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The revised Swiss Spatial Planning Act pursues inward settlement development to slow down urban sprawl and protect arable land. This requires quality-oriented and sustainable internal densification. The Geodesign Framework by Steinitz can support planning processes with public participation, where models and visualizations help to convey complex interrelationships to stakeholders. This paper presents a Geodesign based process model integrating GIS and Parametric Design, that allows to quantify and communicate effects on internal densification caused by changes to building regulations. An overview of the model and the most important results from its verification are presented. They indicate that the model approximates real interrelations well and is a suitable basis for further work.
Creation of Nominal Asset Value-Based Map with GIS: Case Study of Istanbul Beyoğlu and Gaziosmanpaşa Districts
Muhammed Oguzhan Mete, Tahsin Yomralioglu
Istanbul Technical University, Turkey
Valuation of real estate is a value estimation approach encountered in many applications such as taxation, buying, renting, expropriation and urban regeneration. It is very important to determine the most objective, accurate and acceptable value for real estate by considering the spatial developments. Besides the traditional land valuation methods, one of the stochastic methods used to determine the real estate values is “nominal valuation” method. With the nominal valuation approach, many spatial parameters that may affect the unit value are subjected to various spatial analyses and it is possible to produce pixel based land value map. Land value maps which produced using Geographic Information Systems based spatial analyses are in the raster data format and they need to be examined in order to find out how they are compatible with the unit market value where they represent. In this study, first of all, nominal value maps of Beyoğlu and Gaziosmanpaşa districts of Istanbul Turkey, which are selected as the study areas, were produced. As a result, it is aimed to create nominal asset-based land value maps and determine the ideal pixel size in raster based value map in study areas.
A 3D Spatial Data Framework for Urban Land and Property Management
Abbas Rajabifard, Jihye Shin, Behnam Atazadeh, Mohsen Kalantari.
The Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne
Over the last years, the unprecedented urbanization has fostered the rapid development of multi-story buildings and infrastructure facilities, resulting in spatial and functional complexities in cities. Land and property information plays an important role in a wide range of applications in land administration and management in rapidly growing cities. However, the current fragmented practice relied on 2D-based representation does not provide a reliable and accurate legal description of underground and aboveground properties as a foundation of making evidence-based decisions in support of economic prosperity, human activities and the public safety in urban areas. In this paper, the conceptual framework for 3D digital management of urban land and property information is proposed. The suggested framework comprises creating 3D models of urban land and property, validating the integrity of the models, integrating legal and physical information in the various 3D models, and analyzing the federated 3D model with the query. This approach will contribute to integrating silos of urban land and property information which can not only be utilized to manage the complex urban built environment and but also have the potential to reduce costs associated with data duplication.
Automatic Generation of LoD1 City Models and Building Segmentation from Single Aerial Orthographic Images using Conditional Generative Adversarial Networks
3D city models play an important role in multiple applications, but creating them still requires effort using various possible techniques. This paper proposes a new machine-learning-based framework for generating 3D city models. With the help of conditional Generative Adversarial Networks and single orthographic images, segmentation and height estimations of buildings are achieved. The height information per pixel and the building coordinates were generalized using a histogram for heights and the Douglas-Peucker algorithm. The framework was evaluated by using variations of the same dataset (for the city of Berlin) to show possible differences due to changes of the image size and representation of the heights. The evaluation reveals that it is possible to generate block models with a mean absolute height error of 5.53m per building, a mean absolute height error for the whole raster of 1.32m, and a Jaccard Index of 0.55 for the segmentation. While the proposed framework for generating LoD1 city models does not attain the accuracy of previous techniques, our work represents a step towards successfully using machine learning for the automatic generation of city models and building segmentation.