# Orthofacade-Based Assisted Inspection Method for Buildings
## 4 Discussion  
The efficiency, reliability, accuracy and objectivity of the automated building facade inspection process depend on the ability of the applied model to identify, locate and quantify damage on images acquired by the UAV. Most related studies have focused on crack detection only, without considering the geometric and spatial parameters of the detected crack, which represent key information for damage mapping and quantification of facade degradation. The paper proposed the orthofacade-based assisted inspection method--AIM; for crack detection and determination of location and geometrical parameters of detected crack. The AIM enables automatic crack detection, performed via computer code using open-source image-based methods. The model is defined by control points allowing the crack to be precisely located. By applying simple mathematical operations, it is possible to measure a detected crack based on the coordinates of its points. The method gives the user the ability to adjust the desired level of precision by defining a GSD that should not exceed half the dimension of the smallest object to be identified in the orthofacade. The data source used for facade crack inspection contributes to simplifying the facade assessment process. The AIM performs detection on a single high-quality orthomosaic image of the entire facade--an orthofacade, generated from a large number of individual overlapping facade images collected with UAV from close range. Generated in an orthofrectification-based process, performed by photogrammetric software, the resulting orthofacade is geometrically corrected (\"orthorectified\") and can be used to measure the true distances of features within the photograph. Additionally, by adding a facade control points obtained with the total station, the generated orthofacade is georeferenced, which eliminates the need for traditional descriptive crack locating and allows highly precise identification of crack location. In other studies, the automatic detection of cracks from UAV images is mainly based on individual images or less often on one large, stitched facade image. Compared to the orthomosaic generation--applicable for large datasets, the image stitching method works well only if the area of interest is perfectly flat and therefore is recommended only for small datasets [17]. Other research [16] used the data from UAV sensors for crack location, but without control points, UAV images do not provide sufficient accuracy. The proposed method was tested and validated through a case study conducted on a high-rise building facade. The presented results demonstrate that crack inspection using the AIM can be carried out efficiently, objectively and safely, while the reliability and accuracy of the final results are conditioned by the resolution and geometric accuracy of the orthofacade generated within the process. The quality of the generated orthofacade meets the requirements of the case study where the proposed methodology is validated. The applied AIM computer code successfully detected a crack in the orthofacade image. The use of control points enables highly precise determination of the crack location, with accuracy on the sub-centimetre level and very accurate quantification of geometric parameters of the detected crack. Contemporary building survey and condition assessment, science and practice demand a multidisciplinary approach, compiling and combining expert knowledge and decision making based on architecture, civil engineering, geodesy, material science and applied computer science. Despite all the efforts of the international scientific community to develop an automated building inspection framework, based on the combined application of different technologies for accurate data acquisition and reliable data analysis, introducing these technologies into the everyday practice of building facade assessment remains a challenge.