There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Accurate, precise and rapid acquisition of topographic data is fundamental to many sub-disciplines of physical geography. Technological developments over the past few decades have made fully distributed data sets of centimetric resolution and accuracy commonplace, yet the emergence of Structure from Motion (SfM) with Multi-View Stereo (MVS) in recent years has revolutionised three-dimensional topographic surveys in physical geography by democratising data collection and processing. SfM-MVS originates from the fields of computer vision and photogrammetry, requires minimal expensive equipment or specialist expertise and, under certain conditions, can produce point clouds of comparable quality to existing survey methods (e.g. Terrestrial Laser Scanning). Consequently, applications of SfM-MVS in physical geography have multiplied rapidly. There are many practical options available to physical geographers when planning a SfM-MVS survey (e.g. platforms, cameras, software), yet, many SfM-MVS end-users are uncert...

/pdf/structure-from-motion-photogrammetry-in-physical-geography-1yja1579z5.pdf

Structure from Motion Photogrammetry in Physical Geography

Environmental hazards: Assessing risk and reducing disaster

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

/pdf/comprehensive-review-of-artificial-neural-network-4sb2zr5avn.pdf

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

. Photogrammetry and geosciences have been closely linked since the late 19th century due to the acquisition of high-quality 3-D data sets of the environment, but it has so far been restricted to a limited range of remote sensing specialists because of the considerable cost of metric systems for the acquisition and treatment of airborne imagery. Today, a wide range of commercial and open-source software tools enable the generation of 3-D and 4-D models of complex geomorphological features by geoscientists and other non-experts users. In addition, very recent rapid developments in unmanned aerial vehicle (UAV) technology allow for the flexible generation of high-quality aerial surveying and ortho-photography at a relatively low cost. The increasing computing capabilities during the last decade, together with the development of high-performance digital sensors and the important software innovations developed by computer-based vision and visual perception research fields, have extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure-from-motion (SfM) workflows are based upon algorithms for efficient and automatic orientation of large image sets without further data acquisition information, examples including robust feature detectors like the scale-invariant feature transform for 2-D imagery. Nevertheless, the importance of carrying out well-established fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors, still needs to be adapted in the common scientific practice. This review intends not only to summarise the current state of the art on using SfM workflows in geomorphometry but also to give an overview of terms and fields of application. Furthermore, this article aims to quantify already achieved accuracies and used scales, using different strategies in order to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that some lessons learned from former articles, scientific reports and book chapters concerning the identification of common errors or "bad practices" and some other valuable information may help in guiding the future use of SfM photogrammetry in geosciences.

/pdf/image-based-surface-reconstruction-in-geomorphometry-merits-g68ovc3a9x.pdf

Image-based surface reconstruction in geomorphometry - merits, limits and developments

The aim of this study was to examine the capability of structure-from-motion photogrammetry in defining the geometry of cliffs and undercuts in rocks of complex geomorphology. A case site was chosen along pocket beaches near the village of Stara Baska on the Adriatic Sea island of Krk, Gulf of Kvarner, Croatia, where cliff erosion of 5 m in breccias was identified by comparison of aerial photographs from 1960 and 2004. The 3D point cloud was derived from approx. 800 photos taken on 9 January 2014 by a single camera from various elevations and angles, and processed using the online software ReCap (Autodesk). Data acquisition was found to be quick and the method easy to implement. The difference between the georeferenced 3D cloud points and an RTK-GPS survey was 7 cm, i.e. within the limits of RTK-GPS precision. Quantifying the spatial variation in undercut geometries revealed that the deepest and largest (17 m3) undercut was in the south-eastern sector of the beach. Reconstructing the detailed geomorphology of this 3.8-m-deep undercut convincingly demonstrates the high efficiency of the method. Such assessments of spatiotemporal changes in undercut and overhang volumes can prove useful for evaluations of cliff erosion risk. Coupled with the low cost and relatively simple application, this is evidently an attractive technique for meaningful geotechnical and coastal engineering monitoring in the future on the island of Krk and, for that matter, also on other Adriatic islands and in similar settings worldwide.

