Welcome to Geo-SfM#
The Geo-SfM course teaches you the basic concepts of Structure-from-Motion (SfM) photogrammetry using the Agisoft Metashape software suite alongside the Python programming language. No previous programming experience is required, though may come in handy for more advanced uses.
Geo-SfM covers the necessary skills needed to conduct SfM photogrammetry in a scientifically sound manner, targeting specifically the geosciences.
Open access
This course is open for everyone to follow online. The aim of the course is to share processing practices amongst the geosciences and to facilitate more efficient and reproducible processing.
Course format and topics#
This course is split in several modules, consisting of interactive lectures walkthroughs, and exercises. The exercises will focus on developing competence in SfM photogrammetr processing for geoscientific purposes, and in particular focusing on the use of Agisoft Metashape. Typical topics cover the operating of Agisoft Metashape, the use of ArUco markers and automated marker detection, and the use of automated Python scripting for automated processing. Furthermore, optimal image acquisition is discussed, as well as the use of SfM for different use cases.
Motivation for the course#
The main part of the first half of the course is to learn how to use the Agisoft Metashape software suite. However, in addition we also try to address the following and help you to learn a number of other skills related to open and reproducible science. These include:
Keeping a log of every decision and change you make
Standardising your approach to ensure your science is reproducible
Archiving raw data, metadata, processing reports
Learning outcomes#
After taking the Geo-SfM course, the participant has been introduced to photogrammetry processing techniques and possesses the skills to independently process and quality assure photogrammetric data sets such as digital elevation models (DEMs) and three-dimensional digital models (e.g., outcrops, hand-sized samples). Important aspects include:
✔️ The participant has acquired a fundamental understanding of the concepts and workings of structure-from-motion photogrammetry.
✔️ The participant understands and can apply georeferencing and spatial alignment.
✔️ The participant can independently process photogrammetry data to produce orthomosaics, 3D and DEM models.
✔️ The participant is familiar with best-practices in data processes and can conduct quality assessment and data validation.
✔️ The participant is able to perform basic data analysis and feature interpretation.
Classroom assignment: teaching idea
Those using this tutorial in teaching are free to assign the following assignment to their students:
Objectives
By the end of the Geo-SfM (https://unisvalbard.github.io/Geo-SfM) module, all groups should be familiar with:
Image acquisition procedures for structure-from-motion processing
Standardised procedures for the processing of digital outcrop and sample models, including metadata templates and archiving procedures.
GitHub, particularly being able to raise issues and have discussions, as well as create pull-requests to contribute to open resources.
Instructions
In pairs of 2, you will be tasked to reconstruct a number of hand-sized items (e.g., small rock samples, common household items, foods and fruits). Use the online Geo-SfM tutorial to aid you in the steps. Each 3D model should be documented with metadata and uploaded to SketchFab. Subsequently, a pull-request (with updates to this file: UNISvalbard/Geo-SfM) must be submitted containing a new entry with their SketchFab IDs alongside the year of publishing and course ID.
Suggestions and or fixes?#
Fig. 1 This page uses Github as a backbone. All it takes is a Github account, which even comes with student benefits. Once connected, feel free to open an issue as per the above - whether suggesting improvements or highlighting bugs. A more detailed explanation is found on the how to contribute page.#