Level of detail 2, 3D city model creation using a semi-automatic hybrid-driven approach

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University of New Brunswick


Today 55% of the world’s population lives in urban areas, a proportion that is expected to increase to 68% by 2050 (UN, 2018). Many cities already face challenges in meeting the needs of their growing urban population and basic services have become overwhelmed and inaccessible to many. 3D city models can be used to prepare for the future city, enabling informed analysis and sustainable development. This research proposes a new semi-automatic hybrid-driven method for the creation of a LOD2 3D city model. This model will be the base for future research and analysis and help to guide the future city as urbanization continues to grow. Each 3D city model has a level of detail (LOD) assigned to them: a standard set out by the Open Geospatial Consortium (OGC). The LOD outlines the overall usability of each model, determining how they can be used for informed analysis. There are 5 pre-defined LODs (LOD0-4) with LOD0 being a two-dimensional (2D) building footprint and LOD4 being a realistic building model. Currently, LOD0 and LOD1 are readily available but are limited in overall usability. LOD2, 3, and 4 are better for informed analysis but generally require massive amounts of data and powerful computers to make new 3D models; for practical reasons they can only be created over small areas limiting their usability. Therefore, for this thesis project, LOD2 creation methods were the primary focus. There are currently three methods for creating 3D city models – model-driven, datadriven, and hybrid-driven approaches. Model-driven approaches are the fastest and create a 3D city over large areas; however, these approaches created inaccurate models when the expected data did not fit one of the pre-defined libraries. Data-driven approaches are more accurate than model-driven approaches but often require large datasets and complex computer systems and are typically only used to re-create small sections of cities. Hybrid methods are a combination of the model and data-driven approaches combining the pros of both methods. For these reasons, this study focuses on the semi-automatic creation of 3D city models through a hybrid method.