Image-based surface reconstruction in geomorphometry – merits, limits and developments

TLDR

Photogrammetry has been linked to geosciences since the late 19th century, but high costs limited its use to specialists; today commercial and open‑source tools, UAVs, and advances in computing and vision research enable non‑experts to generate 3‑D and 4‑D models from non‑calibrated images using structure‑from‑motion workflows that automatically orient large image sets with robust feature detectors such as SIFT. This review summarizes the state of the art of SfM workflows in geomorphometry, outlines key terms and application fields, and evaluates current accuracies and scales to identify future challenges. The authors emphasize that rigorous fieldwork—proper camera settings, ground control points, and ground truth—must accompany SfM to understand error sources, and they quantify achieved accuracies using various strategies. Lessons learned from prior studies on common errors and bad practices can guide the future use of SfM photogrammetry in geosciences. Abstract.

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.