3D Design and Modeling of Smart Cities from a Computer Graphics Perspective

Abstract

.Modeling cities, and urban spaces in general, is a daring task for computer graphics, computer vision, and visualization. Understanding, describing, and modeling the geometry and behavior of cities are significant challenges that ultimately benefit urban planning and simulation, mapping and visualization, emergency response, and entertainment. In this paper, we have collected and organized research which addresses this multidisciplinary challenge. In particular, we divide research in modeling cities and urban spaces into the areas of geometrical modeling and of behavioral modeling. The first area overlaps significantly with computer graphics and computer vision-our focus is on algorithms that produce intricate geometry quickly from a compact set of specifications (i.e., procedural modeling). The second area of behavioral modeling centers on understanding the underlying socioeconomic, meteorological, and resource consumption/waste production processes occurring within an urban space. Research in urban modeling, even from a computer graphics perspective, must tie the two areas of geometric and behavioral modeling together in order to ensure that useful 3D modeling techniques are developed and are placed within their needed context. In addition, we discuss the growing trend of inverse procedural modeling and some sample urban applications.

MIQP‐based Layout Design for Building Interiors

“…Our problem also relates to urban layout modeling. [Ali12,STBB14] review the topic in a broad context. One common approach generates large-scale layouts by first creating the road network and then by generating parcels.…”

On Improving Urban Environment Representations

“…Another example is GIS, where efforts are focused on extending current tools to incorporate 3D urban modeling, like in the case of Esri's acquisition of City Engine (http://www.esri.com/software/cityengine). A final area where we expect these techniques will have a profound impact is the new discipline of smart cities, where a careful combination of remote sensing technology, planning, networks and modeling tools could completely reshape the way planners conceive their tools (Aliaga, 2012).…”

AAQAL: A Machine Learning-Based Tool for Performance Optimization of Parallel SPMV Computations Using Block CSR

“…The sparse matrix-vector product (SpMV) is considered to be the most important, time-consuming, and widely used scientific kernel [1] in various scientific disciplines such as computer graphics, computer vision [2][3][4][5], robotics [3], 3D or 2D problems [4], acoustic problems [5,6], thermal problems, healthcare, networking, operational research, and computational fluid dynamics (CFD).…”