Topology, homogeneity and scale factors for object detection: application of eCognition software for urban mapping using multispectral satellite image

The research scope of this paper is to apply spatial object based image analysis (OBIA) method for processing panchromatic multispectral image covering study area of Brussels for urban mapping. The aim is to map different land cover types and more specifically, built-up areas from the very high resolution (VHR) satellite image using OBIA approach. A case study covers urban landscapes in the eastern areas of the city of Brussels, Belgium. Technically, this research was performed in eCognition raster processing software demonstrating excellent results of image segmentation and classification. The tools embedded in eCognition enabled to perform image segmentation and objects classification processes in a semi-automated regime, which is useful for the city planning, spatial analysis and urban growth analysis. The combination of the OBIA method together with technical tools of the eCognition demonstrated applicability of this method for urban mapping in densely populated areas, e.g. in megapolis and capital cities. The methodology included multiresolution segmentation and classification of the created objects.Comment: 6 pages, 12 figures, INSO2015, Ed. by A. Girgvliani et al. Akaki Tsereteli State University, Kutaisi (Imereti), Georgi

Analysing trade-offs and synergies between SDGs for urban development, food security and poverty alleviation in rapidly changing peri-urban areas: a tool to support inclusive urban planning

Transitional peri-urban contexts are frontiers for sustainable development where land-use change involves negotiation and contestation between diverse interest groups. Multiple, complex trade-offs between outcomes emerge which have both negative and positive impacts on progress towards achieving Sustainable Development Goals (SDGs). These trade-offs are often overlooked in policy and planning processes which depend on top-down expert perspectives and rely on course grain aggregate data which does not reflect complex peri-urban dynamics or the rapid pace of change. Tools are required to address this gap, integrate data from diverse perspectives and inform more inclusive planning processes. In this paper, we draw on a reinterpretation of empirical data concerned with land-use change and multiple dimensions of food security from the city of Wuhan in China to illustrate some of the complex trade-offs between SDG goals that tend to be overlooked with current planning approaches. We then describe the development of an interactive web-based tool that implements deep learning methods for fine-grained land-use classification of high-resolution remote sensing imagery and integrates this with a flexible method for rapid trade-off analysis of land-use change scenarios. The development and potential use of the tool are illustrated using data from the Wuhan case study example. This tool has the potential to support participatory planning processes by providing a platform for multiple stakeholders to explore the implications of planning decisions and land-use policies. Used alongside other planning, engagement and ecosystem service mapping tools it can help to reveal invisible trade-offs and foreground the perspectives of diverse stakeholders. This is vital for building approaches which recognise how trade-offs between the achievement of SDGs can be influenced by development interventions

Large scale mapping: an empirical comparison of pixel-based and object-based classifications of remotely sensed data

In the past, large scale mapping was carried using precise ground survey methods. Later, paradigm shift in data collection using medium to low resolution and, recently, high resolution images brought to bear the problem of accurate data analysis and fitness-for-purpose challenges. Using high resolution satellite images such as QuickBird and IKONOS are now preferred alternatives. This paper is aimed at comparing pixel-based (PIXBIA) and Geo-object-based (GEOBIA) classification methods using ENVI 4.8 and eCongnition software respectively, and ArcGIS 10.1 for map layout creation. It uses Aba main city in south-eastern Nigeria as a case study. The paper further evaluates the classification accuracies obtained using error matrix and then test the classifications’ agreement to geographic reality using Kappa Coefficient statistical analysis. Analyzing 2012 QuickBird image as a proof of concept, the study shows that the object-based approach had a higher overall accuracy (OA= 98.75%) than the pixel-based approach (OA=79.44%). With a Kappa Coefficient of K=0.97 (very good) for object-based approach and K=0.62 (good) for pixel-based, the object-based method showed a higher class separability between and among examined geographic objects such as water, bare-land and tree canopy as evidenced in the Golf Course under re-construction in Aba city. In addition, the object-based results also show a higher overall producer accuracy (PA=98.42% > PA=85.37) and user accuracy (UA=96.70 > UA=81.04%) respectively. The paper, therefore, recommends that object-based classification method be applied in analyzing high resolution satellite image. The approach is also recommended for mapping urban areas in developing countries such as Nigeria where the paucity of fund required in flying airplane for the production of orthophotos is a major challenge in large scale mapping.Keywords: Image Classification, Object-based Classification, Pixel-based Classification, Remote Sensing, Urban Planning and Mapping

