Matches in Nanopublications for { ?s <http://schema.org/description> ?o ?g. }
- zenodo.6589624 description "Sharan, Malvika In this talk, I discuss open science as a framework to ensure that all our research components can be easily accessed, openly examined and built upon by others. I will introduce The Turing Way - an open source, open collaboration and community-driven guide to reproducible, ethical and inclusive data science and research. Drawing insights from the project, I will share best practices that researchers should integrate to ensure the highest reproducible and ethical standards from the start of their projects so that their research work is easy to reuse and reproduce at all stages of the development. All attendees will leave the talk understanding the many dimensions of openness and how they can participate in an inclusive, kind and inspiring open source ecosystem as they collaboratively seek to improve research culture. All questions and contributions are welcome at the GitHub repository: https://github.com/alan-turing-institute/the-turing-way. Home page: https://malvikasharan.github.io/ This was a closing keynote at Concordia University in Montreal on 27 May 2022." assertion.
- Climate_justice description "Definition of Climate Justice from Wikipedia." assertion.
- Environmental_justice description "Definition of Environmental Justice from Wikipedia" assertion.
- oHE0aD2JQ-k description "This session on "Justice and Climate Change" has been held online during the CESM Workshop 2022. Agenda: - Jola Ajibade: "Understanding the complexity of Climate justice and Climate Change"; - Laura Landrum: "SEARCH - Study of Environmental Arctic Change Program"; - Yifan Cheng: "Informing Climate and Land Surface Model Decisions with Indigenous Guidance"; - Panel discussion with speakers." assertion.
- j.1365-2745.2011.01859.x description "Summary: Climate change in northern high latitudes is predicted to be greater in winter rather than summer, yet little is known about the effects of winter climate change on northern ecosystems. Among the unknowns are the effects of an increasing frequency of acute, short-lasting winter warming events. Such events can damage higher plants exposed to warm, then returning cold, temperatures after snow melt, and it is not known how bryophytes and lichens, which are of considerable ecological importance in high-latitude ecosystems, are affected by such warming events. However, even physiological adaptations of these cryptogams to winter environments in general are poorly understood. Here we describe findings from a novel field experiment that uses heating from infrared lamps and soil warming cables to simulate acute mid-winter warming events in a sub-Arctic heath. In particular, we report the growing season responses of the dominant lichen, Peltigera aphthosa, and bryophyte, Hylocomium splendens, to warming events in three consecutive winters. While summertime photosynthetic performance of P. aphthosa was unaffected by the winter warming treatments, H. splendens showed significant reductions in net photosynthetic rates and growth rates (of up to 48% and 52%, respectively). Negative effects were evident already during the summer following the first winter warming event. While the lichen develops without going through critical phenological stages during which vulnerable organs are produced, the moss has a seasonal rhythm, which includes initiation of growth of young, freeze-susceptible shoot apices in the early growing season; these might be damaged by breaking of dormancy during warm winter events. Synthesis. Different sensitivities of the bryophyte and lichen species were unexpected, and illustrate that very little is known about the winter ecology of bryophytes and lichens from cold biomes in general. In sharp contrast to summer warming experiments that show increased vascular plant biomass and reduced lichen biomass, these results demonstrate that acute climate events in mid-winter may be readily tolerated by lichens, in contrast to previously observed sensitivity of co-occurring dwarf shrubs, suggesting winter climate change may compensate for (or even reverse) predicted lichen declines resulting from summer warming." assertion.
- hess-23-2983-2019.pdf description "Abstract. Rain-on-snow (ROS) events in mountainous catchments can cause enhanced snowmelt, leading to an increased risk of destructive winter floods. However, due to differences in topography and forest cover, the generation of snowpack outflow volumes and their contribution to streamflow are spatially and temporally variable during ROS events. In order to adequately predict such flood events with hydrological models, an enhanced process understanding of the contribution of rainwater and snowmelt to stream water is needed." assertion.
