DOI: 10.82522/5g4b-5s47
title: Dense point cloud of the Marmolada Glacier: alignment of Pre-Detachment (20220702) and Post-Detachment (20220708) 

description: Using data acquired from two independent commercial drone surveys, we generated high-resolution digital models of the Marmolada Glacier before and after the collapse event of July 3, 2022, in northeastern Italy, which resulted in the tragic loss of eleven lives.

We thank the Civil Protection Department of the Autonomous Province of Trento (Gruppo UAS Dipartimento Protezione Civile, Foreste e Fauna; PAT Servizio Antincendi e Protezione Civile) for providing the videos recorded in July 2022, and Dr. Ing. Cristian Ferrari, President of SAT (Società Alpinisti Tridentini), for the video recorded on July 2, 2022, the day before the collapse.
description: Pre- and post-event geolocalised 3D RGB metric models were used to generate two DEMs and two orthophotos: 
i) a video acquired with a DJI commercial drone, in which geolocation metadata are not included, 
ii) 2 acquisitions performed with DJI L1 Lidar in colorimetric mode, that include also acquisition of RGB images through 20 MP sensor and GPS data (WGS 84 reference, EPSG: 4326).
The post-event images were acquired in automatic mode fly with parallel transect waypoints and have georeferencing references, while the frames of the pre-event dataset are extracted from an .mp4 video acquired in manual mode.
The software used for the 3D model generation phases is Agisoft Metashape, hereafter AM, version 1.8.4 build 14856. The hardware used has the following characteristics:
OS: Windows 64 bit
RAM: 31.69 GB
CPU(s): Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
CPU(s): Number 2 NVIDIA GeForce RTX 2070 SUPER
As a first step, it was necessary to extract the individual frames transformed into JPEG images from the pre-event video. After importing the .mp4 file into the METASHAPE software (AM), a decoding script to export 105 frames, each one with a size of 2688 x 1512 without georeferencing.
As second step, two working CHUNKs were generated for the pre- and post-event photos respectively by AM software.
For each dataset, processing was performed to obtain a three-dimensional model using the Structure from motion (SfM) technique. In figure 1, the theorical position estimated by SfM for each data set characterized by black dots. In particular, the figure on the left-hand side represents pre-event manual flight while that on the right-hand side displays the automatic post-event flight.
The first phase of the calculation, SfM builds a two scattered original colour cloud of points that represent a spatial 3D model of the photographed objects. In the next step, for each data set, SfM makes it denser point-cloud. This processing allowed us to obtain two dense-clouds (16,544,671 points pre-event; 118,913,569 points post-event): the pre-event dense-clouds is dimensionless, but geometrically correct while the post-event is dimensional and geo-referenced. 
With the aim of superimposing the two point-clouds, it was necessary to make the first cloud, the pre-event cloud, metric and geo-referenced. 

Therefore the following steps were performed:
1. 	Identified, selected and saved 8 markers, called GCP (Ground Control Points), that are fixed in the post-event point-cloud and on the pre-event frame. These GCP were chosen because did not undergo mechanical displacement pre- and post-event;
2. GCPs were placed in the CHUNCK of the pre-event cloud. For each frame only the pixels corresponding to the individual GCPs were chosen and selected;
3.	 The camera alignment for the pre-event dataset was performed again in order to obtain a georeferenced dense-clouds.

After that, for each CHUNK,  the orthomosaic was build with the source images/frame. The orthomosaic (GEOTIFF format) and the dense point cloud (.LAS format) were exported for each dataset. 
The Lidar360 software was used to check the two dense-clouds, where it is possible to manipulate, classify and visualise point clouds. Two dense-clouds, in .LAS format, were imported into the Lidar360 software. Transverse and longitudinal sections were carried out with the aim of verifying the correct geometric alignment of the two dense-clouds, excluding potential distortions resulting from the SfM algorithm. 
Finally, it was possible to generate the DSM for each dense-clouds by Lidar360.