In this paper, we study the maintenance of city parking facilities (CPFs), specifically on modeling and support for decision-making related to planning CPF maintenance. Managing the maintenance of CPFs is complicated because it is a multidisciplinary problem involving many participants, huge quantities of information, limited budgets, and conflicts of goals and criteria. These facts indicate that the decision-making processes in managing the maintenance of CPFs are ill-defined problems. To help maintenance managers cope with this complexity, we propose an approach that combines analytic hierarchy processing (AHP) and PROMETHEE multicriteria methods, and apply this approach to a priority-setting problem. After assessing the conditions of existing CPFs and the planned states of those CPFs, our approach produced a goal tree and criteria, and defined possible actions for the parking facilities. Representatives of stakeholders provided criteria weights by applying the AHP method. Using PROMETHEE II, we ranked the priorities, and the PROMETHEE V method allowed us to define the implementation phases of maintenance, producing the final maintenance plan. We validated our concept in the city of Split.
Abstract. More than half of the world’s population lives in big urbanized areas. It is not rare that those areas are lacking natural green spaces. Green spaces improve different aspects of life in cities and they are becoming so important that lately more and more attention is given to the so-called green infrastructure. The first step in planning green infrastructure is acquiring information about current city greenery. In this paper, it was investigated how can airborne, spaceborne, and street-level images be used in gathering information about greenery. As spaceborne images, Sentinel-2 satellite images were used and as street-level images, Google Street View 360° photospheres have been utilized. From both sources, information about current greenery status was automatically extracted. Gathered data was aggregated on different spatial units that are suitable for decision making that aims at further developing the green spaces. These top-down and street-level images complement each other in a way that top-down images can be used to track the percentage of green area and its changing over time, while street-level images give information about greenery that is perceived by pedestrians. With proposed methods, it is possible to detect areas that should be considered for greening and also to identify areas that should have priority in that process.
<p><strong>Abstract.</strong> One of the objectives of the Integrated Coastal Zone Management (ICZM) is to prevent and reduce the effects of natural hazards, particularly ones caused by climate changes. The ICZM methodologies include use of geographic information systems, from data collection and geo-analysis to dissemination of information to the public. As a part of the Interreg MED Co-evolve project cofinanced by the European regional development fund, the ICZM based action plan is being developed for the City of Kaštela in Croatia. Activities include assessing coastal vulnerability to climate change, focusing on sea flooding and storm damages and related socio-economic vulnerabilities. The paper presents development of large scale vulnerability analysis, adopted from the methodologies developed for mid and small scales. Suitability of the available data is assessed, either official or open source, and data gaps are described. The analysis’s results are presented in terms of the assets exposed to coastal flooding and storms, and future improvements of analysis towards house level vulnerability analysis is envisaged.</p>
A technical and scientific overview regarding satellite-derived bathymetry (SDB)—one of the most promising and relatively cheap methods of shallow water depth determination—is presented. The main goal of the article is to present information about the possibilities of the SDB method to meet the demanding standard of bathymetric measurements in coastal mapping areas up to 20 m deep, i.e., up to depth areas where the largest number of ports and access waterways are located, as obtained using the bibliometric analysis. The Web of Science (WoS) and Scopus scientific databases, as well as R studio applications Bibliometrix and Biblioshiny, were used for scientific analysis. The bibliometric analysis presents the quantitative aspects of producing and disseminating scientific and professional articles with SDB as their topic. Therefore, the purpose of this study was to give the academic community an insight into the current knowledge about the SDB method, its achievements and shortcomings. The results of the bibliometric analysis of articles dealing with SDB show that most authors use empirical statistical methods. However, in recent years, articles using automated artificial intelligence methods have prevailed, especially the machine learning method. It is concluded that SDB data can become a very important low-cost source of bathymetric data in shallow coastal areas. Satellite methods have been proven to be very effective in very shallow coastal areas (up to a depth of about 20 m), and their biggest advantage is that the depth data obtained in this way are relatively low cost, while major limitations are associated with the parameters that determine the properties of the atmosphere and water column (clear atmosphere and water column) and bottom material. Procedures for different bathymetric applications are being developed. Regardless of the significant progress of the SDB method, which was manifested in the development of sensors and processing methods, its results still do not meet the International Hydrographic Organization (IHO) Standards for Hydrographic Surveys S-44.
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