- article.pdf?sequence=2 description "Abstract Arctic ecosystems are increasingly exposed to extreme climatic events throughout the year, which can affect species performance. Cryptogams (bryophytes and lichens) provide important ecosystem services in polar ecosystems but may be physiologically affected or killed by extreme events. Through field and laboratory manipulations, we compared physiological responses of seven dominant sub-Arctic cryptogams (three bryophytes, four lichens) to single events and factorial combinations of mid-winter heatwave (6C for 7 days), re-freezing, snow removal and summer nitrogen addition. We aimed to identify which mosses and lichens are vulnerable to these abiotic extremes and if combinations would exacerbate physiological responses. Combinations of extremes resulted in stronger species responses but included idiosyncratic species-specific responses. Species that remained dormant during winter (March), irrespective of extremes, showed little physiological response during summer (August). However, winter physiological activity, and response to winter extremes, was not consistently associated with summer physiological impacts. Winter extremes affect cryptogam physiology, but summer responses appear mild, and lichens affect the photobiont more than the mycobiont. Accounting for Arctic cryptogam response to multiple climatic extremes in ecosystem functioning and modelling will require a better understanding of their winter eco-physiology and repair capabilities." assertion.
- s43017-022-00298-5 description "Vegetation indices (VIs), which describe remotely sensed vegetation properties such as photosynthetic activity and canopy structure, are widely used to study vegetation dynamics across scales. However, VI-based results can vary between indices, sensors, quality control measures, compositing algorithms, and atmospheric and sun–target–sensor geometry corrections. These variations make it difficult to draw robust conclusions about ecosystem change and highlight the need for consistent VI application and verification. In this Technical Review, we summarize the history and ecological applications of VIs and the linkages and inconsistencies between them. VIs have been used since the early 1970s and have evolved rapidly with the emergence of new satellite sensors with more spectral channels, new scientific demands and advances in spectroscopy. When choosing VIs, the spectral sensitivity and features of VIs and their suitability for target application should be considered. During data analyses, steps must be taken to minimize the impact of artefacts, VI results should be verified with in situ data when possible and conclusions should be based on multiple sets of indicators. Next-generation VIs with higher signal-to-noise ratios and fewer artefacts will be possible with new satellite missions and integration with emerging vegetation metrics such as solar-induced chlorophyll fluorescence, providing opportunities for studying terrestrial ecosystems globally." assertion.
- gcb.14500 description "Abstract Extreme climatic events are among the drivers of recent declines in plant biomass and productivity observed across Arctic ecosystems, known as “Arctic browning.” These events can cause landscape-scale vegetation damage and so are likely to have major impacts on ecosystem CO2 balance. However, there is little understanding of the impacts on CO2 fluxes, especially across the growing season. Furthermore, while widespread shoot mortality is commonly observed with browning events, recent observations show that shoot stress responses are also common, and manifest as high levels of persistent anthocyanin pigmentation. Whether or how this response impacts ecosystem CO2 fluxes is not known. To address these research needs, a growing season assessment of browning impacts following frost drought and extreme winter warming (both extreme climatic events) on the key ecosystem CO2 fluxes Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP), ecosystem respiration (Reco) and soil respiration (Rsoil) was carried out in widespread sub-Arctic dwarf shrub heathland, incorporating both mortality and stress responses. Browning (mortality and stress responses combined) caused considerable site-level reductions in GPP and NEE (of up to 44%), with greatest impacts occurring at early and late season. Furthermore, impacts on CO2 fluxes associated with stress often equalled or exceeded those resulting from vegetation mortality. This demonstrates that extreme events can have major impac" assertion.
- 3ed30e69-fb38-4045-bd34-2fa907d12353 description "In most places on the planet vegetation thrives, this is known as “greening Earth”. However in certain regions, especially in the Arctic, there are areas exhibiting a browning trend. Here we focus on the Troms and Finnmark counties in northern Norway to assess the extend of the phenomenon and any link with local environmental conditions." assertion.
