Printgrammetry—3-D Model Acquisition Methodology From Google Earth Imagery Data

Abstract

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.

Research on Construction of Natural Resources Three-Dimensional Spatio-Temporal Database System

“…With the progression of digital twin and metaverse technologies, tech giants epitomized by Google have spearheaded innovative research in this domain, striving to formulate high-fidelity urban and indoor/outdoor 3D models via sophisticated image-matching algorithms, and subsequently offering these services on commercial platforms like Google Earth (Horota et al 2020).…”

Identifying Urban Building Function by Integrating Remote Sensing Imagery and POI Data

“…More recently, plenty of virtual global platforms, such as Google Earth, NASA World Wind, and Bing Maps, have opened a new era of digital earth [14,15]. For instance, the high resolution Google earth images, as free and open access data source, have become one of the primary supplementary date source of the traditional land use mapping [16,17]. In specific, the richness in texture, tone and geometric features of Google earth images provide detailed information for land use classification at object level, including buildings and other artificial structures [18,19].…”