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We introduce FACA – Fully Automated Co-Alignment, an open-source software designed to fully automate the workflow for co-aligning point clouds derived from Unmanned Aerial Vehicle (UAV) images using photogrammetry. We developed FACA to efficiently evaluate fieldwork with Unmanned Aerial Vehicles (UAVs) on landslides and coastal dynamics. The software applies to any research requiring comparative precise rapid multi-temporal point cloud generation from UAV imagery. UAVs are an essential element in most contemporary applied geosciences research toolkits. Typical products of UAV flights are point clouds created with photogrammetry, to measure objects and their change if multi-temporal data exists. Ground Control Points (GCPs) are considered the best method to increase the precision and accuracy of point clouds, but placing and measuring them is not always feasible during fieldwork. Co-alignment leads to the local precise alignment of multiple point clouds without GCPs. FACA uses Agisoft Metashape Pro and the Python standard library. The GPLv3 licensed FACA source code focuses on extendability, modifiability, and readability. FACA works interchangeably from the command line or a custom graphical user interface. We distribute the software with both usage and installation instructions. Three multi-temporal test datasets are available. We demonstrate the utility and versatility of FACA v1 with a multi-year and -region dataset acquired along Germany's Baltic Sea coast. FACA is in continuous open development. [ABSTRACT FROM AUTHOR]

The eastern slope of Garmaksla, a flat-topped mountain at the western margin of Billefjorden, Svalbard, is affected by mass movements of different types. Rotational rock slides, rock fall and a rock avalanche affecting the coastal cliff are shallow surface expressions covering a larger rock mass instability that is bordered to the west by the Balliolbreen Fault. This structural feature is part of the Billefjorden Fault Zone and accommodated multi-phase deformation since Devonian time. Based on a comprehensive morpho-structural analysis, the mapped surface features and rock slope failures are explained by a compound rock slide model that reveals a litho-structural control on the type and mechanism of slope instability. The Balliolbreen Fault serves as an inherited zone of weakness that is reactivated as the rear rupture surface of the rock slide. In addition, favourably oriented bedding planes and pre-existing fault zones serve as prime conditioning factors for the compound rock slide. A postglacial age of at least 6 ka is derived from 14C dated sediments of Garmaksla Lake, a perennial sag pond along the main scarp. Although the current state of activity of the compound rock slide is unclear, an increase of shallow slope instabilities is expected owing to climate warming. [ABSTRACT FROM AUTHOR]

This paper introduces the Landslide Susceptibility Assessment Tools – Project Manager Suite (LSAT PM), an open-source, easy-to-use software written in Python. Primarily developed to conduct landslide susceptibility analysis (LSA), it is not limited to this issue and applies to any other research dealing with supervised spatial binary classification. LSAT PM provides efficient interactive data management supported by handy tools in a standardized project framework. The application utilizes open standard data formats, ensuring data transferability to all geographic information systems. LSAT PM has a modular structure that allows extending the existing toolkit by additional tools. The LSAT PM v1.0.0b implements heuristic and data-driven methods: analytical hierarchy process, weights of evidence, logistic regression, and artificial neural networks. The software was developed and tested over the years in different projects dealing with landslide susceptibility assessment. The emphasis on model uncertainties and statistical model evaluation makes the software a practical modeling tool to explore and evaluate different native and foreign LSA models. The software distribution package includes comprehensive documentation. A dataset for testing purposes of the software is available. LSAT PM is subject to continuous further development. [ABSTRACT FROM AUTHOR]

This paper introduces the Landslide Susceptibility Assessment Tools - Project Manager Suite (LSAT PM), an open-source, easy-to-use software written in Python. Primarily developed to conduct landslide susceptibility analyses (LSA), it is not limited to this issue and applies to any other research dealing with supervised spatial binary classification. With its standardized project framework, LSAT PM provides efficient interactive data management supported by handy tools. The application utilizes standard data formats ensuring data transferability to all geographic information systems. LSAT PM has a modular structure allowing to extend the existing toolkit by additional analyses. The LSAT PM v1.0.0b implements heuristic and data-driven methods such as the analytical hierarchy process, weights of evidence, logistic regression, and artificial neural networks. The software was developed and tested over the years in different projects dealing with landslide susceptibility assessment. The emphasis on model uncertainties and statistical model evaluation makes the software a practical modeling tool. Also, it provides the possibility to explore and evaluate different LSA models, even those not created with LSAT PM. The software distribution package includes comprehensive documentation. A dataset for testing purposes of the software is available. LSAT PM is subject to continuous further development. [ABSTRACT FROM AUTHOR]