- 6e7194f5-a479-4555-b8d2-bd4462daaf73 description "The State of the Arctic Terrestrial Biodiversity Report (START) is a product of the Circumpolar Biodiversity Monitoring Program (CBMP) Terrestrial Group of the Arctic Council’s Conservation of Arctic Flora and Fauna (CAFF) Working Group. The START assesses the status and trends of terrestrial Focal Ecosystem Components (FECs)—including vegetation, arthropods, birds, and mammals—across the Arctic, identify gaps in monitoring coverage towards implementation of the CBMP’s Arctic Terrestrial Biodiversity Monitoring Plan; and provides key findings and advice for monitoring. The START is based upon primarily published data, from a special issue of Ambio containing 13 articles by more than 180 scientists" assertion.
- a813607d-29ac-4e73-84c2-ee23315be103 description "Poster presented at EGU 2023 during the ESSI2.8 "HPC and cloud infrastructures in support of Earth Observation, Earth Modeling and community-driven Geoscience approach PANGEO" Convener: Vasileios Baousis | Co-conveners: Tina Odaka, Umberto Modigliani, Anne Fouilloux, Alejandro Coca-CastroECS" assertion.
- d2502da9-7821-4443-84fc-fdf20dd120c9 description "This poster shows the work done to estimate the loss in lichens & mosses in the arctic (arctic browning). ERA5 land data from ECMWF have been used to estimate the changes in vegetation. Poster in svg format that has been presented at EGU 2023 at session ESSI2.8 HPC and cloud infrastructures in support of Earth Observation, Earth Modeling and community-driven Geoscience approach PANGEO Convener: Vasileios Baousis | Co-conveners: Tina Odaka, Umberto Modigliani, Anne Fouilloux, Alejandro Coca-Castro" assertion.
- j.jhydrol.2022.128593 description "Rain-on-snow (ROS) events can greatly affect the snow process and cause severe snowmelt-related hazards. It is important to monitor the spatiotemporal distribution of ROS events over the ungauged High Mountain Asia (HMA). This study investigated the spatiotemporal variability of ROS events over the HMA and its potential influencing factors from 1981 to 2020 based on stand-alone Noah-MP land surface model simulations forced by hourly HARv2 reanalysis dataset. The results demonstrated that ROS activity occurred more frequently in the higher-elevation (2500–4000 m and 5500–6000 m a.s.l) regions of the Tianshan Mountains, Pamir, eastern Hindu Kush, Himalayas, and the western Hengduan Shan, with an annual maximum ROS frequency exceeding 15 days and a maximum intensity reaching 40 mm concentrated in spring and summer. ROS frequency experienced a significant decrease in the high-elevation (3000–4500 m a.s.l) regions of the eastern Hindu Kush, West Himalaya, and western Hengduan Shan with a rate exceeding −1.5 days/decade. The decrease in ROS frequency could be explained by a shifting of precipitation type from snowfall to rain driven by dramatic warming and resulting in a decline in snowfall and shortened snow cover persistence, particularly in spring and summer. On the contrary, significantly increasing trend mainly prevailed in the high-elevation (5000–6000 m a.s.l) regions of Transhimalaya and East Himalaya, exceeding 0.9 days/decade." assertion.
- 1550592 description "ABSTRACT The origin of lichen-free areas in the High Arctic has been attributed to lichen-kill under permanent snowfields developed 300 yr ago during the Little Ice Age. There are inconsistencies in this hypothesis, particularly in regard to the manner of lichen-kill, the mechanism of dead lichen removal once the previously ice-covered ground is exposed again, the period when the lichen-kill occured, and the form of lichen trimlines. An alternative hypothesis is suggested whereby lichen-free areas occur where seasonal snowfields persist for a much greater part of the summer than elsewhere. As a result the lichen growth season there is very short." assertion.
- egusphere-egu23-2579 description "Summary submitted at EGU 2023." assertion.
- train_mooc_tp1n.ipynb description "Jupyter Notebook for training, testing and validating machine learning method to forecast moss and lichen fractional cover mean. This Jupyter Notebook uses Python and Keras." assertion.
- S1873965213000455 description "Abstract Droppings of Svalbard reindeer (Rangifer tarandus platyrhynchus) could affect the carbon and nitrogen cycles in tundra ecosystems. The aim of this study was to evaluate the potential of reindeer droppings originating from the winter diet for emission and/or absorption of methane (CH4) and nitrous oxide (N2O) in summer. An incubation experiment was conducted over 14 days using reindeer droppings and mineral subsoil collected from a mound near Ny-Ålesund, Svalbard, to determine the potential exchanges of CH4 and N2O for combinations of two factors, reindeer droppings (presence or absence) and soil moisture (dry, moderate, or wet). A line transect survey was conducted to determine the distribution density of winter droppings at the study site. The incubation experiment showed a weak absorption of CH4 and a weak emission of N2O. Reindeer droppings originating from the winter diet had a negligible effect on the exchange fluxes of both CH4 and N2O. Although the presence of droppings resulted in a short-lasting increase in N2O emissions on day 1 (24 h from the start) for moderate and wet conditions, the emission rates were still very small, up to 3 μg N2O m−2 h−1." assertion.
- 77a61d94-3318-4d33-a3c0-4730e7026fdb description "This talk is part of the Pangeo Show & Tell series and was given on September 1st 2022 by Felix Cremer. Bio Felix Cremer received his diploma in mathematics from the University of Leipzig in 2014. In 2016 he started his PhD study on time series analysis of hypertemporal Sentinel-1 radar data. He currently works at the Max-Planck-Institute for Biogeochemistry on the development of the JuliaDataCubes ecosystem in the scope of the NFDI4Earth 5 project. Abstract The Earth Data Lab (EDL) is a data cube framework in Julia for the efficient handling of raster data. It is based on the YAXArrays.jl package. YAXArrays.jl provides functionality to deal with labelled arrays, similar to the xarray python package and it also provides efficient and easy multithreading and distributed computation of user defined functions along arbitrary slices of the data. EarthDataLab.jl uses DiskArrays.jl in the backend to deal with out of memory datasets. In this Show-and-Tell Felix is going to give a short introduction into the EarthDataLab.jl package for raster data handling in Julia." assertion.
- 0932cd4b-be0e-468b-8f5c-55fd09410343 description "This is the recorded talk from Felix Cremer during the Pangeo Show & Tell in September 1st, 2022. Felix is going through his Julia Notebook and explain us about handling large geo data with Julia." assertion.
- 0d22ef7c-c889-45a7-9fce-051b027f1915 description "Admin 0 & Countries | Natural Earth" assertion.
- 1fe8d85c-9a4e-4efe-bbec-1d3d1cadab7a description "This is the shared document we use for all the Pangeo Show and Tell. We collect information, Q&A and feedback. Each Show and Tell has its own sub-section." assertion.
- 4f4d02a5-aa19-43ca-b816-7a69b6b6d103 description "cpg file from shapefile dataset." assertion.
- 5494c273-df00-440d-9aa7-8ed8ceea8d03 description "Jupyter Notebook used by Felix during the Pangeo Show & Tell to demonstrate how to use EarthDataLab.jl to do large scale computations. To execute this Jupyter Notebook, data contained in the "input folder" is needed (please create a folder called "data" in the folder where you have stored the notebook)." assertion.
- 6398008a-f0f0-441e-963d-20be3c9a1d88 description "Part of ne_50m_admin_0_countries shapefile (projection information)." assertion.
- 700034d4-cf1c-4c54-809e-e532a9276745 description "You will find here all the information published to advertise the Pangeo Show & Tell Talk frm Felix Cremer on "Handling large geo data with Julia "." assertion.
- 7be40ed1-d34c-415c-8d9e-3465c7dfaf46 description "Version" assertion.
- 9b5c569a-f9bd-4147-9844-4d856bd858db description "Plot from the Julia Jupyter notebook." assertion.
- b96292fe-759a-411e-abfa-5d5c5853f3e5 description "Part of ne_50m_admin_0_countries shapefile." assertion.
- bf9226da-8484-4fb8-9460-830f0ff8a561 description "Part of ne_50m_admin_0_countries shapefile." assertion.
- c80ebbb8-90b6-467b-adb2-c6e637bac5b9 description "YAXArrays.jl is another xarray-like Julia package. A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL. Package Features: - open datasets from a variety of sources (NetCDF, Zarr, ArchGDAL) - interoperability with other named axis packages through YAXArrayBase - efficient mapslices(x) operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets)" assertion.
- d2dd9a75-2a21-4ba4-bf04-78b5fa966244 description "This will become a selection of tutorials on the use of ESDL.jl and YAXArrays.jl julia packages for the handling of large scale out-of-core geospatial datasets." assertion.
- f51a636c-57d5-4bb8-93a3-2b57cc245ae5 description "Part of ne_50m_admin_0_countries shapefile." assertion.
- 71d2b507-4327-4496-aebf-95882376ad3d description "The dataset includes the volumes obtained from the modeling of the radiant heat flux curve observed in SEVIRI data for the paroxysmal events occurred at Mt Etna during the December 2020-February 2022 period and the volumes obtained from DSM difference in three time windows (from 22 August 2020 to 26 February 2021; from 26 February to 27 July 2021; and from 27 July 2021 to 29 June 2022)." assertion.
- f22db927-47fc-4ce4-a865-3765221aad59 description "Raster maps in bsq format of volcanic deposits emplaced at Mt Etna obtained from DSMs difference during three periods: from 22 August 2020 to 26 February 2021 (1); from 26 February to 27 July 2021 (2) and from 27 July 2021 to 29 June 2022 (3)." assertion.
- response-ais.html description "VTexplorer API (https://www.vtexplorer.com) documentation. This documentation can be useful to understand how AIS data can be processed." assertion.
- tle-fmt.php description "This document describes the NORA Two-Line Element Set Format (TLE) where data for each satellite consists of three lines with a fixed format (see document)." assertion.
- edit?usp=sharing description "Main document (Google doc) provided when willing to start with TSAR Overview." assertion.
- view?usp=sharing description "Slides (private) presenting the T-SAR project." assertion.
- view?usp=sharing description "In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations." assertion.
- tsar-project description "Here we gather information about the project (notes taken during meetings, etc.). We use hackmd.io and text is written in markdown." assertion.
- 0AI6umItIl7BxUk9PVA description "Internally shared google drive with data and documents for T-SAR project" assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a description "In transport infrastructures, vessel traffic services, air traffic management, and connected cars all rely on unauthenticated and unencrypted messages transfer that renders these services vulnerable to cyberattacks. Typical attacks such as False Data Injection Attacks (FDIA) are challenging to detect as they alter the semantics of the data (e.g., by adding/removing/multiplying elements on real-time control equipment), while preserving the syntactical correctness of the messages. Identifying these attacks and classifying them as serious threats or unintentional false data has become a significant challenge of traffic monitoring authorities. The TSAR project aims at demonstrating that recent advances in Artificial Intelligence (AI) can be leveraged in the automatic detection of FDIA in transport infrastructures. By combining realistic threat data generation based on constraint-based software testing techniques and automatic detection with deep reinforcement learning, TSAR will propose a new technology for automatic FDIA generation and detection. This technology will be empirically evaluated with end-users from the maritime domain and with open and accessible data in two other domains, namely air traffic control, and connected cars. By leveraging automatic detection of FDIA in traffic management systems, TSAR will also prepare the ground for the upcoming revolution in traffic management, which concerns, self-driving vessels, self-driving aircraft, and self-driving cars." assertion.
- 5bffb6c2-45e3-4a80-9242-5afd61c21063 description "papers, conference proceeding generated by the TSAR project." assertion.
- 636a64a4-ba87-4d16-8a67-455bfd74e7d6 description "Documentation and existing information about surveillance and detection of anomalies using Automatic Identification System data (ground and satellite)." assertion.
- bc6ab0f0-c443-4b11-a772-03fbcdb452c4 description "Bibliography collected on Automatic Identification System and detection of anomalies from AIS data (ground/satellite)." assertion.
- 3e6f07ae-3da5-43ab-a7f9-4334ee01b8d2 description "Distribution of samples on the surface of the globe." assertion.
- c7d9b7cb-7192-471b-82f4-13fe89dc6906 description "The NorSat-3 microsatellite will be launched into space during spring 2021 with a radar detector developed at the Norwegian Defence Research Establishment (FFI). It will provide improved surveillance capability of the shipping traffic in Norwegian national waters. File downloaded from the Norwegian Defence Research Establishment (https://publications.ffi.no/nb/item/asset/dspace:7059/FFI-Facts_NorSat_Engelsk_web_v2.pdf)." assertion.
- ed59cb0a-e359-4f95-932d-88375b08daa7 description "Major transportation surveillance protocols have not been specified with cyber securityin mind and therefore provide no encryption nor identification. These issues expose air and seatransport to false data injection attacks (FDIAs), in which an attacker modifies, blocks or emits fakesurveillance messages to dupe controllers and surveillance systems. There has been growing interestin conducting research on machine learning-based anomaly detection systems that address these newthreats. However, significant amounts of data are needed to achieve meaningful results with this typeof model. Raw, genuine data can be obtained from existing databases but need to be preprocessedbefore being fed to a model. Acquiring anomalous data is another challenge: such data is muchtoo scarce for both the Automatic Dependent Surveillance–Broadcast (ADS-B) and the AutomaticIdentification System (AIS). Crafting anomalous data by hand, which has been the sole methodapplied to date, is hardly suitable for broad detection model testing. This paper proposes an approachbuilt upon existing libraries and ideas that offers ML researchers the necessary tools to facilitatethe access and processing of genuine data as well as to automatically generate synthetic anomaloussurveillance data to constitute broad, elaborated test datasets. We demonstrate the usability of theapproach by discussing work in progress that includes the reproduction of related work, creation ofrelevant datasets and design of advanced anomaly" assertion.
- d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe description "This Research Object contains AIS data (raw and pre-processed by Statsat AS, Norway). It is not public and has been provided by Statsat AS (Norway). If you are working at Simula, information on where to find pre-processed data on the EX3 is given in the Data RO (README.txt in the metadata folder). This dataset has been used for developing new machine learning algorithms for detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway) led by Simula Research Laboratory (Oslo, Norway)." assertion.
- jmse10010112 description "The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations." assertion.
- Two-line_element_set description "Description of Two-Line Element Set (TLE) from Wikipedia." assertion.
- master description "Gitlab repository set up for reproducibility purposes." assertion.
- marivisu-v2 description "Marivisu serves as a demonstrator of the machine learning model developed to detect anomalies in the vessel trajectory. This work was supported by the Norwegian Research Council (RCN) TSAR project under contract 287893. Satellite AIS data used for model development and testing has been made available courteously by its owner, the Norwegian Coastal Administration (Kystverket)." assertion.
- pre-processing description "n maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transhipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) messages transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations." assertion.
- vesseltype_identification_dae description "Private repository containing Anomaly Detection in Vessels Trajectories using Context-Aware Autoencoders" assertion.
- apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf description "The surveillance of maritime traffic is confronted with very important difficulties in detecting illegal activities at sea. In this article, we present the first results of a self-supervised learning method which aims to detect voluntary disconnec- tions of the identification’ system of vessels. By processing data from four Norwegian surveillance satellites, our lear- ning model aims to identify vessels suspected of illegal acti- vities such as fishing in protected areas or crossing econo- mic exclusion zones in real time. In this article, we present an approach based on self-supervised learning techniques, and experienced from real data." assertion.
- 2759 description "Pangeo discourse announcement." assertion.
- 2274 description "Discussion from Pangeo Discourse on DGGS use with Pangeo." assertion.
- pangeo_dggs_2022 description "Github repository with examples used during the Pangeo Show and Tell - 06. Oct., 2022 on "DGGS and their potential impact in Geoscience and Geospatial" by Alexander Kmoch (Landscape Geoinformatics Lab, University of Tartu, Estonia). Twitter: @Lgeoinformatics │ @allixender" assertion.
- bd43e723-e961-4558-9b20-68ebd4b34a9b description "A Discrete Global Grid Systems (DGGS) is a unique type of spatial reference system comprising of a hierarchy of uniquely identifiable discrete grid cells that span the globe at multiple resolutions. A DGGS can support efficient management, storage, integration, exploration, mining, and visualisation of large geospatial datasets, and several systems of tesselation and indexing schemes exist. The main topic of this session is to introduce the audience to the theoretical background of Discrete Global Grid Systems (DGGS), current real-world implementations and exemplary use cases. This includes grid generation, data indexing and sampling with DGGRID, and some spatial analysis with with H3 and rHealPix." assertion.
- 392f6daf-80e8-4691-a100-3a27db027fcc description "Slides for the presentation on DGGS given during Pangeo Show and Tell October 6, 2022 by Alex Kmoch." assertion.
- 8a387283-0d83-4b6a-9fda-f6aec378d7b5 description "A Discrete Global Grid System is a spatial reference system that uses a hierarchical tessellation of cells to partition and address the globe. OGC Abstract Specification, 2017" assertion.
- environment.yml description "Conda environment for running DGGS notebook examples." assertion.
- h3_intro.ipynb description "Jupyter Notebook demonstrating how to perform Spatial Data Analysis with H3." assertion.
- kkLRtyZtxs0 description "This YouTube video is part of the Pangeo Show & Tell series and was given on October 6 2022 by Alexander Kmoch, Department of Geography of the University of Tartu, (Estonia)." assertion.
- showandtell description "This is the shared document we use for all the Pangeo Show and Tell. We collect information, Q&A and feedback. Each Show and Tell has its own sub-section." assertion.
- showandtell description "This is the shared document we use for all the Pangeo Show and Tell. We collect information, Q&A and feedback. Each Show and Tell has its own sub-section." assertion.
- conda-linux-64.lock description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- conda-osx-64.lock description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- conda-win-64.lock description "Lock conda file for win-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- b128b282-dee7-44a7-bc21-f1fd21452a83 description "The research object refers to the Exploring Land Cover Data (Impact Observatory) notebook published in the Environmental Data Science book." assertion.
- environment.yml description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.
- notebook.html description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- 0566d7df-d790-44bd-bcd1-fe89c0582a29 description "A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion." assertion.
- 6aa4b4a0-c7dc-4762-aee1-e8dc94a1705c description "A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion." assertion.
- b90bc0b8-2d26-4e0c-b255-c2399b52d45d description "A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion." assertion.
- 07e76ee3-fe6c-4fb1-903d-3a8c5236e51b description "This Jupyter notebook is a tool that can load seabird (https://www.seabird.com/) (.cnv) and (.ros) files and plot discrete samples that where collected with the CTD rosette of the Niskin bottle at a predefined depth/pressure level. The tool is handy, when in-sea during a cruise, to compare and check the difference between the records of the dissolved oxygen bottle, the winkler oxygen, and the CTD profile of the water columns. ie. Winkler method is based on the titration to determine dissolved oxygen note: this is part of a series of notebooks to calibrate seabird oxygen sensor based on Winkler Derived Coefficients." assertion.
- dev description "YAXArrays.jl is another xarray-like Julia package. A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL. Package Features: - open datasets from a variety of sources (NetCDF, Zarr, ArchGDAL) - interoperability with other named axis packages through YAXArrayBase - efficient mapslices(x) operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets)" assertion.
- ESDLTutorials description "This will become a selection of tutorials on the use of ESDL.jl and YAXArrays.jl julia packages for the handling of large scale out-of-core geospatial datasets." assertion.
- ne_50m_admin_0_countries.dbf description "Part of ne_50m_admin_0_countries shapefile." assertion.
- ne_50m_admin_0_countries.shp description "Part of ne_50m_admin_0_countries shapefile." assertion.
- ne_50m_admin_0_countries.shx description "Part of ne_50m_admin_0_countries shapefile." assertion.
- a802f7dc-f3f4-4eac-b69f-748fb90958fb description "This talk is part of the Pangeo Show & Tell series and was given on September 1st 2022 by Felix Cremer. Bio Felix Cremer received his diploma in mathematics from the University of Leipzig in 2014. In 2016 he started his PhD study on time series analysis of hypertemporal Sentinel-1 radar data. He currently works at the Max-Planck-Institute for Biogeochemistry on the development of the JuliaDataCubes ecosystem in the scope of the NFDI4Earth 5 project. Abstract The Earth Data Lab (EDL) is a data cube framework in Julia for the efficient handling of raster data. It is based on the YAXArrays.jl package. YAXArrays.jl provides functionality to deal with labelled arrays, similar to the xarray python package and it also provides efficient and easy multithreading and distributed computation of user defined functions along arbitrary slices of the data. EarthDataLab.jl uses DiskArrays.jl in the backend to deal with out of memory datasets. In this Show-and-Tell Felix is going to give a short introduction into the EarthDataLab.jl package for raster data handling in Julia." assertion.
- 2ada4d46-d001-4d7c-904b-d5f4667f4dd2 description "Plot from the Julia Jupyter notebook." assertion.
- ne_50m_admin_0_countries.README.html description "Admin 0 & Countries | Natural Earth" assertion.
- ne_50m_admin_0_countries.VERSION.txt description "Version" assertion.
- ne_50m_admin_0_countries.cpg description "cpg file from shapefile dataset." assertion.
- ne_50m_admin_0_countries.prj description "Part of ne_50m_admin_0_countries shapefile (projection information)." assertion.
- overallintro.ipynb description "Jupyter Notebook used by Felix during the Pangeo Show & Tell to demonstrate how to use EarthDataLab.jl to do large scale computations. To execute this Jupyter Notebook, data contained in the "input folder" is needed (please create a folder called "data" in the folder where you have stored the notebook)." assertion.
- 18_e8wmI9Os description "This is the recorded talk from Felix Cremer during the Pangeo Show & Tell in September 1st, 2022. Felix is going through his Julia Notebook and explain us about handling large geo data with Julia." assertion.
- 2656 description "You will find here all the information published to advertise the Pangeo Show & Tell Talk frm Felix Cremer on "Handling large geo data with Julia "." assertion.
- b7f139b2-b89b-494a-8687-8f3fc4aaae83 description "A dedicated workflow in ArcGIS was developed to identify targets from the bathymetry within the MAELSTROM Project - Smart technology for MArinE Litter SusTainable RemOval and Management" assertion.
- 33180bf8-3307-4e46-97cb-5efcfaaa583d description "Here some results" assertion.
- 348a403c-6567-4905-90f8-186f7fb3a558 description "Related documents and resources" assertion.
- 53880215-19eb-4373-8319-fdc74c7f7508 description "Data input" assertion.
- bcc4d42f-ac3e-40d4-a5fe-8f26bf7e6b8d description "It contains Jupyter notebook" assertion.
- 21c5bfb9-d2c8-4c02-9c90-5e845d8f75e9 description "Shape file with targets detected from bathymetry" assertion.
- ae49cef0-be33-4dd7-87f5-f57d8a6bab8e description "This paper presents a semi-automated method to recognize, spatially delineate and characterise morphometrically pockmarks at the seabed" assertion.
- b14d905d-32b8-43af-91b9-9f413262aaa6 description "This Notebook provides a workflow of ArcGis toolboxes to identify ML targets from bathynetry." assertion.