Printgrammetry—3-D Model Acquisition Methodology From Google Earth Imagery Data - Rafael Kenji Horota; Alysson Soares Aires; Ademir Marques; Pedro Rossa; Eniuce Menezes de Souza; Luiz Gonzaga; Mauricio Roberto Veronez (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020)
This article proposes a technique named Printgrammetry, a structured workflow that allows the extraction of 3-D models from Google Earth platform through the combination of image captures from the screen monitor with structure from motion algorithms. This technique was developed to help geologists and other geoscientists in acquiring 3-D photo-realistic models of outcrops and natural landscapes of big proportions without the need of field mapping and expensive equipment. The methodology is detailed aiming to permit easy reproducibility and focused on achieving the highest resolution possible by working with the best images that the platform can provide. The results have shown that it is possible to obtain visually high-quality models from natural landscapes from Google Earth by acquiring images at high level of detail regions of the software, using a 4K monitor, multidirectional screenshots, and by marking homogeneously spaced targets for georeferencing and scaling. The geometric quality assessment performed using light detection and ranging ground truth data as comparison shows that the Printgrammetry dense point clouds have reached 98.1% of the total covered area under 5 m of distance for the Half Dome case study and 96.7% for the Raplee Ridge case study. The generated 3-D models were then visualized and interacted through an immersive virtual reality software that allowed geologists to manipulate this virtual field environment in different scales. This technique is considered by the authors to have a promising potential for research, industrial, and educational projects that do not require high-precision models.