ERP customization failure: Institutionalized accounting practices, power relations and market forces

Purpose: This paper examines a detailed case study of Enterprise Resource Planning (ERP) customization failure in an Egyptian state-owned company (AML) by drawing on new institutional sociology and its extensions. It explains how ERP customization failure is shaped by the interplay between institutionalised accounting practices, conflicting institutions, power relations and market forces. Methodology/Approach: The research methodology is based on using an intensive case study informed by new institutional sociology, especially the interplay between conflicting institutions, power relations and market forces. Data were collected from multiple sources, including interviews, observations, discussions and documentary analysis. Findings: The findings revealed that the inability of the ERP system to meet the core accounting requirements of the control authorities (the Central Agency for Accountability) was the explicit reason cited for the ERP failure. The externally imposed requirements of the Uniform Accounting System and planning budgets were used to resist both other institutional pressures (from the Holding Company for Engineering Industries) and market and competitive pressures. Research limitations: There are some limitations associated with the use of the case study method, including the inability to generalize from the findings of a single case study, some selectivity in the individuals interviewed, and the subjective interpretation by the researchers of the empirical data. Practical implications: The paper identifies that the interplay between institutional pressures, institutionalised accounting practices, intra-organizational power relations, and market forces contributed to the failure to embed ERP in a major company. Understanding such relationships can help other organisations to become more aware of the factors affecting successful implementation of new ERP systems and provide a better basis for planning the introduction of new technologies. Originality/value of paper: This paper draws on recent research and thinking in sociology, especially the development and application of new institutional sociology. In addition, the paper is concerned with ERP implementation and use and management accounting in a transitional economy, Egypt, and hence contributes to debate about exporting Western accounting practices and other technologies to countries with different cultures and different stages of economic and political development. Classification: Research paper/ case stud

Neural network parameters affecting image classification

The study is to assess the behaviour and impact of various neural network parameters and their effects on the classification accuracy of remotely sensed images which resulted in successful classification of an IRS-1B LISS II image of Roorkee and its surrounding areas using neural network classification techniques. The method can be applied for various defence applications, such as for the identification of enemy troop concentrations and in logistical planning in deserts by identification of suitable areas for vehicular movement. Five parameters, namely training sample size, number of hidden layers, number of hidden nodes, learning rate and momentum factor were selected. In each case, sets of values were decided based on earlier works reported. Neural network-based classifications were carried out for as many as 450 combinations of these parameters. Finally, a graphical analysis of the results obtained was carried out to understand the relationship among these parameters. A table of recommended values for these parameters for achieving 90 per cent and higher classification accuracy was generated and used in classification of an IRS-1B LISS II image. The analysis suggests the existence of an intricate relationship among these parameters and calls for a wider series of classification experiments as also a more intricate analysis of the relationships

Interobserver and intraobserver reliability of a new radiological classification for femoroacetabular impingement syndrome

Purpose: Radiological evaluation of femoroacetabular impingement is based on single-plane parameters such as the alpha angle or the center edge angle, or complex software reconstruction. A new simple classification for cam and pincer morphologies, based on a two-plane radiological evaluation, is presented in this study. The determination of the intraobserver and interobserver reliability of this new classification is the purpose of this study. Methods: We retrospectively reviewed the three-view hip study in patient undergoing hip arthroscopy for FAI syndrome between October 2015 and April 2016. Any case having protrusio acetabuli, coxa profunda or which has undergone previous osteotomic surgery was excluded. Five observers used our proposed classification to identify three different stages for the cam and pincer morphologies. Inter- and intraobserver agreement of classification was determined using average pairwise Cohen’s kappa coefficient. Results: The interobserver agreement for the pincer and cam morphologies was excellent. For the pincer morphology classification, the average Kappa agreement was 0.838 (range 0.764–0.944). For the cam morphology, the average pairwise Cohen’s kappa coefficient was 0.846 (range 0.734–0.929). The intraobserver agreement was excellent as well. The average percent pairwise agreement was 0.870 and 0.845 for pincer and cam type, respectively. Conclusions: The new classification system shows excellent levels of inter- and intraobserver agreement for both deformities. This classification is demonstrated to be a useful tool in planning hip arthroscopy. Further studies are needed to correlate the classification itself with specific intraoperative findings

