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31 results for “Automated processing”
PnP module: multi-material components manufacturing by Automated Tape Laying process
<p><strong>Introduction</strong></p> <p>The Automated Tape Laying (ATL) process is an automated technique used for composites manufacturing based on fiber placement processes. This module is part of AIMEN Technology Centre Open Pilot Line focusing on manufacturing of multi-material components. This module is composed by a movement system (robot) and heating system (ATL head), which can be composed by IR system or laser source. </p> <p><strong>Asset Administration Shell</strong></p> <p>The Asset Administration Shell (AAS) modelling follows the <em>Product</em>, <em>Process</em> and <em>Resources</em> (PPR) model. The relation between the assets allows the traceability of the Product by demonstrating a Digital Thread based on AAS and how the active AAS modelling allows the Plug and Produce capabilities in a modular production scheme.</p> <p>In this repository some examples of AAS modeling (.aasx files) for a subset of assets in the shop floor (Resources), Product and Process can be found, as well as the architecture of the whole module.</p> <p><strong>Architecture</strong></p> <p>The information gathered by the central unit/industrial PC (Operational Technology) will be available in DIMOFAC platform (Information Technology) as well as the Product information related to the design and/or simulation (Engineering Technology). In the central unit the software in charge of taking the decision and allowing Plung and Produce capabilities is named “Orchestrator”, and in the product side, the software in charge of register all the information related with a specific software “Digital Thread”.</p> <p> </p> <p><strong>AAS Demonstration</strong></p> <p>A demonstration video is available: <a href="https://www.youtube.com/watch?v=aOP6QWiF5FE&t=7s">PnP module: multi-material components manufacturing by Automated Tape Laying process - YouTube</a></p>
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process - Datasets
<p>This dataset contains supporting data for a research project aimed at analysing herbarium samples from the New England area at a large scale with deep learning techniques. Details on the methodology are shared in the acompanying paper (to be published).</p> <p>Content:</p> <ul> <li>dataset600k_withAI.csv : A dataset of over 600.000 herbarium samples with its record metadata and a corresponding AI phenological annotations with matching confidence scores. The entirety of the record headers are provided, extracted directly from the NEVP portal. In addition, the AI labels are defined by the following headers. These 8 columns represent 4 binary classifiers with the Presence/Absence of each 4 traits and corresponding confidence (as a percentage - presence/absence percentages sum to 1).<br> <ul> <li> <table> <tbody> <tr> <td>Flowering</td> <td>Not Flowering</td> <td>Budding</td> <td>Not Budding</td> <td>Fruiting</td> <td>Not Fruiting</td> <td>Reproductive</td> <td>Not Reproductive</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li>data_species_with_statuses.csv: A processed dataset summarizing flowering period shift at a species level. Two types of headers are provided. <ul> <li>First metadata concerning the flowering shift and the data used to compute that value: <ul> <li> <table> <tbody> <tr> <td>genus</td> <td>genus_species</td> <td>slope</td> <td>nb_specimens</td> <td>p_value_significance</td> <td>trend_category</td> </tr> <tr> <td>Genus of the species</td> <td>Binomial name of the species</td> <td>Regression slope defining the flowering shift as a slope</td> <td>Number of herbarium specimens used to compute the shift</td> <td>P-value significance of the slope being non-zero. ('Non Significant'/'Significant')</td> <td>Summary of the shift as a binary characteristic ('Earlier'/'Later')</td> </tr> </tbody> </table> </li> </ul> </li> <li>Second, metadata summarizing various traits associated to each species: <ul> <li> <table> <tbody> <tr> <td>lifeform_status</td> <td>native_introduced_status</td> <td>wetland_status</td> <td>seasonality_average</td> <td>seasonality_spread</td> </tr> <tr> <td>Growth form from the USDA PLANTS Database. 'Forb_Herb', 'Shrub_Tree' or 'Vine'</td> <td>'Native'/'Introduced' status from the USDA PLANTS Database.</td> <td> <p>National Wetland Plant List (NWPL) Wetland Indicator Status within the Northcentral and Northeast Region</p> <p>'OBL'/'FACW'/'FAC'/'FACU'/'UPL'</p> </td> <td>A characteristic of the flowering season of the species based on the mean Day of Year of the analysed specimens: if <=180: 'Early', else 'Late'</td> <td>A characteristic of the flowering season of the species based on the spread of the flowering season. Less than 28 days: 'Narrow', larger: 'Large'.</td> </tr> </tbody> </table> <p> </p> </li> </ul> </li> </ul> </li> <li>phylogenetic_tree.tre: The raw data used to generate the visualization of the flowering seasonality character and the detected flowering shift foreach species on a phylogenetic tree.</li> <li>phylogenetic_processed_dataset.csv: The processed dataset resuting from the phylogenetic signal analysis. For each trait, an associated significance binary value is provided.</li> </ul>