Coastal cliff geometry derived from structure-from-motion photogrammetry at Stara Baška, Krk Island, Croatia

A way to minimize uncertainty and achieve the best possible project performance in construction project management can be achieved during the procurement process, which involves selecting an optimal contractor according to “the most economically advantageous tender.” As resources are limited, decision-makers are often pulled apart by conflicting demands coming from various stakeholders. The challenge of addressing them at the same time can be modelled as a multi-criteria decision-making problem. The aim of this paper is to show that the analytic hierarchy process (AHP) together with PROMETHEE could cope with such a problem. As a result of their synergy, a decision support concept for selecting the optimal contractor (DSC-CONT) is proposed that: (a) allows the incorporation of opposing stakeholders’ demands; (b) increases the transparency of decision-making and the consistency of the decision-making process; (c) enhances the legitimacy of the final outcome; and (d) is a scientific approach with great potential for application to similar decision-making problems where sustainable decisions are needed.

A Multi-Criteria Decision Support Concept for Selecting the Optimal Contractor

Life-cycle management of urban infrastructure projects is very complex process from both management and economic aspects. Focus of this research is on urban infrastructure maintenance phase of a life-cycle, especially on decision making in maintenance problems. Urban infrastructure maintenance management deals with complex decision making process. The reasons for a complexity are: lots of participants, multi- disciplinarity, huge quantity of information, limited budget, conflict goals and criteria. These facts indicate that decision making processes in urban infrastructure management undoubtedly belong to ill-defined problems. In order to cope with such complexity and to help project managers during decision making processes this research proposes an application of multicriteria methods. Multicriteria methodology proposed herein is applied on priority setting problem. It starts with goal analysis followed by definition of urban infrastructure elements and development of adequate criteria set. Evaluation of criteria importance (weights) is based on a set of experts’ opinions processed by AHP method. An assessment of maintenance conditions of urban infrastructure elements is provided trough monitoring process. The way of using proper forms and procedures for data collection is presented as well. All collected data are processed by PROMETHEE multicriteria methods. The main result of a multicriteria process is priority maintenance list for urban infrastructure elements. The methodology is tested on road infrastructure of town of Split.

/pdf/decision-support-system-to-urban-infrastructure-maintenance-3ke9poebcc.pdf

Decision Support System to Urban Infrastructure Maintenance Management

A contractor’s ability to prepare a competitive bid for a construction tender is crucial for its survival on the market. The bid price estimation strategy should promote the probability of winning a sufficient amount of tenders but, at the same time, ensure the economic stability and development of the company. This paper aims to address this issue in the area of Czech public construction procurement. The opinions, experiences and practices of contractors were collected through a questionnaire survey, and the data were evaluated with the support of statistical methods. This revealed that Czech contractors mostly base their multicriteria bidding strategy on cost-oriented pricing while considering various aspects such as the risks and attractiveness of the tender. The Czech construction market is generally perceived as oriented toward low costs, and with a relatively common occurrence of abnormally low bids. The findings presented in this paper may help contractors improve their current bidding strategies in public construction procurement.

https://www.mdpi.com/2075-5309/11/2/47/pdf

Bidding Strategy in Construction Public Procurement: A Contractor’s Perspective

The maintenance planning within the urban road infrastructure management is a complex problem from both the management and technoeconomic aspects. The focus of this research is on decision-making processes related to the planning phase during management of urban road infrastructure projects. The goal of this research is to design and develop an ANN model in order to achieve a successful prediction of road deterioration as a tool for maintenance planning activities. Such a model is part of the proposed decision support concept for urban road infrastructure management and a decision support tool in planning activities. The input data were obtained from Circly 6.0 Pavement Design Software and used to determine the stress values (560 testing combinations). It was found that it is possible and desirable to apply such a model in the decision support concept in order to improve urban road infrastructure maintenance planning processes.

https://downloads.hindawi.com/journals/complexity/2018/5160417.pdf

Urban Road Infrastructure Maintenance Planning with Application of Neural Networks

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