An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment.

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region\u27s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera\u27s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification ( presence/absence detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Reliability in the utilization of the Global Positioning System (GPS) data demands a higher degree of accuracy with respect to time and positional information required by the user. However, various extrinsic and intrinsic parameters disrupt the data transmission phenomenon from GPS satellite to GPS receiver which always questions the trustworthiness of such data. Therefore, this manuscript offers a comprehensive insight into the data preprocessing methodologies evolved and adopted by present-day researchers. The discussion is carried out with respect to standard methods of data cleaning as well as diversified existing research-based approaches. The review finds that irrespective of a good number of work carried out to address the problem of data cleaning, there are critical loopholes in almost all the existing studies. The paper extracts open end research problems as well as it also offers an evidential insight using use-cases where it is found that still there is a critical need to investigate data cleaning methods

Detecting healthy concrete surfaces

Teams of engineers visually inspect more than half a million bridges per year in the US and EU. There is clear evidence to suggest that they are not able to meet all bridge inspection guideline requirements due to a combination of the level of detail expected, the limited time available and the large area of bridge surfaces to be inspected. Methods have been proposed to address this problem through damage detection in visual data, yet the inspection load remains high. This paper proposes a method to tackle this problem by detecting (and disregarding) healthy concrete areas that comprise over 80-90% of the total area. The originality of this work lies in the method’s slicing and merging to enable the sequential processing of high resolution bridge surface textures with a state of the art classifier to distinguish between healthy and potentially unhealthy surface texture. Morphological operators are then used to generate an outline mask to highlight the classification results in the surface texture. The training and validation set consists of 1,028 images taken from multiple Department of Transportation bridge inspection databases and data collection from ten highway bridges around Cambridge. The presented method achieves a search space reduction for an inspector of 90.1% with a risk of missing a defect patch of 8.2%. This work is of great significance for bridge inspectors as they are now able to spend more time on assessing potentially unhealthy surface regions instead of searching for these needles in a mainly healthy concrete surface haystack.This work is partly funded by Trimble Inc. and by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 31109806.0007. SeeBridge is co-funded by Funding Partners of the ERA-NET Plus Infravation and the European Commission. The Funding Partners of the Infravation 2014 Call are: Ministerie van Infrastructuur en Milieu, Rijkswaterstaat, Bundesministerium für Verkehr, Bau und Stadtentwicklung, Danish Road Directorate, Statens Vegvesen Vegdirektoratet, Trafikverket – Trv, Vegagerðin, Ministere de L’ecologie, du Developpement Durable et de L’energie, Centro para el Desarrollo Tecnologico Industrial, Anas S.P.A., Netivei Israel – National Transport Infrastructure Company Ltd. and Federal Highway Administration USDOT

Road condition assessment from aerial imagery using deep learning

Terrestrial sensors are commonly used to inspect and document the condition of roads at regular intervals and according to defined rules. For example in Germany, extensive data and information is obtained, which is stored in the Federal Road Information System and made available in particular for deriving necessary decisions. Transverse and longitudinal evenness, for example, are recorded by vehicles using laser techniques. To detect damage to the road surface, images are captured and recorded using area or line scan cameras. All these methods provide very accurate information about the condition of the road, but are time-consuming and costly. Aerial imagery (e.g. multi- or hyperspectral, SAR) provide an additional possibility for the acquisition of the specific parameters describing the condition of roads, yet a direct transfer from objects extractable from aerial imagery to the required objects or parameters, which determine the condition of the road is difficult and in some cases impossible. In this work, we investigate the transferability of objects commonly used for the terrestrial-based assessment of road surfaces to an aerial image-based assessment. In addition, we generated a suitable dataset and developed a deep learning based image segmentation method capable of extracting two relevant road condition parameters from high-resolution multispectral aerial imagery, namely cracks and working seams. The obtained results show that our models are able to extraction these thin features from aerial images, indicating the possibility of using more automated approaches for road surface condition assessment in the future