An automated nD model creation on BIM models

The construction technology (CONTEC) method was originally developed for automated CONTEC planning and project management based on the data in the form of a budget or bill of quantities. This article outlines a new approach in an automated creation of the discrete nD building information modeling (BIM) models by using data from the BIM model and their processing by existing CONTEC method through the CONTEC software. This article outlines the discrete modeling approach on BIM models as one of the applicable approaches for nD modeling. It also defines the methodology of interlinking BIM model data and CONTEC software through the classification of items. The interlink enables automation in the production of discrete nD BIM model data, such as schedule (4D) including work distribution end resource planning, budget (5D)—based on integrated pricing system, but also nD data such as health and safety risks (6D) plans (H&S Risk register), quality plans, and quality assurance checklists (7D) including their monitoring and environmental plans (8D). The methodology of the direct application of the selected classification system, as well as means of data transfer and conditions of data transferability, is described. The method was tested on the case study of an office building project, and acquired data were compared to actual construction time and costs. The case study proves the application of the CONTEC method as a usable method in the BIM model environment, enabling the creation of not only 4D, 5D models but also nD discrete models up to 8D models in the perception of the construction management process. In comparison with the existing BIM classification systems, further development of the method will enable full automated discrete nD model creation in the BIM model environment

SARS-CoV-2 virus classification based on stacked sparse autoencoder

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. Considering that motivation, the authors proposed an efficient viral genome classifier for the SARS-CoV-2 using the deep neural network based on the stacked sparse autoencoder (SSAE). For the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation was applied. We performed four experiments to provide different levels of taxonomic classification of the SARS-CoV-2. The SSAE technique provided great performance results in all experiments, achieving classification accuracy between 92% and 100% for the validation set and between 98.9% and 100% when the SARS-CoV-2 samples were applied for the test set. In this work, samples of the SARS-CoV-2 were not used during the training process, only during subsequent tests, in which the model was able to infer the correct classification of the samples in the vast majority of cases. This indicates that our model can be adapted to classify other emerging viruses. Finally, the results indicated the applicability of this deep learning technique in genome classification problems.Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) 00

Multiple Criteria Decision Analysis: Classification Problems and Solutions

Multiple criteria decision analysis (MCDA) techniques are developed to address challenging classification problems arising in engineering management and elsewhere. MCDA consists of a set of principles and tools to assist a decision maker (DM) to solve a decision problem with a finite set of alternatives compared according to two or more criteria, which are usually conflicting. The three types of classification problems to which original research contributions are made are Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. Nominal classification: Assign alternatives to nominal groups structured by the DM, so that the number of groups, and the characteristics of each group, seem appropriate to the DM. Research on screening is divided into two parts: the design of a sequential screening procedure that is then applied to water resource planning in the Region of Waterloo, Ontario, Canada; and the development of a case-based distance method for screening that is then demonstrated using a numerical example. Sorting problems are studied extensively under three headings. Case-based distance sorting is carried out with Model I, which is optimized for use with cardinal criteria only, and Model II, which is designed for both cardinal and ordinal criteria; both sorting approaches are applied to a case study in Canadian municipal water usage analysis. Sorting in inventory management is studied using a case-based distance method designed for multiple criteria ABC analysis, and then applied to a case study involving hospital inventory management. Finally sorting is applied to bilateral negotiation using a case-based distance model to assist negotiators that is then demonstrated on a negotiation regarding the supply of bicycle components. A new kind of decision analysis problem, called multiple criteria nominal classification (MCNC), is addressed. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCNC is the classification of alternatives into nominal groups, structured by the DM, who specifies multiple characteristics for each group. The features, definitions and structures of MCNC are presented, emphasizing criterion and alternative flexibility. An analysis procedure is proposed to solve MCNC problems systematically and applied to a water resources planning problem