**4. Experimental results**

We present in this section, the first significant results of the construction of an Urban Augmented Reality (UAR) scene model resulting from the combination of photogrammetry and fuzzy modeling techniques for future analyses. In our


#### **Table 2.**

*Process of image matching in stereovision within FCM framework.*


**Table 3.**

*Image matching in stereovision within FCM framework results.*

incremental validation process, we rely on two major works [64, 87, 93, 95] to assess the performance of our method and its robustness for large-scale deployment.

#### **4.1 The urban augmented reality model scene**

Based on the 744 image pairs, the total number of photographs therefore amounts to 745. The prodigiously increased computing capacities of mobile devices open opportunities for augmented reality applications. The FPAK we developed a enables the conversion of urban built environment photos into Urban Augmented Reality (UAR) model as illustrated with **Figure 6**. To achieve AUR, we use the kernel in conjunction with Apple ARKit [96] and RealityKit [97] frameworks. The use of RealityKit framework let implement high-performance 3D simulation and rendering. It leverages information provided by the ARKit framework to seamlessly integrate virtual urban built environment into the real world. In turn, the kernel mainly focuses on considering the imprecision of blind spots inherent in the overlapping of shots during the acquisition of photographs to be used as raw materials for the work of

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

**Figure 6.** *Experimental UAR model for 3D spatial analysis.*

implementing augmented reality scenes. In addition, it provides a flexible architecture that fosters the development augmented reality applications about research in theoretical and quantitative geography like UAR.

#### **4.2 Datasets and input quality analytics**

The quality of data (accuracy, precision, and resolution) taken by sensors as smartphones is determined by many factors related to both the capture technique and the physical environment. Ideal physical conditions should favor diffused and homogeneous lighting and all protruding urban objects should have enough space around them. In addition, when taking photos, special attention should be paid to the following object/environment characteristics: sufficient texture detail and minimal reflective surfaces.

To select from the entire set of photographic data, the images meeting these criteria as well as those set out in **Table 1**, a valuation of the threshold values (high, and low) based on a fuzzy set as shown in **Figure 7**.

To ensure the quality of the input data, the input image quality sorting process consisted of sifting through the 1028 raw images captured for the entire study area. Indeed, the 800 photos organized in 799 pairs to constitute the input database are the result of the application of this cleaning process. Also, although variations in the quality of photogrammetric data are attributable to factors beyond the control of the operator, several steps can be taken to increase the likelihood that the data collected will achieve the desired quality. The following three points of vigilance are in order: 1) Consider the expected data collection conditions (e.g., weather, lighting), the quality of the camera and the lens. 2) Using the target range and camera specifications, calculate the desired spacing between successive frames to ensure adequate overlap. The interval [0.7–0.9] is the optimum since the value of 0.7 already gives excellent results. 3) After data collection, review and remove any poor quality/blurry images by manual or automatic means.

**Figure 7.** *Fuzzy set assigned to input image quality factor.*
