Programme détaillé - 22 juin

It is possible to attend on line (registration required)

08:30-09:15 It's about time... to observe surface dynamics in 4D point clouds

Katharina Anders 3DGeo Research Group, Institute of Geography, Heidelberg University

Near-continuous time series of point clouds are becoming increasingly available for the observation of surface dynamics in natural landscapes. With these enormous 4D datasets (3D + time), challenges arise for (automatic) analysis, as the timing, duration, and magnitude of changes are often highly variable, and surface activities coincide or overlap in landscape dynamics. The talk features different use cases of 4D observation and puts forward approaches to incorporate the full temporal information in change analysis for the extraction and characterization of surface activity.

09:15-10:00 Presentation of the CloudCompare project and its latest developments

Daniel Girardeau-Montaut CloudCompare project

CloudCompare is an open source software for visualizing and processing point clouds and other 3D entities. We will present the project, its history, its main applications, with a highlight on the newest features introduced in the 2.12 version.

10:15-11:00 Measuring Change at the Earth’s Surface with Topographic Differencing

Chelsea Scott Arizona State University

In this presentation, I will discuss the methodology and applications of vertical and 3D differencing of high resolution topography. Topographic differencing measures landscape change from river erosion, urban growth, coastal processes, earthquakes, volcanic eruptions, and landslides among other events. Vertical differencing is the raster subtraction of multi-temporal topographic data. 3D differencing resolves horizontal and vertical displacements by registering topographic point clouds with a rigid deformation. I will discuss OpenTopography’s (https://opentopography.org) implementation of on-demand topographic differencing and specifically the standardization of the differencing workflow. I will show applications of differencing including fault creep rates along the Central San Andreas fault and state-wide topographic differencing applied to Indiana.

11:00-11:45 Antonio Abellan

Antonio Abellan crealp

Antonio Abellan is the director of the Research Center on the Alpine Environment (CREALP) in Valais. Prior to his current role, he was involved in several academic positions in world leading universities, including an assistant professorship in Engineering Geology & Geohazars in the UK (University of Leeds), a research fellow position at the Scott Polar Research Institute (University of Cambridge), a post-doc at the Risk Analysis Group (University of Lausanne), adjunct assistant professorship in the department of Geological Sciences and Geological Engineering (Queens University) and guest professorship at Eichstätt University.

13:15-14:00 Glacier mass change observations with remote sensing

Fanny Brun Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France

Glaciers are icons of climate and their global retreat is seen by a large audience as one of the most visible consequence of climate change. As glaciologists, we aim at quantifying the glacier mass changes at variable spatial and temporal scales. For the Cloud 2022 Summer school, I will review two aspects of glacier mass change quantification.
First, I will present how we use low resolution satellite imagery to calculate glacier mass balance for the period 2000-present. We use time series of digital elevation models (DEMs) that cover almost all glaciers and ice caps (excluding Greenland and Antarctica ice sheets). The presentation will focus on the methodological developments, but I will also show some important results that are largely included in the Assessment Report 6 of IPCC.
Second, I will present studies that investigated small scale (i.e. at the scale of a single glacier) changes of glacier surface. I will focus on the ice cliffs that are present on debris cover glaciers and show how 3D models derived from terrestrial and UAV photogrammetry can help resolve the contribution of these cliffs to glacier mass loss.

14:00-14:30 Deep learning based 3D point clouds change detection: an application to cliffs dynamics

Iris de Gélis Magellium, F-31000, Toulouse, France IRISA UMR 6074, Université Bretagne Sud, F-56000 Vannes, France CNES

Mainly depending on their lithology, coastal cliffs are prone to changes due to erosion. This erosion could increase due to climate change leading to potential threats for coastal users, assets, or infrastructure. Thus, it is important to be able to understand and characterize cliff face changes at fine scale. Usually, monitoring is conducted thanks to distance computation and manual analysis of each cliff face over 3D point clouds to be able to study 3D dynamics of cliffs. This is time consuming and inclined to each one judgment in particular when dealing with 3D point clouds data. Indeed, 3D point clouds characteristics (sparsity, impossibility of working on a classical top view representation, volume of data, ...) make their processing harder than 2D images. Last decades, an increase of performance of machine learning methods for earth observation purposes has been performed. A presentation of Siamese KPConv network, a deep learning based architecture taking as input raw 3D point clouds will be presented with an application to coastal cliffs change retrieval.

14:45-15:30 Estimating dense 3D displacement vector fields for point cloud-based landslide monitoring

Zan Gojcic Nvidia

Areal deformation monitoring based on point clouds can be a very valuable alternative to the established point-based monitoring techniques, especially for deformation monitoring of natural scenes. However, established deformation analysis approaches for point clouds do not necessarily expose the true 3D changes, because the correspondence between points is typically established naïvely. In this talk, I will discuss the challenges of point cloud-based deformation analysis of natural scenes and touch upon the shortcomings of the traditional method. Deriving from these challenges, I will present a novel fully automated deformation analysis pipeline capable of estimating real 3D displacement vectors from point cloud data. Different from the traditional methods that establish displacements based on the proximity in the Euclidean space, our approach estimates dense 3D displacement vector fields by searching for corresponding points across the epochs in the space of 3D local feature descriptors. Due to this formulation, our method is also sensitive to motion and deformations that occur parallel to the underlying surface.

15:30-16:15 Point clouds in teaching: resources and strategies

Beth Pratt-Sitaula UNAVCO

Beth Pratt-Sitaula is a Science Education Project Manager for UNAVCO, Research Associate at Central Washington University (CWU), and Academic Director for SIT’s Nepal: Geoscience in the Himalaya. She has extensive experience with geoscience and geohazard education. With UNAVCO she is the project manager for GEodesy Tools for Societal Issues project. With CWU, she has been a PI on Cascadia EarthScope Earthquake and Tsunami Education Program (CEETEP) and EarthScope Alaska Native Geoscience Learning Experience (ANGLE).