How to Build an Image Processing Pipeline for Automating Multiparameter Histocytometry Analysis
<p>Image files for evaluation of an upcoming Current Protocols submission, as well as associated reference files.</p>
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 1. Examples for Today Well Manageable and not yet Well Manageable Processes in Automation
<p>Despite the many progresses that have been achieved in automation and AI over the last decades, applications have mainly been successful for circumscribed, well-defined tasks in environments that are relatively specific, well-structured, and characterized by a limited number of possible occurring processes, states, and ways how to react to them [59]. There, technical solutions can even exceed certain human capabilities. The situation changes however if we switch to systems that should perform a broader range of tasks in less well-structured environments. Here, the limits of technical feasibility are being stretched to the utmost [7]. This fact is probably best illustrated by<br> the following two concrete examples in Figure 1.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 18. Different Modules involved in the Autonomous Decision-Making Process
<p>The basic functioning of this architecture is now described in the following step by step<br> using Figure 18a-f, where always the relevant modules of the model are highlighted for better<br> comprehension.</p>
Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>This paper mainly focuses on automated detection of White Matter Lesions of brain using<br> fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the<br> representation of an image into a meaningful image and makes it easier to analyze. As a first step,<br> MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient<br> image enhancement techniques. The pre-processed image is subjected to clustering. The clustering<br> algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC)<br> and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to<br> initialization and are easily trapped in local optima. In order to obtain an optimized result, the<br> clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic<br> global optimization tool which is used in many optimization problems. Figure 2 represents overall<br> process of automatic detection of WMLs of brain. Since MS lesions present different characteristics<br> from lesions in elderly individuals there are many clustering models to determine the accuracy but<br> those methods are not directly applicable to predict the accurate lesions because of the decreased<br> contrast between White Matter and Grey Matter in elderly people. The proposed clustering models<br> are derived by extending the objective functions of FCM and Possibilistic clustering with a<br> Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and<br> is shown to be more robust to noise and other artifacts than competing approaches.</p>
Dataset for Perspectives on Open Science and The Future of Scholarly Communication: Internet Trackers, Algorithmic Persuasion and Robotic Process Automation
<p>This data set was created between 01-04.2021, to study the current landscape of using web trackers in scholarly communication. The data set is part of an article (manuscript) that is intended to be published under the this title: Perspectives on Open Science and The Future of Scholarly Communication: Internet Trackers, Algorithmic Persuasion and Robotic Process Automation.</p>
[SZESLR] Semi-automated Systematic Literature Review (SLR) Design and P-graph (Process Graph Theory) SLR Database
<p>(c) Széchenyi István University and Artificial Intelligence National Laboratory (MILAB)</p> <p>A proposed semi-automated Systematic Literature Review (SLR) design has been deployed to conduct an exhaustive literature review on contributions concerning applications of the P-graph framework as a case study. The P-graph framework (<a href="https://p-graph.org/">https://p-graph.org</a>) was initially conceived for solving problems related to the synthesis of chemical processes; in particular, the framework has been useful for developing sustainable and resilient systems.