Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model

Abstract

Cities are increasingly looking to become smarter and more resilient. Also, the use of computer vision takes a considerable place in the panoply of techniques and algorithms necessary for the 3D reconstruction of urban built environments. The models thus obtained make it possible to feed the logic of decision support and urban services thanks to the integration of augmented reality. This chapter describes and uses Fuzzy Cognitive Maps (FCM) as computing framework of visual features matching in augmented urban built environment modeling process. It is a combination of the achievements of the theory of fuzzy subsets and photogrammetry according to an algorithmic approach associated with the ARKit renderer. In this experimental research work, part of which is published in this chapter, the study area was confined to a portion of a housing estate and the data acquisition tools are in the domain of the public. The aim is the deployment of the algorithmic process to capture urban environments built in an augmented reality model and compute visual feature in stereovision within FCM framework. The comparison of the results obtained with our approach to two other well-known ones in the field, denotes the increased precision gain with a scalability factor.

Urban Augmented Reality for 3D Geosimulation and Prospective Analysis

“…Accurate and detailed 3D models of the urban environment are essential for realistic and contextually relevant augmentations. Techniques such as laser scanning [12], photogrammetry [12], and CAD modeling enable the creation of high-fidelity 3D models [1,25]. These models capture the geometric and semantic information of buildings, streets, and other urban elements, forming the foundation for precise and visually consistent augmentations [18,22,26].…”

“…Machine learning techniques [14,15,22,24] play a crucial role in augmenting reality. They enable object recognition [12,24], semantic understanding, and real-time tracking. Machine learning algorithms can be trained to recognize and classify urban objects, allowing for intelligent augmentations and predictive analysis [27].…”

“…This technology has already made remarkable strides in entertainment and gaming [8][9][10][11], but its potential for practical applications in urban planning and land use is yet to be fully explored [6,7,9,11]. In this paper, we propose a framework that leverages urban augmented reality models (UARM) [12] to implement 3D geosimulation and prospective analysis in the context of urban built environments.…”