**3. Materials and methods**

Physical based rendering 3D simulation of large-scale urban built environment processes is one of greatest challenges of modern computing techniques in urban

*Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented… DOI: http://dx.doi.org/10.5772/intechopen.110551*

studies and regional planning [17, 39, 47, 50]. In fact, urban systems are naturally complex by own [1–5, 14]. Simulation allows us to understand the reasons and effects of events and situations in a real system. Moreover, it allows us to predict the results of actions on future states of the system. The level of detail [15, 39, 45, 50] between simulation results and real system behavior depends on the model used. More-precise models with large data may reflect reality in a more-precise manner; however, the complexity directly influences the time required to compute model changes.

#### **3.1 Urban study area**

The model created in this study covers a portion of a new housing estate under construction in the town of Belfort in France. The area of the development project for building individual houses has 25 plots of 600 to 900 m<sup>2</sup> . **Figure 2** provides an overview of the area called "Jardins du MONT".

Indeed, it is a real estate program whose architecture of the houses is contemporary, of high quality and of superior range located less than 10 minutes by car, bus, or bike from the city center of Belfort. We are also less than 10 minutes' walk from the heart of the "Techn'Hom" business park (GE, Alstom...), one of the economic lungs of the town, with an exceptional view of Belfort, its fortifications and the surroundings, all integrated in a green, calm and privileged urban setting.

The general framework of our research work includes 3D spatial analysis, the temporal evolution of new housing estates and the deployment of smart cities, with scientific tools in artificial intelligence. Also, it seemed legitimate to us to take an interest in this portion of the city under construction to experiment with our approach which is the subject of this chapter: create an augmented reality scene model of the built environment through the combination of photogrammetry [76–81] and fuzzy modeling techniques.

#### **3.2 Sensor for data acquisition**

In addition to the question of costs, the spatial scale of the data to be collected as well as the expected quality dictate the choice of tools to be preferred. There are

**Figure 2.** *Urban study area "Jardins du MONT", Belfort (France).* several tools for Geodata collection [38, 49]: Total Stations, Global Navigation Satellite System (GNSS) receivers, Light Detection and Ranging (LiDAR) scanner, Static Terrestrial Laser Scanning (STLS), Airborne Laser Scanning (ALS), Helicopter Laser Scanning (HLS), Mobile Laser Scanning (MLS), Drone, Tablets and Smartphones. As part of this experimental study, we use portable and mobile sensor which smartphones are equipped with. And for good reason, the sensors of these modern devices perfectly meet the requirements relating to the acquisition of data for photogrammetry [82]. Range (or depth) data is crucial for understanding and working with the 3D scene projected onto a 2D plane forming an image. There are multiple ways to obtain such information [83–87], either using a depth sensor or estimating depth. A depth sensor is a device that provides the distance from the sensor to an element in the scene, although it is possible to collect distance information using two or more RGB cameras from a scene.

Due to its following features: wide color capture for photos and live photos, lens correction, retina flash, auto image stabilization, burst mode, etc. we used the iPhone 13 Pro Max as a sensor for acquiring images to feed the model. **Figure 3** illustrates it.
