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11 results for “Critical Infrastructure”
Towards an open pipeline for the detection of Critical Infrastructure from satellite imagery – A case study on electrical substations in The Netherlands
<p><strong>Abstract.</strong> Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI prevents their incorporation in large-scale risk assessment studies. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMaps, processes the imagery and assets' masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The best metrics achieved are an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable AI.</p>
Dataset: Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assessments
<p>The Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assements is a database that contains fragility and vulnerability curves that can be used to evaluate the expected or potential damages to infrastructure assets due to flooding, earthquakes, windstorms and landslides. The database consists of three Excel-spreadsheets:</p> <ul> <li><em>Table_D1_Summary_CI_Vulnerability_Data:</em> summary table with information on hazard, exposure, and vulnerability characteristics as well as a number of details regarding reliability and reference purposes.</li> <li><em>Table_D2_Hazard_Fragility_and_Vulnerability Curves:</em> collection of fragility and vulnerability curves</li> <li><em>Table_D3_Costs:</em> cost values that can be used in combination with the curves for the estimation of asset damages</li> </ul> <p>Please consult the following publication for detailed information: Nirandjan, S., Koks, E. E., Ye, M., Pant, R., van Ginkel, K. C. H., Aerts, J. C. J. H., and Ward, P. J.: Review article: Physical Vulnerability Database for Critical Infrastructure Multi-Hazard Risk Assessments – A systematic review and data collection, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2023-208, in review, 2024.</p>
Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events
<p>Datasets generated during and/or analyzed during the Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events.</p>
The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure
<p>Dataset and Python code complementing the publication </p> <p>Kaiser, S.; Boike, J.; Grosse, G.; Langer, M. The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure. <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>, 6107. https://doi.org/10.3390/rs14236107</p> <ul> <li><strong>AROSICS.zip</strong> contains the orthomosaic of 2018 shifted to 2019 with the AROSICS algorithm. The .txt file contains the x-/y-shift in map units [m].</li> <li><strong>CC_DistancePointClouds.zip</strong> contains the distance point clouds as calculated via Multiscale Model to Model Comparison (M3C2 after Lague et. al, 2013) at each post-processing level (I-IV) and the validation.</li> <li><strong>CC_PointCloudProcessing.zip</strong> contains the point clouds at post-processing levels II-IV.</li> <li><strong>ODM_Orthomosaics.zip</strong> contains the orthomosaics of 2018 and 2019 as processed in WebODM (based on OpenDroneMap).</li> <li><strong>ODM_PointClouds.zip</strong> contains the raw point clouds of 2018 and 2019 (post-processing level I) as processed in WebODM (based on OpenDroneMap).</li> <li><strong>PointCloudStatistics.zip</strong> contains the M3C2 distance statistics at each post-processing level (I-IV) and the validation for the whole point cloud and the two subsets.</li> <li><strong>Python_ChangeDetection.zip</strong> contains the Python (v 3.6) script for calculating the displacement vectors Dx, Dy, Dz for each distance point cloud, rasterizing the attribute "vertical displacement (Dz)" of the distance point cloud with the highest accuracy (post-processing level IV), applying a Sobel edge detection filter to highlight high image gradients and clustering the image into two categories: change (high image gradient) and no change (low image gradient). Needed data input is <strong>CC_DistancePointClouds.zip.</strong></li> <li><strong>Subsets.zip </strong>contains shapefiles of the two subsets.</li> </ul>
SIRIUS - Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in Permafrost Regions of AlaSka
<p>The SIRIUS inventory integrates data from (i) the Sentinel-1/2 derived Arctic coastal human impact dataset (SACHI) (Bartsch et al., 2021), (ii) OpenStreetMap dataset for the infrastructure and land use information (OpenStreetMap Contributors and Geofabrik GmbH, 2018), (iii) the pan-Arctic catchments summary database (ARCADE) for the watersheds (Speetjens et al., 2022), (iv) the modeled Northern Hemisphere permafrost map by Obu et al. (2018), and (v) the contaminated sites database and reports by the State of Alaska Department of Environmental Conservation (2023) (DEC) to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska.</p> <p>The dataset is deployed as a GeoPackage and can be imported to spatial databases (e.g. PostgreSQL/PostGIS), a Geographic Information System (e.g. QGIS), and used within geospatial processing libraries (e.g. Python's GeoPandas). All layers can be queried either in dependence or combination with one another.</p> <p>Each GeoPackage contains the following layers:</p> <ul> <li>ARCADE_WatershedsDB</li> <li>DEC_ContaminatedSitesAK</li> <li>OSM_Point_InfrastructureHIElements</li> <li>SACHI_OSM_InfrastructureHIElements</li> <li>SACHI_OSM_InfrastructureHIElements_RRNetwork</li> <li>UiO_MAGT</li> <li>UiO_PermafrostProbability</li> <li>UiO_PermafrostZones</li> </ul> <p>A corresponding manuscript, including application examples and a thorough description of the individual components, was submitted to be published in an open-access journal.</p> <p><strong>Download Data</strong></p> <ul> <li><strong>Python Scripts</strong> <ul> <li>01_InfrastructureDataETL: reprojects the input Shapefiles and raster datasets to a common coordinate system (EPSG:5936) and then clips datasets to the boundary of Alaska. It also includes a step for filtering the permafrost probability raster dataset based on a minimum probability threshold of 50% and rounds the values in the mean annual ground temperature raster dataset.</li> <li>02_OSM-aggregation: processes the OpenStreetMap (OSM) geospatial data. It imports and merges OSM polygon and point data, cleans and extracts unique values of "fclass" and "osm_type", and aggregates these values for manual categorization, based on the OSM key-value-scheme. The script assigns Land Use/Cover Area frame Statistical Survey (LUCAS) categories to the data, filters out natural objects and places, and resolves unknown categories by identifying intersections between datasets.</li> <li>03_SACHI-aggregation: assigns LUCAS categories to the SACHI dataset based on the 'Use' column.</li> <li>04_SACHI-OSM_decisiontree: performs a series of geospatial operations to determine the overlap between polygonal OSM features and SACHI features and assigns LUCAS categories to the overlapping features based on certain criteria and dissolves them. The overlapping and non-overlapping features are then combined into a single dataset: the harmonized critical infrastructure and human-impacted areas dataset.</li> <li>05_TextMiningNLTK-CSSites: performs text mining and data preprocessing on the reports of the DEC contaminated sites database. It extracts dates, calculates cleanup times for inactive sites, identifies contaminants based on abbreviations and text entries, and extracts information related to contaminants and the medium they are found in.</li> </ul> </li> <li><strong>GeoPackages</strong> <ul> <li>PermaRisk_RRNetworkLine_v01_r00.gpkg contains the rail and road network as line geometries.</li> <li>PermaRisk_RRNetworkPolygonal_v01_r00.gpkg contains the rail and road network as polygon geometries.</li> </ul> </li> </ul> <p> </p> <ul> </ul> <p> </p>
Aerocargo Trafic Argentina - from 2014 to 2019 (no covid-19 data included) Critical Infrastructure Risk Research Program
<p>Proyectos Bianuales de Investigación</p> <p>Universidad Nacional de Cuyo</p> <p>Propuestas metodológicas y modelos de concepción, operación y gestión<br> para la reducción de vulnerabilidad e incremento de la resiliencia en la<br> nueva generación de infaestructuras crítica de américa latina.<br> Código Sigeva: B083<br> Resolución Nro.: Otorgamiento: RES 4142/2019-R- Expediente de pago: NOTA-<br> CUY:38892/2019<br> </p>
Support data for article "The concept of network resource control of a 5G cluster focused on the smart city's critical infrastructure needs"
<p>Support data for article:</p> <div>V. Kovtun, K. Grochla, and K. Połys, “The concept of network resource control of a 5G cluster focused on the smart city’s critical infrastructure needs,” Alexandria Engineering Journal, vol. 94. Elsevier BV, pp. 248–256, May 2024. doi: 10.1016/j.aej.2024.03.038.</div> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
Navigating agricultural landscapes: Responses of critically endangered giant tortoises to farmland vegetation and infrastructure
<p><strong>Context</strong>: Interactions between wildlife and anthropogenic infrastructure, such as roads, fences, and dams, can influence wildlife movement, and potentially cause human-wildlife conflict. In the Galapagos archipelago, two species of critically endangered giant tortoise encounter infrastructure and human-modified vegetation in farms, which could influence movement choices.</p> <p><strong>Objectives</strong>: We investigated factors influencing tortoise movement and habitat selection in the agricultural landscape of Santa Cruz Island, Galapagos.</p> <p><strong>Methods</strong>: We examined the movement of 27 tortoises collected using GPS tracking between 2014 and 2020, in relation to the location of vegetation, ponds, fences, and roads.</p> <p><strong>Results:</strong> We found that tortoises preferred pasture over native vegetation, but there was little difference among their preferences for native vegetation, crops, or invasive vegetation. Tortoises also travelled slower in pasture, and faster in invasive vegetation, compared to crops and native vegetation. Tortoises were more likely to be found closer to ponds than predicted by chance. Our results indicated that most fences were porous to tortoises, with limited impact on their movement. Tortoises were more likely to use areas near roads with low-traffic.</p> <p><strong>Conclusions</strong>: Pastures and ponds are important habitats for tortoises in farms and are likely to be used preferentially by tortoises. Overall, fences and roads did not strongly obstruct tortoise movements, however, this may lead to potential injury to tortoises on roads and property damage for farmers. To best identify priority areas for managing wildlife on farms, we recommend evaluating the combined effects of multiple anthropogenic landscape features on wildlife movements.</p>
Navigating agricultural landscapes: Responses of critically endangered giant tortoises to farmland vegetation and infrastructure
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FIGURE 1 in Bird names as critical communication infrastructure in the contexts of history, language, and culture
FIGURE 1. An example of the greater stability of English bird names relative to scientific names for a single species, which also shows the gateway effects of an eponymous English name to the literature (i.e., the area under the blue curve indicates the importance in communication and access of that name relative to all the others). This is an historic overview of the use of Wilson's Plover (blue) as an English bird name relative to the five scientific names the species has had from the early 1800s through 2019, Aegialitis wilsonius Ord (red), Aegialitis wilsonia (dark green), Ochthodromus wilsonius (golden), Pagolla wilsonia (bright green), and Charadrius wilsonia (purple), using Google Books Ngram Viewer.
Dataset of "Detecting Weaknesses and Analyzing Cascading Failures in Critical Infrastructure Networks Using an Interconnected Simulation System"
<p>This article introduces an innovative simulator for critical infrastructure, developed to enable comprehensive<br>simulation of data and power grid and their interconnections. The simulator offers user-input functionality,<br>allowing for detailed modelling and analysis of various impact scenarios on infrastructures. A key feature<br>of this tool is the use of a state matrix to represent the current state of infrastructures, which is updated<br>after each simulation. This update facilitates accurate and dynamic modelling of changes in infrastructure<br>networks and their interconnections.<br>The simulator is designed to test the functionality of both individual components and the overall network<br>integrity, including the analysis of the cascading failures, where a failure in one part can impact other parts<br>of the infrastructure. This capability is essential for a deeper understanding of risks and for developing<br>effective strategies to protect and ensure the resilience of critical infrastructures. The simulator represents a<br>significant advancement in the field of critical infrastructure simulation, providing a tool for better prediction,<br>identification, and mitigation of potential threats, thereby enhancing the security and resilience of critical<br>systems.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.