True orthophoto generation using google earth imagery and comparison to UAV orthophoto

Abstract

Extended Abstract
Introduction
A digital orthophoto is a reliable, accurate, and low-cost map for acquiring knowledge, including geolocation, distance, area, and changes in imagery features. It is now considered one of the most widely used and sophisticated digital photogrammetry products. Orthophoto map creation is substantially faster than traditional topographic map production because of the development of powerful algorithms for processing aerial, drone, ground, and satellite imagery. To begin, orthophoto is a result of photogrammetry processing that employs the Digital Terrain Model (DTM), which is commonly observed in classic aerial photogrammetry. In orthophotos, you will frequently notice an effect in which the terrain representation is very accurate, but there is a tilt in the buildings and other tall structures, which is caused by the use of DTM, which only maps the natural shape of the earth, excluding vegetation and all man-made objects and structures. A true orthophoto map provides a vertical view of the earth's surface, eliminating building tilting and providing access to practically any location on the ground. Traditionally, measuring digital surface models has been highly complex and costly. It is generally accomplished through the use of LiDAR or ground measurements. The end result of drone photogrammetry is known as an orthomosaic. In actuality, an orthomosaic is comparable to a true orthophoto (since it is formed using a digital surface model), but it is often not based on a metric camera with accurate focal length and internal dimensions, as they are expensive and not readily accessible for UAVs. Furthermore, orthomosaics may be generated using both nadir and oblique images. Drone-based orthomosaics are created based on the digital surface model rather than as a separate survey like traditional aerial photogrammetry. The DSM is produced by drone photogrammetry based on the 3D point cloud, which is the initial output of data processing.
        Materials & Methods
The huge success of online services like Google Earth, Google Maps, Bing Maps, and so on increased demand for orthophotos, resulting in the development of new algorithms and sensors. It is commonly understood that orthophoto quality is determined by image resolution, camera calibration, orientation accuracy, and DTM accuracy. Because digital cameras produce high-resolution imagery, one of the most important consequences in orthophoto generation is the spatial resolution of the DTM: standing objects, such as buildings, plants, and so on, exhibit radial displacement in the final orthophoto. In practical applications, orthophotos are utilized as small and medium scale maps; updated earth surface maps; three-dimensional urban scene reconstruction; village surveying; land planning; precision agriculture; desertification monitoring; land use surveying; and other sectors. True orthophotos are orthophotos that have been improved to minimize tilt inaccuracy and projection discrepancies. The true orthophoto is exceedingly stringent with the original image; the heading overlap and side overlap are at least 80% and 60% overlap, respectively. Due to the reduction of displacements produced by camera tilt and height difference, the use of orthophoto as a spatial data format with high geometric accuracy has found growing applications in recent years. With the growing relevance of geographic information systems, particularly in metropolitan areas, the use of orthophoto in conjunction with spatial data has grown. Because orthophoto contains correct spatial and textural information about complications, it is feasible to produce virtual reality by integrating it with 3D models, where it is able to properly quantify the height and plane location of complications during 3D viewing. In this research, a novel approach for generating orthophotos from Google Earth imagery for specific purposes was developed and qualitatively and quantitatively compared to orthophotos created from UAV images.
Results and discussion
The result demonstrated the total error of orthomosaic generation from Google Earth imagery and UAV data to be 0.124 and 0.059 m/pixel, respectively. Moreover, the visual findings reveal that the edges of low-height barriers in the orthophoto generated from Google Earth images are superior to those in the orthophoto generated from drone imagery, but the edges of high-height obstacles, particularly those with noticeable shadows, are of poor quality. The findings of statistical parameters in quantitative surveys using randomly selected points in non-building regions revealed that the errors in the orthophoto derived from Google Earth data are 1.10 meters and 1.34 meters in terms of mean error and root mean square error (RMSE), respectively. In addition, the orthophoto generated from UAV data and Google Earth showed a 95% correlation and a 91% determination coefficient. In contrast, in building regions, the average height error and average square root error in the orthophoto generated from Google Earth data compared to UAV data were around 9 meters and 5 meters, respectively. Statistical metrics in these locations also revealed a low correlation of 80% and a determination coefficient of 65%.
Conclusions
In this research, a novel approach for generating orthophotos from Google Earth imagery for specific purposes was developed and qualitatively and quantitatively compared to orthophotos created from UAV images. As a result, as the height of the obstacles and the presence of lengthy shadows increase, so does the inaccuracy of the height component of the orthophoto derived from Google Earth imagery. Therefore, it is advised that orthophotos for special applications, flat regions, and hills be created using Google Earth images. Additionally, Google Earth data offers the following advantages: free of charge; the utilization of historical imagery to generate orthophotos; and nearly four times less processing time and volume.