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Natural disasters and human interference have endangered heritage structures around the world. Therefore, 3D modeling of buildings is important for historical preservation, particularly in low-income and war-affected countries. The majority of 3D structure surveying acquisition approaches, terrestrial laser scanning (TLS), total station measurements, or traditional photogrammetry require either high-cost technologies or professional user supervision. Structure from motion (SfM) approaches address both of these issues by allowing a non-expert user to produce a dense point cloud for real structures by taking a few 2D photographs with a digital camera and processing them with highly automated and freely available data processing tools. The state of the art for the SfM technique is presented in this paper. Agisoft Metashape, VisualSFM, and Regard3D, three well-known types of SfM software, were examined and compared. The 3D point cloud was scaled and transformed into a local coordinates system using total station instruments that were used to obtain some ground control points (GCPs). Ninety-six 2D digital photographs for the historical Emara Palace in Najran, Saudi Arabia, were obtained as data input, and the image matching, bundle adjustment (BA), completeness, and accuracy of three used packages were calculated and compared.

The fully automated Structure-from-Motion approach for developing digital elevation models and orthomosaics has been known and used in photogrammetry for at least 15 years. Years of practice and experience have allowed researchers to provide a solid description of the applicability and limitations of this method. That being said, the impact of input processing parameters in software on the quality of photogrammetric products has yet to be fully ascertained empirically. This study is aimed at identifying the most advantageous processing workflow to fill this research gap by testing 375 different setup variations in the Agisoft Metashape software for the same set of images acquired using an unmanned aerial vehicle in a proglacial area. The purpose of the experiment was to determine three workflows: (1) the fastest, which has the shortest calculation time; (2) the best quality, which is as accurate as possible, regardless of the time taken for the calculations; and (3) the optimal, which is a compromise between accuracy and calculation time. Each of the 375 processing setup variations was assessed based on final product accuracy, i.e., orthomosaics and digital elevation models. The three workflows were selected based on calculating the height differences between the digital elevation models and the control points that did not participate in their georeferencing. The analyses of the root mean square errors (RMSE) and standard deviations indicate that excluding some of the optimization parameters during the camera optimization stage results in high RMSE and an increase in the values of standard deviation errors. Furthermore, it was shown that increasing the detail of individual processing steps in software does not always positively affect the accuracy of the resulting models. The experiment resulted in the development of three different workflows in the form of Python scripts for Agisoft Metashape software, which will help users to process image sets efficiently in the context of earth surface dynamics studies.

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include (1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and (2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.