Structure from motion photogrammetry in physical geography

Abstract

Accurate, precise and rapid acquisition of topographic data is fundamental to many sub-disciplines of physical geography. Technological developments over the past few decades have made fully distributed data sets of centimetric resolution and accuracy commonplace, yet the emergence of Structure from Motion (SfM) with Multi-View Stereo (MVS) in recent years has revolutionised three-dimensional topographic surveys in physical geography by democratising data collection and processing. SfM-MVS originates from the fields of computer vision and photogrammetry, requires minimal expensive equipment or specialist expertise and, under certain conditions, can produce point clouds of comparable quality to existing survey methods (e.g. Terrestrial Laser Scanning). Consequently, applications of SfM-MVS in physical geography have multiplied rapidly. There are many practical options available to physical geographers when planning a SfM-MVS survey (e.g. platforms, cameras, software), yet, many SfM-MVS end-users are uncertain as to the errors associated with each choice and, perhaps most fundamentally, the processes actually taking place as part of the SfM-MVS workflow. This paper details the typical workflow applied by SfM-MVS software packages, reviews practical details of implementing SfM-MVS, combines existing validation studies to assess practically achievable data quality and reviews the range of applications of SfM-MVS in physical geography. The flexibility of the SfM-MVS approach complicates attempts to validate SfM-MVS robustly as each individual validation study will use a different approach (e.g. platform, camera, georeferencing method, etc.). We highlight the need for greater transparency in SfM-MVS processing and enhanced ability to adjust parameters that determine survey quality. Looking forwards, future prospects of SfM-MVS in physical geography are identified through discussion of more recent developments in the fields of image analysis and computer vision.