parameters will exhibit on objects at specific distances. Depending on the project requirements, obtaining the required point density impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. A limited un- derstanding of the capabilities of these systems means that defining point density in project specifications is a compli- cated process. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at spec- ified distances. We have previously demonstrated the capa- bilities of our system for calculating point spacing, profile angle and profile spacing individually. Each of these ele- ments are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings - all important features in asset management surveys. This paper will introduce the current version of the MobIle Map- ping point densIty Calculator (MIMIC), MIMIC’s visuali- sation module and finally discuss the methods employed to validate our work

Calculating the effect of dual-axis scanner rotations and surface orientation on scan profiles

The large volumes of point cloud data collected by a Mobile Mapping System(MMS) equipped with a laser scanner have attracted the attention of the research community, primarily towards developing automated algorithms to help when processing this data. This has resulted in insufficient attention being paid to quantifying the capabilities of these systems, and due to the relative youth of this technology there is no concrete understanding of the point density that different hardware configurations and operating parameters will exhibit on objects at specific distances. Obtaining the required point density for a project impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. Lack of understanding of these systems makes defining point density in project specifications a complicated process. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at specified distances. We have previously demonstrated the effect that scanner orientation in one axis, scanner configuration and scanner operating speed have on scan profiles. We have also focused on the effect on scan profiles of the combined vertical and horizontal rotations of the scanner (dual-axis rotations) and also incorporated point spacing for planar surfaces at different scanner mirror speeds, pulse repetition rates and field of view as a function of range into our model. The subject of this paper is to investigate the effect that a dual-axis scanner rotation has on profile spacing and to design a theoretical system to calculate the angular change on profiles exhibited on horizontal and vertical surfaces for different system configurations. The second goal of the research presented in this paper is to include in our calculations a method for incorporating surfaces that are not parallel to the direction of travel or that are not perfectly vertical, such as walls facing away from the road or sloped surfaces. Profile angle impacts on profile spacing and is a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings, all important features in asset management surveys. A number of tests were designed to investigate these issues and the results show that these tests have justified our methods, but it has been made apparent that vehicle dynamics play a larger role than anticipated

Initial Results From European Road Safety Inspection (eursi) Mobile Mapping Project

Mobile mapping systems are becoming a popular method for collecting high quality near 3D information of terrestrial scenes. Modern mobile mapping systems can produce millions of georeferenced points per minute. These can be used to gather quantitative information about surfaces and objects. With this geospatial data it is becoming possible to segment and extract the road surface. In this paper, we will detail a novel LIDAR based road edge extraction algorithm which is applicable to both urban and rural road sections

Estimating missing data in hierarchical space-time series with a short temporal extent

A challenging problem exists in the estimation of missing space-time data where the time series are relatively short, and the space series belong to a spatial hierarchy. An example is provided by the population estimates for regions belonging to the NUTS hierarchy which are available from the EUROSTAT data portal. The table demo_r_gind3 provides estimates of the population of NUTS0/1/2/3 regions at the 1st January 2000…2012 inclusive. Inspection of the table reveals that estimates are missing for 2000-2003 for two of the five NUTS3 regions in the NUTS2 region of Liège. There are other instances of missing data at NUTS3 where there are data for the corresponding higher level NUTS regions. The EUROSTAT table demo_r_d2jan provides estimates of the population on the 1st January for a longer time period, 1990…2012 inclusive, but these are only to NUTS2. Again, there is missing data. The question then arises as to whether it is possible to estimate the missing series. The NUTS2 values act as a constraint on the NUTS3 values – the total population of the NUTS3 regions should equal those of the corresponding NUTS2 regions. However, the relative shortness of the available series is a challenge if conventional methods of time series analysis are adopted. Furthermore, the imposition of the spatial constraints is both a check as well as a challenge

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management

Avaliação do desempenho financeiro das empresas do setor eléctrico em Portugal

A avaliação do desempenho financeiro do sector elétrico tem sido o foco de atenção de diversos estudos, mas existe uma escassez de literatura científica que aborde especificamente o desempenho financeiro das empresas deste sector. Assim, o presente trabalho tem como objetivo avaliar o desempenho financeiro das empresas reguladas que operam no mercado elétrico Português. Com este objetivo em mente, propõe-se uma estrutura de modelagem que combina o uso do método de estimação Generalized Method of Moments (GMM) com a análise Data Envelopment Analysis (DEA). Este estudo centra-se no período entre 2010 e 2014, altura em que o governo português necessitou de ajuda financeira externa. O método de estimação GMM permitiu selecionar as variáveis corporativas intrínsecas, que foram então usadas para avaliar o desempenho financeiro das empresas do setor elétrico através do modelo DEA Slack Based Measure (SBM). Neste contexto, o retorno sobre o património líquido (ROE), a alavancagem (Leverage) e os fluxos de caixa sobre o total do ativo (CFTA) foram selecionados como outputs, enquanto o valor da soma das depreciações e das amortizações sobre o total de ativos (DATA) foram considerados como inputs. Os resultados sugerem que enquanto em 2010 a maioria das empresas não eficientes deveriam investir em novos ativos fixos de modo a tornarem-se eficientes, em 2014, uma percentagem expressiva de empresas não eficientes deve diminuir este tipo de investimento. Adicionalmente, em ambos os períodos, a maioria das empresas do sector elétrico não eficientes deveria aumentar o seu ROE para se tornar eficiente, destacando o papel do ROE na explicação da eficiência financeira. Em 2014, as empresas não eficientes são capazes de gerar fluxos de caixa com eficiência, uma vez que quase não são necessários ajustes em relação aos valores de CFTA obtidos por estas empresas. Por fim, a necessidade de promover a alavancagem para aumentar o desempenho financeiro é mais evidente em 2010 do que em 2014, reconhecendo que os novos investimentos feitos neste período utilizam capital alheio

Improving Satellite-derived Bathymetry

Bathymetry is traditionally acquired using singlebeam or multibeam echosounders. This method produces accurate depth measurements along transects but is constrained by operating cost and an inability to survey in very shallow waters. Airborne Lidar is able to produce accurate bathymetric information over clear waters at depths up to 70m, but can be costly and is limited by a relatively coarse bathymetric sampling interval. Experience in Irish waters has resulted in very poor seabed detection along the east coast and limited penetration on the west coast. An efficient and cost-effective alternative is satellitederived bathymetry

Improving Satellite-derived Bathymetry

Bathymetry is traditionally acquired using singlebeam or multibeam echosounders. This method produces accurate depth measurements along transects but is constrained by operating cost and an inability to survey in very shallow waters. Airborne Lidar is able to produce accurate bathymetric information over clear waters at depths up to 70m, but can be costly and is limited by a relatively coarse bathymetric sampling interval. Experience in Irish waters has resulted in very poor seabed detection along the east coast and limited penetration on the west coast. An efficient and cost-effective alternative is satellitederived bathymetry