</p> <p>The current contribution includes the P-graph SLR database itself, and additionally, the Python scripts used to generate such results. The case study has been selected in view of the authors’ familiarity with the P-graph framework, and because they maintain a manually-collected database of works on the subject. The contributions concerning the framework are easily distinguishable from others involving similar methodologies. It was observed that the implementation of the proposed SLR design extended prior review papers on the P-graph framework and provided additional information on authors and institutions devoted to research on this subject.</p> <p>Generally, it is expected that the dataset will assist researchers, scholars, and practitioners concerned with industrial sustainability in streamlining their literature reviews, thus accelerating the acquisition of pertinent publications that could advance and enrich their own research works on such a subject. Moreover, it is expected that, with proper adaptation, the proposed semi-automated SLR design can be applied to conduct literature reviews in other scientific fields whose results can be validated against existing reviews when available.</p> <p>The normalized P-graph database hereby presented thus can be used as a source of validated information for researchers. The P-graph database was uploaded in various file formats that support database management software (e.g. Zotero .RIS) as well as an "EasyHandle" MS Office (.XLSX) file.</p>
Raw, processed and merged Data for Swiss Cat+ East A1 project related to the automated and high-throughput Bayesian Optimization of CO2 hydrogenation heterogeneous catalysts
<p> All files generated during the fully digitalized automated and high-throughput experimentally-guided Bayesian Optimization project, which led to the synthesis of 144 heterogeneous catalysts with a Chemspeed unit (6 generations of 24) and their testing under CO2 hydrogenation conditions with Avantium fixed bed units. Below are some indication to understand the naming of the files.</p> <ul> <li>A1 stands for the internal project number.</li> <li>G1 to G5 stands for the catalyst generation number and G2NC for the alternative second generation suggested by the Bayesian Optimizer without considering the cost of catalyst as an objective (No_Cost).</li> <li>Three fixed bed units have been used, named XDB4x (a 4 parallel reactors unit), XDC4x (another 4 parallel reactors unit) and XR16x (a 16 parallel reactors unit).</li> <li>Individual fixed bed testing raw files (FB_RawData) generated by each unit are then processed to extract the mean and standard deviation (std) values (e.g conversion, selectivity) and to compute reactions rates.</li> <li>Then the processed files for each individual reactor (XDB, XDC, XR) are combined into one file (All_FBData), and finally aggregated with the synthesis details, viathe catalyst barcodes (AllData_Processed).</li> <li>Finally, the processed file for each generation are merged together (AllGen_Merged) and a condensed file is generated for a given reaction temperature (AllGen_275CDataProcessed_Merged)</li> </ul>
LipidQuant 1.0: Automated data processing in lipid class separation - mass spectrometry quantitative workflows
Open the record for dataset details and reuse information.
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
An automated workflow for parallel processing of large multiview SPIM recordings
<p>Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on <em>snakemake</em> to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it.</p>
Aerosol products presented in "ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications"
<p>ALICENET output products on aerosol optical and physical properties and vertical layering presented in “Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: Alicenet – An Italian network of Automated Lidar-Ceilometers for 4D aerosol monitoring: infrastructure, data processing, and applications, AMT, https://doi.org/10.5194/egusphere-2024-730, 2024”.</p> <p>The aod*.txt files include the following information:</p> <p>- date: date in UTC<br>- AOD_ALICENET: AOD as retrieved by ALICENET at 1064 nm<br>- AOD_AERONET/SKYNET: AOD measured by a co-located photometer from AERONET/SKYNET (level 2) at 1020 nm<br>- AE: Angstrom Exponent from AERONET/SKYNET (level 2)</p> <p>The contiunous.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- continuous_aerosol_layer: Continous Aerosol Layer heights as retrieved by ALICENET</p> <p>The mixed.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- mixed_aerosol_layer: Mixed Aerosol Layer heights as retrieved by ALICENET</p> <p>This work received partial financial support from the EC H2020 Project RI-URBANS (GA No 101036245), and benefited from work done within the Action PROBE (CA18235), supported by COST (European Cooperation in Science and Technology).</p>
A Scalable Data Management System Data Model facilitating the integration of any inspection system and the automated data digitalization process.
<p>In the context of the EU-funded project PILOTING (No. 871542), a scalable Data Management System (DMS) was deployed and facilitated the easy integration of nine different robotic systems and various payloads and the storing of all the data observations produced during the inspections. In particular, a three-phase methodology was adopted for the creation of the DMS Data Model (DMS-DM) in order to define the architectural design of the DMS. <br> The first phase was to semantically define the entities of the PILOTING ecosystem, identifying assets and activities that were important to the data providers, and including them in the data model design. The second phase was to identify relationships between the entities, focusing on information that must pre-exist for entities to be semantically accurate, hierarchies between entities, especially physical assets, and the data needed to form the inspection plan. For the third phase, the information collected by the previous two stages of the data model construction was used to create the overall data model, taking into consideration the possible integration with third parties and the needs of the I&M Visualization portal. <br> The constructed document is attempted to present the designed Entity-Relationship-Diagram (ERD) of the DMS-DM. Additional definitions of the existing entities and the relations between them are also depicted. </p>
Automated processing of aerial imagery for geohazards monitoring: Results from Fagradalsfjall eruption, SW Iceland, August 2022
<p><strong>1-</strong> <strong>Dataset Summary</strong><br> Here we present a dataset of DEMs (Digital Elevation Models), orthomosaics, and lava area outlines for the August 2022 eruption at Fagradalsfjall, SW Iceland. The dataset consists of: (1) five aerial surveys collected over the course of the August 2022 Fagradalsfjall eruption, (2) one survey carried out on 14 August 2022 using Pléiades satellite stereo images, and (3) a larger aerial survey, covering the 2021 and 2022 eruption sites in late September 2022 after the volcanic activity concluded.</p> <p><strong>2- Background</strong></p> <p>The volcano at Fagradalsfjall, SW-Iceland, began erupting on 3 August 2022 at 13:20 following 10 months of quiescence. As part of the response plan, a series of photogrammetric surveys were conducted in rapid, operational mode throughout the duration of the eruption. Subsequent production of data products for natural hazards monitoring (lava maps, lava volumes, effusion rates) were calculated within hours and reported to the Icelandic Civil Defense, following a similar approach that described in Pedersen et al., 2022a and in Gouhier et al., 2022. At the start of the 2022 eruption, GCPs had not yet been placed around the new fissure, but reference data (orthomosaics and DEMs) which had been georeferenced using targets measured with differential GNSS existed of the eruption site from September 2021 from Pedersen et al. (2022b) were available to use as a reference in the new workflow instead of GCPs. Due to the urgent need from authorities for information about the new eruption, a processing method that avoids the time-consuming task of manual GCP selection using a reference image for georeferencing was preferable in this instance. Besides the acquisition of aerial photographs, the CIEST2 initiative was also re-activated to collect Pléiades stereo images in emergency mode (Gouhier et al., 2022).</p> <p><strong>3 – Overview of data collection</strong></p> <p>Table 1 contains the overview of the surveys collected and presented in this repository.</p> <p> Table 1. Summary of surveys included in this dataset, by survey date.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Date & Time</strong><br> <strong>YYYYMMDD HH:MM</strong></p> </td> <td> <p><strong>Sensor</strong></p> </td> <td> <p><strong>Platform</strong></p> </td> <td> <p><strong>Flight alt.</strong><br> <strong>(m asl)</strong></p> </td> <td> <p><strong>Images</strong></p> </td> <td> <p><strong>Surveyed</strong><br> <strong>km<sup>2</sup></strong></p> </td> </tr> <tr> <td> <p>20220803 17:05</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203*</p> </td> <td> <p>~ 850</p> </td> <td> <p>46</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>20220804 11:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>~ 2100</p> </td> <td> <p>32</p> </td> <td> <p>35</p> </td> </tr> <tr> <td> <p>20220813 09:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>~ 750</p> </td> <td> <p>123</p> </td> <td> <p>9</p> </td> </tr> <tr> <td> <p>20220814 13:00</p> </td> <td> <p>Pléiades</p> </td> <td> <p>PHR1B</p> </td> <td> <p>n/a</p> </td> <td> <p>2</p> </td> <td> <p>14</p> </td> </tr> <tr> <td> <p>20220815 08:15</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>2100</p> </td> <td> <p>20</p> </td> <td> <p>23</p> </td> </tr> <tr> <td> <p>20220816 10:06</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>2100</p> </td> <td> <p>19</p> </td> <td> <p>26</p> </td> </tr> <tr> <td> <p>20220926 12:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-BMW**</p> </td> <td> <p>2100</p> </td> <td> <p>~20</p> </td> <td> <p>18</p> </td> </tr> </tbody> </table> <p>* TF-203: Savannah S aircraft</p> <p>** TF-BMW: Vulcanair P68 Observer 2 aircraft, operated by Garðaflug ehf.</p> <p><strong>4- Methods</strong></p> <p><strong>4.1 Processing of the aerial photographs from 3-16 Aug 2022</strong><br> Throughout the eruption, aerial surveys were conducted using a Hasselblad A6D 100 MP camera with 35 mm focal lens, from a height of 750 – 2,100 m above ground over the active lava field from an ultralight aircraft with a window in the bottom to allow for vertical photos to be taken (see supplement of Pedersen et al., 2022a for details and images of the setup). The camera was manually triggered to give ~70% overlap, and approximate flight lines were prepared beforehand for use with a handheld GPS during the flight to give ~30 % side overlap.<br> <br> An automated processing pipeline was created in python, which leverages tools from the Ames Stereo Pipeline (ASP, Shean et al., 2016) and Agisoft Metashape stand-alone Python API (v. 1.8.4). The processing and georeferencing of the aerial data were done in three steps, with all steps being automated except for the digitization of lava outlines. First, using a very high-resolution reference orthomosaic and DEM created in September 2021 and georeferenced with ground control points (Pedersen et al., 2022b), interest points (IPs) in each image were matched with the reference dataset, using the ASP routine <em>ipfind. </em>This created GCPs for each image over stable terrain. Second, hillshades were created from both the reference DEM and the source dataset DEM and matches in IPs were found in both, creating a second round of ground control points to refine the georeferencing of the entire block. Finally, the alignment of the source DEM was refined using the <em>dem_align</em> (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM.</p> <p><strong>4.2 Processing of the Pléiades stereo images</strong><br> The Pléiades stereo images were processed using the Ames Stereo Pipeline, using the general workflow of <em>mapproject </em>and <em>parallel_stereo </em>(e.g., Deschamps-Berger et al., 2020). The <em>parallel_stereo </em>routine used default arguments, plus the following arguments:</p> <p><em>--stereo-algorithm asp_mgm -t rpcmaprpc --corr-seed-mode 3 --corr-max-levels 2 --cost-mode 3 --subpixel-mode 9 --corr-kernel 7 7 --subpixel-kernel 15 15</em></p> <p>We used the DEM from 4 Aug 2022 as the reference for <em>mapproject </em>and for the final DEM co-registration applied to the produced Pléiades DEM.</p> <p><strong>4.3 Processing of the 26 September 2022 dataset</strong><br> The survey from 26 September 2022 was collected and processed using direct georeferencing from an on-board GPS antenna. The final alignment of the block was refined using the dem_align (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM. Because this survey covered a much larger area, the reference DEM for the final coregistration was the <a href="https://atlas.lmi.is/mapview/?application=DEM">ÍslandsDEM </a>v.1.0 (Landmælingar Íslands, 2022).</p> <p><strong>4.4. Maps of the lava outlines, lava thickness, lava volume, Time Average Effusion Rate (TADR)</strong><br> For each survey, a differential DEM (dDEM) showing elevation changes since the 2021 eruption was created by subtracting the reference DEM (ÍslandsDEM v.1.0, which includes the post-eruption DEM from Pedersen et al., 2022a) from the source DEM. Lava outlines, lava thickness lava volume, TADR and uncertainties were calculated using the methods described in Pedersen et al., 2022a. Table 2 summarizes calculations from this dataset.</p> <p> </p> <p>Table 2. Summary of survey results calculated from August 2022 Fagradalsfjall eruption DEMs and orthomosaics.</p> <table align="center"> <thead> <tr> <th> <p><strong> Date Start</strong></p> </th> <th> <p><strong>Date End</strong></p> </th> <th> <p><strong>Time </strong></p> <p><strong>Difference</strong></p> </th> <th> <p><strong>Lava </strong></p> <p><strong>Area<sup>*</sup> End </strong></p> <p><strong>(km<sup>2</sup>)</strong></p> </th> <th> <p><strong>dh<sup>**</sup> </strong></p> <p><strong>(m)</strong></p> </th> <th> <p><strong>Volume<sup>+</sup></strong></p> <p><strong>End<br> (1e+6 m<sup>3</sup>)</strong></p> </th> <th> <p><strong>TADR<sup>++</sup><br> (m<sup>3</sup>/s)</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>20220803<br> 13:20</p> </td> <td> <p>20220803<br> 17:05</p> </td> <td> <p>0d 03h 45m</p> </td> <td> <p>0.07</p> </td> <td> <p>5.88</p> </td> <td> <p>0.43</p> <p>± 0.03</p> </td> <td> <p>32.1</p> <p>± 1.5</p> </td> </tr> <tr> <td> <p>20220803<br> 17:05</p> </td> <td> <p>20220804<br> 11:00</p> </td> <td> <p>0d 21h 40m</p> </td> <td> <p>0.14</p> </td> <td> <p>11.13 </p> </td> <td> <p>1.57</p> <p>± 0.05</p> </td> <td> <p>17.7</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220804<br> 11:00</p> </td> <td> <p>20220813<br> 09:00</p> </td> <td> <p>8d 22h 00m</p> </td> <td> <p>1.27<sup>#</sup></p> </td> <td> <p>6.90</p> </td> <td> <p>10.33</p> <p>± 0.6</p> </td> <td> <p>11.4</p> <p>± 0.7</p> </td> </tr> <tr> <td> <p>20220813<br> 13:08</p> </td> <td> <p>20220814<br> 13:00</p> </td> <td> <p>0d 23h 52m</p> </td> <td> <p>1.24</p> </td> <td> <p>7.28</p> </td> <td> <p>10.62</p> <p>± 0.70</p> </td> <td> <p>2.8</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220814<br> 13:00</p> </td> <td> <p>20220815<br> 08:15</p> </td> <td> <p>0d 19h 15m</p> </td> <td> <p>1.26</p> </td> <td> <p>7.46</p> </td> <td> <p>10.99</p> <p>± 0.55</p> </td> <td> <p>4.1</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220815<br> 08:15</p> </td> <td> <p>20220816<br> 10:16</p> </td> <td> <p>1d 2h 01m</p> </td> <td> <p>1.28</p> </td> <td> <p>7.49</p> </td> <td> <p>11.13</p> <p>± 0.53</p> </td> <td> <p>2.0</p> <p>± 0.7</p> </td> </tr> <tr> <td> <p>20220816<br> 10:16</p> </td> <td> <p>20220821<br> 06:00<sup>##</sup></p> </td> <td> <p>4d 19h 44m</p> </td> <td> <p>1.28</p> </td> <td> <p>7.69</p> </td> <td> <p>11.39</p> <p>± 0.44</p> </td> <td> <p>0.653</p> <p>± 0.10</p> </td> </tr> </tbody> </table> <p><sup>*</sup>Total area of the lava field since 2022-08-03 before activity started.</p> <p><sup>**</sup>dh end is the mean thickness of the lava flow-field in the end of the given period.</p> <p><sup>+</sup>Volume erupted since 2022-08-03 before activity started.</p> <p><sup>++</sup>Time-averaged discharge rate for the given period</p> <p><sup>#</sup>Extrapolated value. Survey does not cover entire active lava area.</p> <p><sup>##</sup>End Time: 21 August 2022, 6:00. This time deduced from field observations from members of the Institute of Earth Sciences, University of Iceland. Values calculated from 26 September 2022 dataset.</p> <p>Figures and visual summaries of the processing method, resulting lava volumes, and uncertainties can be found in this poster: <a href="https://ftp.lmi.is/stm/Sydney/Fagradalsfjall_Aug2022/poster_faf_automated_proc2_srg_2022.pdf">Fagradalsfjall August 2022</a>.<br> <br> Orthomosaics from this dataset are viewable online at: <a href="https://atlas.lmi.is/mapview/?application=umbrotasja">https://atlas.lmi.is/mapview/?application=umbrotasja</a></p> <p><strong>Data naming conventions:</strong></p> <ul> <li>Data type: DEM, ortho, outline, diffDEM, lavafree, lava</li> <li>Acquisition date: YYYYMMDD_HHMM</li> <li>Platform/Sensor for data collection: Pléiades (PLE), Hasselblad A6D from aircraft (A6D)</li> <li>Resolution: 2x2 m (DEMs) and 30x30 cm (Orthomosaics)</li> <li>Folders (by survey): YYYYMMDD_HHMM_platform (during eruption) or 'posteruption'_platform (sensors/platform: A6D or PLE)</li> </ul> <p><strong>Data Specifications:</strong></p> <ul> <li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https:/epsg.io/3057">https://epsg.io/3057</a>)</li> <li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li> <li>Raster data format: GeoTIFF</li> <li>Raster compression system: ZSTD (<a href="http://facebook.github.io/zstd/">http://facebook.github.io/zstd/</a>)</li> <li>Vector data format: GeoPackage (<a href="https://www.geopackage.org/">https://www.geopackage.org/</a>)</li> <li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li> </ul>
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