Accurate urban road model reconstruction from high resolution remotely sensed imagery based on Support Vector Machine and Gabor filters

In this paper, we presented an automatic system for precise urban road model reconstruction based on aerial images with high spatial resolution. The proposed approach consists of two steps: i) road surface detection and ii) road pavement marking extraction. In the first step, support vector machine (SVM) was utilized to classify the images into two categories: road and non-road. In the second step, road lane markings are further extracted on the generated road surface based on 2D Gabor filters. The experiments using several pan-sharpened aerial images of Brisbane, Queensland have validated the proposed method.

Sonae MC: The road model of Kaizen

Sonae MC is considered the first success case of Kaizen in the retail industry. Before becoming a true role model for so many companies, there was a long road to walk. However, it may still be hard to understand the steps taken on the way. How could a training program develop into an integral continuous improvement system, and how did it affect the company – its people, culture, operations and strategy? How was it possible to get everyone on board? How could it be sustained until today, when Kaizen usually fails in the West? What were the critical factors for success?

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

Object-Space Road Extraction in Rural Areas Using Stereoscopic Aerial Images

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Semiautomatic object-space road extraction combining a stereoscopic image pair and a TIN-Based DTM

In this letter, a semiautomatic method for road extraction in object space is proposed that combines a stereoscopic pair of low-resolution aerial images with a digital terrain model (DTM) structured as a triangulated irregular network (TIN). First, we formulate an objective function in the object space to allow the modeling of roads in 3-D. In this model, the TIN-based DTM allows the search for the optimal polyline to be restricted along a narrow band that is overlaid upon it. Finally, the optimal polyline for each road is obtained by optimizing the objective function using the dynamic programming optimization algorithm. A few seed points need to be supplied by an operator. To evaluate the performance of the proposed method, a set of experiments was designed using two stereoscopic pairs of low-resolution aerial images and a TIN-based DTM with an average resolution of 1 m. The experimental results showed that the proposed method worked properly, even when faced with anomalies along roads, such as obstructions caused by shadows and trees.

A study on matching and multi-objective fuzzy control strategy of heavy truck suspension system

Matching method of heavy truck-rear air suspensions is discussed, and a fuzzy control strategy which improves both ride comfort and road friendliness of truck by adjusting damping coefficients of the suspension system is found. In the first place, a Dongfeng EQ1141G7DJ heavy truck’s ten DOF whole vehicle-road model was set up based on Matlab/Simulink and vehicle dynamics. Then appropriate passive air suspensions were chosen to replace the original rear leaf springs of the truck according to truck-suspension matching criterions, consequently, the stiffness of front leaf springs were adjusted too. Then the semi-active fuzzy controllers were designed for further enhancement of the truck’s ride comfort and the road friendliness. After the application of semi-active fuzzy control strategy through simulation, is was indicated that both ride comfort and road friendliness could be enhanced effectively under various road conditions. The strategy proposed may provide theory basis for design and development of truck suspension system in China.

Runoff, sediment loss and water quality from forest roads in a southeast Queensland coastal plain Pinus plantation

Runoff and sediment loss from forest roads were monitored for a two-year period in a Pinus plantation in southeast Queensland. Two classes of road were investigated: a gravelled road, which is used as a primary daily haulage route for the logging area, and an ungravelled road, which provides the main access route for individual logging compartments and is intensively used as a haulage route only during the harvest of these areas (approximately every 30 years). Both roads were subjected to routine traffic loads and maintenance during the study. Surface runoff in response to natural rainfall was measured and samples taken for the determination of sediment and nutrient (total nitrogen, total phosphorus, dissolved organic carbon and total iron) loads from each road. Results revealed that the mean runoff coefficient (runoff depth/rainfall depth) was consistently higher from the gravelled road plot with 0.57, as compared to the ungravelled road with 0.38. Total sediment loss over the two-year period was greatest from the gravelled road plot at 5.7 t km−1 compared to the ungravelled road plot with 3.9 t km−1. Suspended solids contributed 86% of the total sediment loss from the gravelled road, and 72% from the ungravelled road over the two years. Nitrogen loads from the two roads were both relatively constant throughout the study, and averaged 5.2 and 2.9 kg km−1 from the gravelled and ungravelled road, respectively. Mean annual phosphorus loads were 0.6 kg km−1 from the gravelled road and 0.2 kg km−1 from the ungravelled road. Organic carbon and total iron loads increased in the second year of the study, which was a much wetter year, and are thought to reflect the breakdown of organic matter in roadside drains and increased sediment generation, respectively. When road and drain maintenance (grading) was performed runoff and sediment loss were increased from both road types. Additionally, the breakdown of the gravel road base due to high traffic intensity during wet conditions resulted in the formation of deep (10 cm) ruts which increased erosion. The Water Erosion Prediction Project (WEPP):Road model was used to compare predicted to observed runoff and sediment loss from the two road classes investigated. For individual rainfall events, WEPP:Road predicted output showed strong agreement with observed values of runoff and sediment loss. WEPP:Road predictions for annual sediment loss from the entire forestry road network in the study area also showed reasonable agreement with the extrapolated observed values.

