FACADE RECONSTRUCTION FOR TEXTURED LOD2 CITYGML MODELS BASED ON DEEP LEARNING AND MIXED INTEGER LINEAR PROGRAMMING

The paper describes a workflow for generating LoD3 CityGML models (i.e. semantic building models with structured facades) based on textured LoD2 CityGML models by adding window and door objects. For each wall texture, bounding boxes of windows and doors are detected using “Faster R-CNN”, a deep neural network. We evaluate results for textures with different resolutions on the ICG Graz50 facade dataset. In general, detected bounding boxes match very well with the rectangular shape of most wall openings. Thus, no further classification of shapes is required. Windows are typically aligned to rows and columns, and only a few different types of windows exist for each facade. However, the neural network proposes rectangles of varying sizes, which are not always aligned perfectly. Thus, we use post-processing to obtain a more realistic appearance of facades. Window and door rectangles get aligned by solving a mixed integer linear optimization problem, which automatically leads to a clustering of these openings into few different classes of window and door types. Furthermore, an a-priori knowledge about the number of clusters is not required

User hints for optimisation processes

Innovative improvements in the area of Human-Computer Interaction and User Interfaces have en-abled intuitive and effective applications for a variety of problems. On the other hand, there has also been the realization that several real-world optimization problems still cannot be totally auto-mated. Very often, user interaction is necessary for refining the optimization problem, managing the computational resources available, or validating or adjusting a computer-generated solution. This thesis investigates how humans can help optimization methods to solve such difficult prob-lems. It presents an interactive framework where users play a dynamic and important role by pro-viding hints. Hints are actions that help to insert domain knowledge, to escape from local minima, to reduce the space of solutions to be explored, or to avoid ambiguity when there is more than one optimal solution. Examples of user hints are adjustments of constraints and of an objective function, focusing automatic methods on a subproblem of higher importance, and manual changes of an ex-isting solution. User hints are given in an intuitive way through a graphical interface. Visualization tools are also included in order to inform about the state of the optimization process. We apply the User Hints framework to three combinatorial optimization problems: Graph Clus-tering, Graph Drawing and Map Labeling. Prototype systems are presented and evaluated for each problem. The results of the study indicate that optimization processes can benefit from human interaction. The main goal of this thesis is to list cases where human interaction is helpful, and provide an ar-chitecture for supporting interactive optimization. Our contributions include the general User Hints framework and particular implementations of it for each optimization problem. We also present a general process, with guidelines, for applying our framework to other optimization problems

A risk mitigation framework for construction / asset management of real estate and infrastructure projects

The increasing demand on residential, office, retail, and services buildings as well as hotels and recreation has been encouraging investors from both private and public sectors to develop new communities and cities to meet the mixed demand in one location. These projects are huge in size, include several diversified functions, and are usually implemented over many years. The real estate projects’ master schedules are usually initiated at an early stage of development. The decision to start investing in infrastructure systems, that can ultimately serve fully occupied community or city, is usually taken during the early development stage. This applies to all services such as water, electricity, sewage, telecom, natural gas, roads, urban landscape and cooling and heating. Following the feasibility phase and its generated implementation schedule, the construction of the infrastructure system starts together with a number of real estate projects of different portfolios (retail, residential, commercial,…etc.). The development of the remaining real estate projects continues parallel to customer occupancy of the completed projects. The occurrence of unforeseen risk events, post completing the construction of infrastructure system, may force decision makers to react by relaxing the implementation of the remaining unconstructed projects within their developed communities. This occurs through postponing the unconstructed project and keeping the original feasibility-based sequence of projects unchanged. Decision makers may also change the sequence of implementing their projects where they may prioritize either certain portfolio or location zone above the other, depending on changes in the market demand conditions. The change may adversely impact the original planned profit in the original feasibility. The profit may be generated from either real estate portfolios and/or their serving Infrastructure system. The negative impact may occur due to possible delayed occupancy of the completed real estate projects which in turn reduces the services demand. This finally results in underutilization of the early implemented Infrastructure system. This research aims at developing a dynamic decision support prototype system to quantify impacts of unforeseen risks on the profitability of real estate projects as well as its infrastructure system in the cases of changing projects’ implementation schedules. It is also aimed to support decision makers with scheduled portfolio mix that maximizes their Expected Gross Profit (EGP) of real estate projects and their infrastructure system. The provided schedules can be either based on location zone or portfolio type to meet certain marketing conditions or even to respect certain relations between neighbor projects’ implementation constraints. In order to achieve the research objectives, a Risk Impact Mitigation (RIM) decision support system is developed. RIM consists mainly of four models, Real Estate Scheduling Optimization Model RESOM, Sustainable Landscape Optimization Model SLOM, District Cooling Optimization Model DCOM and Water Simulation Optimization Model WSOM. Integrated with the three Infrastructure specialized models SLOM, DCOM, WSOM, RESOM provides EGP values for individual Infrastructure systems. The three infrastructure models provide the demand profile that relate to a RESOM generated implementation schedule. RESOM then uses these profiles for calculating the profits using the projects’ capital expenditure and financial expenses. The three models included in this research (SLOM, DCOM and WSOM) relate to the urban landscape, district cooling and water systems respectively. RIM is applied on a large scale real estate development in Egypt. The development was subjected to difficult political and financial circumstances that were not forecasted while preparing original feasibility studies. RIM is validated using a questionnaire process. The questionnaire is distributed to 31 experts of different academic and professional background. RIM’s models provided expected results for different real life cases tested by experts as part of the validation process. The validation process indicated that RIM’s results are consistent, in compliance with expected results and is extremely useful and novel in supporting real estate decision makers in mitigating risk impacts on their profits. The validation process also indicated promising benefits and potential need for developed commercial version for future application within the industry

Methods for Structural Pattern Recognition: Complexity and Applications

Katedra kybernetik

Park Equity Modeling: A Case Study of Asheville, North Carolina

Parks and greenspaces are publicly available entities that serve the vital purpose of promoting multiple aspects of human welfare. Unfortunately, the existence of park disparities is commonplace within the park setting. Specifically, marginalized individuals encounter limited park access, insufficient amenity provision, and poor maintenance. To remedy these disparities, we propose a process in which we select candidate park facilities and utilize facility location models to determine the optimal primary parks from both existing and candidate sites. We note that platforms currently exist to identify the geographical areas where residents lack sufficient access to parks. However, these platforms do not yet integrate the variety of demographic, infrastructural, dimensional, monetary, and environmental factors to guide decisions of future park locations. Further, these tools do not have the ability to recommend multiple park sites by considering how simultaneous park selection affects overall access. To support park and government agencies in their aims to improve the distribution and quality of greenspaces, we present a case study of park selection optimization modeling in Asheville, North Carolina. We propose mixed-integer programs that maximize park access across different dimensions of equity. The developed facility location models serve as intuitive preliminary tools to support proactive park and greenspace planning initiatives. Our research process includes developing an understanding of current park and greenspace inequities. We determine the key indicators of park goodness in order to formulate and analyze facility location models that promote park and greenspace equity. We begin this study with an introduction to park and greenspace benefits and disparities and discuss current park distribution and equity initiatives within Asheville, North Carolina. We explore literature concerning park requirements and facility location modeling. We represent the components of park goodness and equity in the formulation of two facility location models and include the data collection, analysis, and visualization of Asheville to depict model elements. Finally, we present and discuss the results of multiple analyses to recommend new park locations in Asheville and to determine the effectiveness of our models as a tool to guide strategic park location decisions based upon user-defined criteria and goals. This study serves as an initial step in the further development and incorporation of mathematical modeling to achieve social goals within the recreational setting

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence