Buildings and terrain unified – multidimensional dual data structure for GIS

© 2016 Wuhan University. 3D city models are widely used in many disciplines and applications, such as urban planning, disaster management, and environmental simulation. Usually, the terrain and embedded objects like buildings are taken into consideration. A consistent model integrating these elements is vital for GIS analysis, especially if the geometry is accompanied by the topological relations between neighboring objects. Such a model allows for more efficient and errorless analysis. The memory consumption is another crucial aspect when the wide area of a city is considered – light models are highly desirable. Three methods of the terrain representation using the geometrical–topological data structure – the dual half-edge – are proposed in this article. The integration of buildings and other structures like bridges with the terrain is also presented

A Unified 3D Spatial Data Model for Surface and Subsurface Spatial Objects

Three Dimensional (3D) spatial modelling is an abstract representation of  reality using mathematically proven relationships defined as points, lines, polygons and solids to represent man-made and natural features above, on and below the surface of the earth. 3D topology is the interrelationships existing between these objects to enable visualization, query and analysis. 3D mod-elling of subsurface objects and their integration with the surface and above surface objects currently lags behind despite efforts of researchers and the attempt at viewing above, surface and subsurface man-made objects for earth realism. Level of Details (LoD) for spatial objects has been extensively studied. However, these have not been extended to man-made features below the surface. LoD maps for surface and subsurface integration exist for most city centres but the 3D component is lacking and this does not enhance the Level of Realism (LoR) in most city centres. Knowledge about the surface and subsurface 3D objects for city centres, mining and 3D cadastre will create awareness among stakeholders for effective planning of a city or mine. This paper provides a discussion for 3D surface and subsurface integration. Various 3D spatial data models currently in existence for the integration of surface and subsurface models are discussed and a geometric, topological 3D object oriented model is sug-gested. A UML diagram for the top hierarchy class is presented and a conceptual and logical model for surface and subsurface integration is also discussed. A simulation of the above, on and below 3D spatial models for man-made constructions at differ-ent LoDs is presented. A simulation of this with regards to mining and cadastre is also presented. The model presented can be adopted in realising 3D GIS for mining and 3D cadastre can be realised in Ghana. Further work is geared towards 3D spatial analysis for such an integrated model

The dual half-edge-a topological primal/dual data structure and construction operators for modelling and manipulating cell complexes

© 2016 by the authors. There is an increasing need for building models that permit interior navigation, e.g., for escape route analysis. This paper presents a non-manifold Computer-Aided Design (CAD) data structure, the dual half-edge based on the Poincaré duality that expresses both the geometric representations of individual rooms and their topological relationships. Volumes and faces are expressed as vertices and edges respectively in the dual space, permitting a model just based on the storage of primal and dual vertices and edges. Attributes may be attached to all of these entities permitting, for example, shortest path queries between specified rooms, or to the exterior. Storage costs are shown to be comparable to other non-manifold models, and construction with local Euler-type operators is demonstrated with two large university buildings. This is intended to enhance current developments in 3D Geographic Information Systems for interior and exterior city modelling

Three dimensional compact abstract cell complexes topological data structure for buildings in CityGML

