Search

Accurate cave surveying is crucial for understanding their genesis, current state, and potential hazards, especially in challenging environments marked by limited accessibility and poor visibility. This study applies geomatics techniques, including Terrestrial Laser Scanning (TLS), SLAM-based Mobile Mapping Systems (MMS), and digital photogrammetry, to create three-dimensional models of artificial caves in Gravina in Puglia, Apulia region, southern Italy. The research aims to assess these methodologies' accuracy, reliability, and performance for structural monitoring and hazard assessment. Despite challenges such as rough conditions, limited accessibility and poor visibility, the study reveals promising insights into the capabilities of these techniques for efficient surveying in complex underground environments. While highlighting the potential of MMS for cost-effective and rapid data acquisition, digital photogrammetry using spherical cameras also emerges as a viable alternative, offering comprehensive data collection capabilities with minimal capture time. Further research is warranted to optimize these techniques for enhanced hazard assessment and structural monitoring in challenging underground environments. [ABSTRACT FROM AUTHOR]

In recent decades, photogrammetric and machine learning technologies have become essential for a better understanding of environmental and anthropic issues. The present work aims to respond one of the most topical problems in environmental photogrammetry, i.e., the automatic classification of dense point clouds using the machine learning (ML) technology for the refraction correction on the fluvial water table. The applied methodology for the acquisition of multiple photogrammetric flights was made through UAV drones, also in RTK configuration, for various locations along the Orco River, sited in Piedmont (Italy) and georeferenced with GNSS—RTK topographic method. The authors considered five topographic fluvial cross-sections to set the correction methodology. The automatic classification in ML has found a valid identification of different patterns (Water, Gravel bars, Vegetation, and Ground classes), in specific hydraulic and geomatic conditions. The obtained results about the automatic classification and refraction reduction led us the definition of a new procedure, with precise conditions of validity.

The geomatic survey in the speleological field is one of the main activities that allows for the adding of both a scientific and popular value to cave exploration, and it is of fundamental importance for a detailed knowledge of the hypogean cavity. Today, the available instruments, such as laser scanners and metric cameras, allow us to quickly acquire data and obtain accurate three-dimensional models, but they are still expensive, require a careful planning phase of the survey, as well as some operator experience for their management. This work analyzes the performance of a smartphone device for a close-range photogrammetry approach for the extraction of accurate three-dimensional information of an underground cave. The image datasets that were acquired with a high-end smartphone were processed using the Structure from Motion (SfM)-based approach for dense point cloud generation: different image-matching algorithms implemented in a commercial and an open source software and in a smartphone application were tested. In order to assess the reachable accuracy of the proposed procedure, the achieved results were compared with a reference dense point cloud obtained with a professional camera or a terrestrial laser scanner. The approach has shown a good performance in terms of geometrical accuracies, computational time and applicability. [ABSTRACT FROM AUTHOR]

Every year, there are almost 50,000 forest fires in Europe (127/day), which have burned an area equal to more than 450,000 ha. An effective management of forest fires is therefore fundamental in order to reduce the number of the fires and, especially, the related burned areas, preserving the environment and saving human lives. However, some problems still exist in the structure of information and in the harmonization of data and fire management procedures among different European countries. Pursuing the same interoperability aims, the European Union has invested in the development of the INSPIRE Directive (Infrastructure for Spatial Information in Europe) to support environmental policies. Furthermore, the EU (European Union) is currently working on developing ad hoc infrastructures for the safe management of forests and fires. Moving from this premises and following an analysis of the state of the art of information systems for forest fire-fighting, in the light of the end-user requirements, the paper presents the INSPIRE-compliant design of a geographical information system, implemented using open-source platforms. [ABSTRACT FROM AUTHOR]

According to the Annual Fire Report 2013 (European Commission-Joint Research Centre, 2014), there have been 873 forest fires in Europe, in 2013, for a total of 340559 ha of territory. A comparison of this data to that of the previous years, highlights that, when the intended goal is that of preserving the environment and saving human lives, the importance of the correct management of forest fires can not be underestimated. In the past years, the European Union has invested in the development of the INSPIRE Directive (Infrastructure for Spatial Information in Europe) to support environmental policies. Furthermore, the EU is currently working on developing "ad hoc" infrastructures for the safe management of forests and fires. The AF3 EU project (Advanced Forest Fire Fighting), financed by the FP7, addresses the issue of developing innovative tools to handle all stages of forest fires. The project develops a single control center for the coordination of monitoring, manoeuvring, and post-fire operations. The SDI platform (Spatial Data Infrastructure) represents another component which was designed in the context of this project. It is based on a GIS (Geographic Information System) which is able to efficiently integrate multi-modal data. Following an analysis of the state of the art of information systems for forest fire-fighting, and in light of the end-user requirements analyzed within the AF3 project, we propose a geo-topographic database based on the INSPIRE Directive and developed on opensource platforms, which provides interoperability of the data and allows forecasting and monitoring of high-risk areas, decision making, damage estimation, and post-fire management. [ABSTRACT FROM AUTHOR]