Abstract
Abstract. Photogrammetry and geosciences have been closely linked since the late 19th century due to the acquisition of high-quality 3-D data sets of the environment, but it has so far been restricted to a limited range of remote sensing specialists because of the considerable cost of metric systems for the acquisition and treatment of airborne imagery. Today, a wide range of commercial and open-source software tools enable the generation of 3-D and 4-D models of complex geomorphological features by geoscientists and other non-experts users. In addition, very recent rapid developments in unmanned aerial vehicle (UAV) technology allow for the flexible generation of high-quality aerial surveying and ortho-photography at a relatively low cost.The increasing computing capabilities during the last decade, together with the development of high-performance digital sensors and the important software innovations developed by computer-based vision and visual perception research fields, have extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure-from-motion (SfM) workflows are based upon algorithms for efficient and automatic orientation of large image sets without further data acquisition information, examples including robust feature detectors like the scale-invariant feature transform for 2-D imagery. Nevertheless, the importance of carrying out well-established fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors, still needs to be adapted in the common scientific practice.This review intends not only to summarise the current state of the art on using SfM workflows in geomorphometry but also to give an overview of terms and fields of application. Furthermore, this article aims to quantify already achieved accuracies and used scales, using different strategies in order to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that some lessons learned from former articles, scientific reports and book chapters concerning the identification of common errors or "bad practices" and some other valuable information may help in guiding the future use of SfM photogrammetry in geosciences.
Highlights
Works on projective geometries date back to more than five centuries, when scientists derived coordinates of points from several images and investigated the geometry of perspectives (Doyle, 1964)
Growing computing capacities and rapid developments in computer vision led to the method of structure from motion (SfM), which opened the way for low-cost, highresolution topography
This review aims to highlight the development of SfM photogrammetry as a valuable tool for geoscientists: 1. The method of SfM photogrammetry is briefly summarised, and algorithmic differences due to their emergence from computer vision as well as photogrammetry are clarified (Sect. 2)
Summary
Works on projective geometries date back to more than five centuries, when scientists derived coordinates of points from several images and investigated the geometry of perspectives (Doyle, 1964). Photogrammetry has rapidly advanced to be an essential tool in geosciences during the last two decades and has lately been gaining momentum driven by digital sensors leading to flexible, fast and facile generation of images. SfM photogrammetry can be performed with images acquired by consumer-grade digital cameras and is very flexible in its implementation. The diversity of possible applications led to a variety of terms used to describe SfM photogrammetry either from a photogrammetric or a computer vision standpoint. In this review a series of studies that utilise the algorithmic advance of high automation in SfM are considered – i.e. no initial estimates of the image network geometry or user interactions to generate initial estimates are needed. Some parameter settings typical for photogrammetric tools (e.g. camera calibration values) can be applied to optimise both accuracy and precision, and ground control point (GCP) or scale identification is still necessary
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