UAV observation of the recent evolution of the Planpincieux Glacier (Mont Blanc – Italy)

2020 ◽  
Author(s):  
Daniele Giordan ◽  
Niccolò Dematteis ◽  
Fabrizio Troilo
Keyword(s):  
Time Lapse ◽  
High Mountain ◽  
Important Data ◽  
Critical Acceleration ◽  
Digital Terrain ◽  
Terrain Models ◽  
Current State ◽  
Frontal Part ◽  
Mountain Environment ◽  
Data Source

<p>Planpincieux is one of the glaciers located on the Italian side of the Mont Blanc (Italy). This glacier is monitored using a permanent monoscopic time-lapse camera since 2013. In 2019, the frontal part of the glacier has been characterized by a critical acceleration that could trigger a large ice avalanche able to reach the underlying Planpincieux village. During the emergency, the working group composed of Fondazione Montagna Sicura, CNR IRPI and the Aosta Valley Region Authority improved the monitoring system with a ground-based SAR to control the glacier evolution. An important data source used for a better understanding of the structure of the more unstable glacier sector has been the acquisition of a sequence of digital terrain models (DTMs) acquired by unmanned aerial vehicles (UAV) and helicopters. The approach adopted for the DTM generation is the acquisition of a photo sequence and the application of the structure from motion algorithm. The investigated area of the glacier is located in high-mountain environment and is characterized by a complex topography that does not facilitate the use of UAV. But the availability of a sequence of DTMs has been very useful for the improvement of the knowledge of the current state and recent evolution of the Planpincieux Glacier.</p>

scholarly journals Propuesta metodológica para la caracterización y tipificación de las costas españolas. Aplicación a las costas de Galicia.

2022 ◽  
Author(s):  
Augusto Pérez-Alberti
Keyword(s):  
Methodological Approach ◽  
Coastal Dunes ◽  
Sandy Beaches ◽  
Technical Problems ◽  
Digital Terrain ◽  
Terrain Models ◽  
Concrete Type ◽  
Management Plans ◽  
Data Source ◽  
Scale Classification

There are several coastal classifications. Most of them have been elaborated worldwide using tectonic, climatic, topographic, or oceanographic criteria. Other classifications have been generated on a larger scale and focused on classifying the coastal forms, as cliffs, beaches, estuaries, lagoons, or dune complexes in different places.This project analyzes the types of coastlines, understanding as such each sector that presents certain topographic conditions marked by the elevation and slope, and that was modeled on a concrete type of rock in a specific climatic and marine environment. This paper describes a methodological approach for a detailed scale classification. This approach based on the delimitation of the different coastal systems, exemplified in cliffs and boulder beaches, sandy beaches, and dunes. In this case the shore platforms, marshes and lagoons have not been considered for the technical problems derived from the LiDAR data source, from which the 2 m spatial resolution digital terrain models (DTM) are derived.The first step in the classification was a manual delimitation combining DTMs and orthophotographs. Subsequently, other typification has been carried out through the automatic creation of Coastal Topographic Units (CTU). This index is the combination of two variables: coastal elevation and slope. The possible integration of others, such as orientation or lithology, is possible, but generate a very high number of units and make it difficult to interpret. For this reason, this study did not consider more variables.In this project 30 CTUs was generated, and then selecting only those that appear in the cliffs, boulder beaches, sandy beaches, and coastal dunes sectors. The possibility of viewing one or several CTUs in any sector of the coast allows to know more accurately the conditions of each sector and these categories could be improve the coastal management plans.

scholarly journals Lidar-Based Morphometry of Conical Hills in Temperate Karst Areas in Slovenia

2021 ◽  
Vol 13 (14) ◽  
pp. 2668
Author(s):  
Tamás Telbisz
Keyword(s):  
Empirical Distribution ◽  
Inverse Gaussian ◽  
Digital Terrain ◽  
Terrain Models ◽  
Starting Point ◽  
Significant Difference ◽  
Karst Areas ◽  
Hill Area ◽  
The Hill ◽  
Comprehensive Study

Conical hills, or residual hills, are frequently mentioned landforms in the context of humid tropical karsts as they are dominant surface elements there. Residual hills are also present in temperate karsts, but generally in a less remarkable way. These landforms have not been thoroughly addressed in the literature to date, therefore the present article is the first attempt to morphometrically characterize temperate zone residual karst hills. We use the methods already developed for doline morphometry, and we apply them to the “inverse” topography using LiDAR-based digital terrain models (DTMs) of three Slovenian sample areas. The characteristics of hills and depressions are analysed in parallel, taking into account the rank of the forms. A common feature of hills and dolines is that, for both types, the empirical distribution of planform areas has a strongly positive skew. After logarithmic transformation, these distributions can be approximated by Inverse Gaussian, Normal, and Weibull distributions. Along with the rank, the planform area and vertical extent of the hills and dolines increase similarly. High circularity is characteristic only of the first-rank forms for both dolines and hills. For the sample areas, the the hill area ratios and the doline area ratios have similar values, but the total extent of the hills is slightly larger in each case. A difference between dolines and hills is that the shapes of hills are more similar to one another than those of dolines. The reason for this is that the larger, closed depressions are created by lateral coalescence, while the hills are residual forms carved from large blocks. Another significant difference is that the density of dolines is much higher than that of hills. This article is intended as a methodological starting point for a new topic, aiming at the comprehensive study of residual karst hills across different climatic areas.

scholarly journals IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning

2021 ◽  
Vol 13 (12) ◽  
pp. 2417
Author(s):  
Savvas Karatsiolis ◽  
Andreas Kamilaris ◽  
Ian Cole
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Surface Model ◽  
Large Area ◽  
Digital Terrain ◽  
Terrain Models ◽  
Architectural Features ◽  
Rgb Images

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.

scholarly journals An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Computational Power ◽  
Specific Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Current State ◽  
Data Source ◽  
Point Cloud Classification ◽  
And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

scholarly journals Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

Water ◽  
2014 ◽  
Vol 6 (2) ◽  
pp. 271-300 ◽  
Author(s):  
Jenni-Mari Vesakoski ◽  
Petteri Alho ◽  
Juha Hyyppä ◽  
Markus Holopainen ◽  
Claude Flener ◽  
...  
Keyword(s):  
Digital Terrain Models ◽  
Digital Terrain ◽  
Terrain Models
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