As the significance of visualising objects in three dimensional is now recognised, most city modelling approaches support 3D primitives in the construction (3D) of objects and visualisation. Although the visualisation of city models is in 3D, the topological information maintained remains in two dimensional (2D). This hinders the 3D model to serve its full potential, as the topological information that gives meaning to the objects is not preserved explicitly. The support of 3D topology is crucial for 3D spatial analysis that requires connectivity information and adjacencies in order to produce accurate output in 3D. This research investigates the implementation of a 3D topological model specifically using the Compact Abstract Cell Complexes (CACC) topological data structure for preserving the topological information of buildings in City Geographic Markup Language (CityGML). As the international standard for city modelling, the topological component of CityGML is in 2D via the simple topology-incidence. The use of the simple topology-incidence mechanism within CityGML allows only explicitly stored surfaces can be referenced. This then brings up the issue of inconsistent visualisation which is usually resolved by modelling the two buildings with two separate surfaces representing the common surface. However, the connectivity information between the two connected buildings are not preserved in CityGML as they do not share the same explicitly stored surface. Three objectives were established for the study namely to determine the specifications of a topological data structure for preserving topological information of buildings in CityGML, to implement a topological structure for buildings in CityGML that supports connectivity queries and adjacency analyses for city modelling, and to validate the proposed topological data structure in terms of geometric and topological properties in comparison to the existing CityGML topology mechanism. Several tasks were carried out to complete this research, including extraction of geometrical properties from CityGML, generation of topological links, adjacency analysis using topological information, and visualisation of 3D model and adjacency analysis results. The absence of a comprehensive topological model within CityGML made it necessary to use the geometric properties of the buildings in CityGML as a stand-in model to extract the topological properties that would subsequently be the basis for generating topological links. The CACC topological model preserves topological information by building topological links where points are connected to build alpha-0 links (1D lines), alpha-0 links are connected to build alpha-1 links (2D surfaces), alpha-1 links are connected to build alpha-2 links (3D volumes) and alpha-3 links represent the connectivity between 3D buildings. This allows connectivity between elements of different dimension as any link can be decomposed to its related lower dimension elements. Next, by implementing CACC topological model, the connectivity information for two buildings that are connected but modelled with two separate surfaces can be preserved. The support of topological information via the CACC topological model also allows the seamless execution of adjacency queries between building elements, including elements of different dimensions

Automated construction of variable density navigable networks in a 3D indoor environment for emergency response

Widespread human-induced or natural threats on buildings and their users have made preparedness and quick response as crucial issues for saving human lives. Available information about an emergency scene, e.g. the building structure, material and trapped people helps for decision-making and organizing rescue operations. The ability to evaluate potential scenarios for human evacuation, and then identifying the paths of egress during an emergency is critical for rescue and emergency services. Good quality models supporting real, or near-real, time decision-making and allowing the implementation of automated methods are highly desirable. In this paper, we propose a new automated method for deriving a navigable network in a 3D indoor environment, including a full 3D topological model which may be used not only for standard navigation but also for finding alternative egress routes and simulating phenomena associated with disasters such as fire spread and heat transfer

Heuristic 3d Reconstruction Of Irregular Spaced Lidar

As more data sources have become abundantly available, an increased interest in 3D reconstruction has emerged in the image processing academic community. Applications for 3D reconstruction of urban and residential buildings consist of urban planning, network planning for mobile communication, tourism information systems, spatial analysis of air pollution and noise nuisance, microclimate investigations, and Geographical Information Systems (GISs). Previous, classical, 3D reconstruction algorithms solely utilized aerial photography. With the advent of LIDAR systems, current algorithms explore using captured LIDAR data as an additional feasible source of information for 3D reconstruction. Preprocessing techniques are proposed for the development of an autonomous 3D Reconstruction algorithm. The algorithm is designed for autonomously deriving three dimensional models of urban and residential buildings from raw LIDAR data. First, a greedy insertion triangulation algorithm, modified with a proposed noise filtering technique, triangulates the raw LIDAR data. The normal vectors of those triangles are then passed to an unsupervised clustering algorithm – Fuzzy Simplified Adaptive Resonance Theory (Fuzzy SART). Fuzzy SART returns a rough grouping of coplanar triangles. A proposed multiple regression algorithm then further refines the coplanar grouping by further removing outliers and deriving an improved planar segmentation of the raw LIDAR data. Finally, further refinement is achieved by calculating the intersection of the best fit roof planes and moving nearby points close to that intersection to exist at the intersection, resulting in straight roof ridges. The end result of the aforementioned techniques culminates in a well defined model approximating the considered building depicted by the LIDAR data

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin