Structure from Motion (SfM) Photogrammetry is a versatile low-cost technique that proves its efficiency in several applications a couple of decades ago. It became even possible for end-users to produce dense point clouds from high quality images for the desired area following SfM and Multi View Stereo (MVS) algorithms. However, there still some obstacles encounter by photogrammetric users who have limited resources and face difficulty in accessing certain locations to provide these images. Therefore, researchers are directed to examine the feasibility of using alternative data capture methodologies to produce a 3D environment using images captured from Google Earth 3D imagery service following Printgrammetry technique without the need to access the field or use expensive field equipment such as Unmanned aerial Vehicle (UAV). The aim of this work is to assess and examine the level of details and precision of the 3D model extracted from Printgrammetry using data capture methodology from a 4K monitor. This is mainly to report the preliminary quality assurance of this new methodology for future accuracy assessment applications. Different flight plans are applied using multiple flying heights, multi-directional images, double grid settings, and various distributions of GCPs in a homogeneous and spaced manner. The results confirmed that the flying height selected in the flight preparation plan depends mainly on the Level of Details (LOD) required to be delivered for the region, where it is possible to obtain visual high-quality models at lower flight height and with more details. In regard to the data precision level, the modelling and mapping results achieved in this research are found to be acceptable when relied on Google Earth coordinates as a reference for GCPs. However, there are several factors that must be considered in future works when deal with such projects that affect the quality of the final 3D model such as the quality of the image and the skill of the operator in executing outcomes from individual workflow steps.

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