Extração semi-automática de rodovia baseada em um par estéreo de imagens aéreas e em otimização por programação dinâmica no espaço-objeto

This article proposes a method for 3D road extraction from a stereopair of aerial images. The dynamic programming (DP) algorithm is used to carry out the optimization process in the object-space, instead of usually doing it in the image-space such as the DP traditional methodologies. This means that road centerlines are directly traced in the object-space, implying that a mathematical relationship is necessary to connect road points in object and image-space. This allows the integration of radiometric information from images into the associate mathematical road model. As the approach depends on an initial approximation of each road, it is necessary a few seed points to coarsely describe the road. Usually, the proposed method allows good results to be obtained, but large anomalies along the road can disturb its performance. Therefore, the method can be used for practical application, although it is expected some kind of local manual edition of the extracted road centerline.

Extração semiautomática de rodovias no espaço-objeto: uso integrado de um estereopar de imagens aéreas e um MDT

Neste artigo é proposto um método semiautomático para extração de rodovias combinando um estereopar de imagens aéreas de baixa resolução com um poliedro gerado a partir de um modelo digital do terreno (MDT). O problema é formulado no espaço-objeto através de uma função objetivo que modela o objeto 'rodovia' como uma curva suave e pertencente a uma superfície poliédrica. A função objetivo proposta depende também de informações radiométricas, que são acessadas no espaço-imagem via relação de colinearidade entre pontos da rodovia no espaço-objeto e os correspondentes nos espaços imagem do estereopar. A linha poligonal que melhor modela a rodovia selecionada é obtida por otimização no espaço-objeto da função objetivo, tendo por base o algoritmo de programação dinâmica. O processo de otimização é iterativo e dependente do fornecimento por um operador de uma aproximação inicial para a rodovia selecionada. Os resultados obtidos mostraram que o método é robusto frente a anomalias existentes ao longo das rodovias, tais como obstruções causadas por sombras e árvores.

Extração semi-automática de rodovias no espaço-objeto: uso integrado de um estéreo par de imagens aéreas e um MDT

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Extração semi-automática da malha viária em imagens aéreas digitais de áreas rurais utilizando otimização por programação dinâmica no espaço objeto

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Developing an integrated decision model for the enhancement of sustainability deliverables for Australian road infrastructure projects

Road and highway infrastructure provides the backbone for a nation's economic growth. The versatile dispersion of population in Australia, from sparsely settled communities in remote areas to regenerated inner city suburbs with high density living in metropolitans, calls for continuing development and improvement on roads infrastructure under the current federal government policies and state governments' strategic plans. As road infrastructure projects involve large resources and mechanism, achieving sustainability not only in economic scales but also through environmental and social responsibility becomes a crucial issue. Current efforts are often impeded by different interpretation on sustainability agenda by stakeholders involved in these types of projects. As a result, sustainability deliverables at the project level is not often as transparent and measurable, compared to promises in project briefs and designs. This paper reviews the past studies on sustainable infrastructure construction, focusing on roads and highway projects. Through literature study and consultation with the industry, key sustainability indicators specific to road infrastructure projects have been identified. Based on these findings, this paper introduces an on-going research project aimed at identifying and integrating the different perceptions and priority needs of the stakeholders, and issues that impact on the gap between sustainability foci and its actual realization at project end level. The exploration helps generate an integrated decision-making model for sustainable road infrastructure projects. The research will promote to the industry more systematic and integrated approaches to decision-making on the implementation of sustainability strategies to achieve deliverable goals throughout the development and delivery process of road infrastructure projects in Australia.

Developing a life-cycle costing analysis model for sustainability enhancement in road infrastructure project

With increasing pressure to provide environmentally responsible infrastructure products and services, stakeholders are putting significant foci on the early identification of financial viability and outcome of infrastructure projects. Traditionally, there has been an imbalance between sustainable measures and project budget. On one hand, the industry tends to employ the first-cost mentality and approach to developing infrastructure projects. On the other, environmental experts and technology innovators often push for the ultimately green products and systems without much of a concern for cost. This situation is being quickly changed as the industry is under pressure to continue to return profit, while better adapting to current and emerging global issues of sustainability. For the infrastructure sector to contribute to sustainable development, it will need to increase value and efficiency. Thus, there is a great need for tools that will enable decision makers evaluate competing initiatives and identify the most sustainable approaches to procuring infrastructure projects. In order to ensure that these objectives are achieved, the concept of life-cycle costing analysis (LCCA) will play significant roles in the economics of an infrastructure project. Recently, a few research initiatives have applied the LCCA models for road infrastructure that focused on the traditional economics of a project. There is little coverage of life-cycle costing as a method to evaluate the criteria and assess the economic implications of pursuing sustainability in road infrastructure projects. To rectify this problem, this paper reviews the theoretical basis of previous LCCA models before discussing their inability to determinate the sustainability indicators in road infrastructure project. It then introduces an on-going research aimed at developing a new model to integrate the various new cost elements based on the sustainability indicators with the traditional and proven LCCA approach. It is expected that the research will generate a working model for sustainability based life-cycle cost analysis.

Integration of a road traffic noise model (ASJ) and traffic simulation (AVENUE) for a built-up area

A road traffic noise prediction model (ASJ MODEL-1998) has been integrated with a road traffic simulator (AVENUE) to produce the Dynamic areawide Road traffic NoisE simulator-DRONE. This traffic-noise-GIS based integrated tool is upgraded to predict noise levels in built-up areas. The integration of traffic simulation with a noise model provides dynamic access to traffic flow characteristics and hence automated and detailed predictions of traffic noise. The prediction is not only on the spatial scale but also on temporal scale. The linkage with GIS gives a visual representation to noise pollution in the form of dynamic areawide traffic noise contour maps. The application of DRONE on a real world built-up area is also presented.