Enhancing wind erosion monitoring and assessment for U.S. rangelands

Wind erosion is a major resource concern for rangeland managers because it can impact soil health, ecosystem structure and function, hydrologic processes, agricultural production, and air quality. Despite its significance, little is known about which landscapes are eroding, by how much, and when. The National Wind Erosion Research Network was established in 2014 to develop tools for monitoring and assessing wind erosion and dust emissions across the United States. The Network, currently consisting of 13 sites, creates opportunities to enhance existing rangeland soil, vegetation, and air quality monitoring programs. Decision-support tools developed by the Network will improve the prediction and management of wind erosion across rangeland ecosystems. © 2017 The Author(s)The Rangelands archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information

Towards an electric bike level of service

The fast-growing market of electric bikes (e-bikes) has introduced a paradigm shift in mobility with a promise to enhance the sustainability agenda. An in-depth understanding of transport quality of service (QOS) from the e-bike rider’s perspective is a promising approach to sustain the role of the e-bike in mobility. Level of service (LOS) is a method by which to quantify QOS for different transport modes. However, to date, the knowledge on e-bike LOS (ELOS) lags far behind that on other transport modes. Therefore, the central aim of this thesis is to provide fundamental knowledge related to the development of ELOS. To address the main aim of the thesis, the travel behaviour and riding characteristics associated with e-bikes were scrutinised. Both qualitative and quantitative methods were employed to provide knowledge on the travel behaviour (strategical level) and riding characteristics (tactical level) related to e-bikes. From a strategic perspective, an extensive review of the literature was conducted to explore which transport mode LOS is applicable for developing ELOS. Based on the findings from the state of the art and the reviewed literature, bike LOS (BLOS) was deemed substantial for the development of ELOS. Thus, to move towards the development of ELOS, a set of studies was conducted to understand the comfort concerns of e-bike riders via the literature review, interviews and a field experiment. Based on the reviewed literature, it appears evident that research related to the travel behaviour of e-bike users is sparse and that the scale of e-bike substitution for other modes of transport is unclear. The findings of the aforementioned study led to the proposition of a preliminary theoretical framework for the development of ELOS and served as a roadmap for conducting the studies that followed. To provide a deeper understanding of the travel behaviour related to e-bikes, a qualitative study was conducted to explore e-bike users’ (riders) and nonusers’ comfort concerns. This study was extended to include the comfort and health concerns of e-bike users and nonusers in the unprecedented COVID-19 pandemic situation. The findings of this study provided a set of e-bike riding comfort variables, such as infrastructure facilities and e-bike performance in both pre- and peri-pandemic situations. This study also documented the potential effect of e-bike substitution for other transport modes such as public transport and cars. From a tactical level of analysis, there was a lack of studies to facilitate understanding the riding characteristics associated with e-bikes, specifically where vulnerable road users are involved. To address this knowledge gap, the interaction between e-bike users and pedestrians was studied in an off-road facility experiment. The study was designed to evaluate whether the traffic characteristics of passing (same-direction) and meeting (opposite-direction) encounters impose different difficulties for the navigation of the e-bike rider in pedestrian crowds. The results suggested that passing events cause the e-bike rider more hindrance compared to meeting events. This study was further extended to investigate the sociodemographic characteristics of e-bike riders along with their characteristics of riding in traffic and eventually model e-bike riders’ comfort in pedestrian crowds. In sum, this thesis addresses the knowledge gaps related to e-bike comfort concerns based on different study setups, which can be used substantially for developing ELOS. Along with exploring e-bike riders’ comfort concerns, the thesis puts forward information related to e-bike nonusers in both pre- and peri-pandemic situations. The findings of the thesis are applicable for planners and policy-makers when integrating the role of e-bikes in mobility policies. At a general level, the findings of the studies presented in this thesis pave the way for developing future ELOS and highlight the dire need to develop the concept of ELOS based on different contexts. All in all, the thesis opens new avenues into the field of e-bike comfort modelling by rendering the importance of the subject as an independent mode of transport

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care