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  • 1
    Call number: S 99.0139(337)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 337
    Type of Medium: Series available for loan
    Pages: 151 Seiten , Illustrationen, Diagramme
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 337
    Language: German
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2017 , Inhaltsverzeichnis 1 Einleitung 1.1 Zielsetzung 1.2 Struktur 2 Grundlagen und Stand der Forschung 2.1 Mobile Mapping Systeme 2.1.1 Allgemeine Funktionsweise 2.1.2 Riegl VMX-250 2.1.3 Alternative Systeme 2.2 Punktwolken 2.2.1 Definition 2.2.2 Abgrenzung zu vermaschten Punkten 2.2.3 Speicherformate von Punktwolken 2.2.4 Visualisierungstechniken 2.2.5 Level Of Detail 2.3 Farbmodelle 2.3.1 Farbanpassung 2.4 Verteiltes Rechnen 2.5 Verdeckungsanalyse 2.6 Registrierung mehrerer Datensätze 2.7 Visualisierungssysteme 2.7.1 Standalone Point Cloud Viewer 2.7.2 Webbasierte Systeme 3 Effizienzbetrachtungen 3.1 Effiziente Verarbeitung von Massendaten durch Parallelisierung 3.1.1 Parallelisierungsformen 3.1.2 Umsetzung 3.1.3 Vergleich 3.2 Effiziente Datenstrukturen 3.2.1 Scanstreifen 3.2.2 Scanstreifenbasierte Pufferstrategie 3.2.3 Rasterdatenstruktur 3.2.4 Randproblematik und Caching 4 Modulare Verarbeitungskette f ̈ur Mobile Mapping Daten 4.1 Analyse der beteiligten Komponenten des Herstellerworkflows 4.2 Exemplarische modulare Verarbeitungskette 4.3 Vorverarbeitungsmodul 4.3.1 Vereinfachung 4.3.2 Zeitsegmentierung 4.3.3 Bestimmung von Punktattributen 4.4 Segmentierung und Klassifikation 4.4.1 Bodenextraktion 4.4.2 Objektsegmentierung 5 Sensordatenintegration: Kalibrierung der Kameraorientierung 5.1 Zeitstempelabweichung 5.2 Ansatz 5.3 Extraktion von Silhouetten 5.3.1 Extraktion von Silhouetten aus Kamerabildern 5.3.2 Extraktion von Silhouetten aus Laserscandaten 5.4 ICP-basierte Identifikation der Korrespondenzen 5.4.1 Beschränkung der Scanpunktbildsilhouette 5.4.2 Gruppierung der Scanpunktdaten 5.4.3 ICP unter Berücksichtigung der Punktnormalen 5.5 Bestimmung der Kameraparameter mittels Rückwärtsschnitt 5.5.1 Wahl der Stichprobe 5.5.2 Anzahl an Iterationen 5.5.3 Bewertung der gefundenen Modelle 5.6 Ergebnisse 5.7 Verbesserungspotential und Probleme 5.7.1 Laufzeiten 5.7.2 Robustheit des Verfahrens und Qualität der Ergebnisse 6 Farbbestimmung 6.1 Farbextraktion 6.2 Verdeckungsanalyse 6.2.1 Geometrische Verdeckungsanalyse 6.2.2 Ballbasierter Tiefenpuffer 6.2.3 Ergebnisse 6.2.4 Nicht erfasste und dynamische Objekte 6.3 Farbanpassung 6.3.1 Einfärbesituationen benachbarter Scanpunkte 6.3.2 Objektweise Farbanpassung 6.3.3 IDP-Interpolierte radiometrische Helligkeitsanpassung von Bodenpunkten 6.3.4 Radiometrische Helligkeits- und Sättigungsanpassung von Objektpunkten 6.4 Farbsynthese 6.4.1 Histogrammbasierte Farbinterpolation 6.4.2 Ergebnis 7 Aus Punktwolken abgeleitete Modelle 7.1 3D Modelle 7.1.1 Identifikation planarer Bereiche 7.1.2 Nachbearbeitung der erstellten Texturen 7.1.3 Effiziente Verwaltung von Texturen 7.1.4 Erhöhung der Speichereffizienz 7.1.5 Level of Detail 7.2 2D Modelle 7.2.1 Trackjektorienabschnitte 7.2.2 Ermittlung relevanter Ebenen 7.2.3 Ergebnis 8 Visualisierung von Mobile Mapping Daten 8.1 3D Visualisierung 8.1.1 Visualisierung via Web-App 8.1.2 Performante Client-Server Kommunikation und Serialisierung 8.1.3 Scheduling der LOD-Daten 8.1.4 GUI Responsiveness 8.1.5 Navigation und Nutzerinteraktion 8.2 2D Visualisierung 8.2.1 Parallax Scrolling Visualisierung via Android-App 8.2.2 Beleuchtungsmodell 8.2.3 Ergebnis und Ausblick 9 Schlussfolgerungen und Ausblick 9.1 Ausblick Literaturverzeichnis
    Location: Lower compact magazine
    Branch Library: GFZ Library
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  • 2
    Publication Date: 2021-08-03
    Description: Abstract
    Description: SEVA is a scalable exploration tool that supports users to conduct change detection based on optical Sentinel-2 satellite observations. It supports the following essential steps of change detection: a) exploration and selection of optical satellite images to recognize proper data for the current application scenario, b) automated extraction of changes from the optical satellite images, c) analysis of errors and d) assessment and interpretation of the extracted changes.
    Description: TechnicalInfo
    Description: License: GNU General Public License, Version 3, 29 June 2007 Copyright © 2020 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany SEVA is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. SEVA is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
    Keywords: remote sensing ; satellite data ; visualilsation ; change detection ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION 〉 GEOGRAPHIC INFORMATION SYSTEMS 〉 WEB-BASED GEOGRAPHIC INFORMATION SYSTEMS
    Type: Software , Software
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  • 3
    Publication Date: 2016-10-05
    Print ISSN: 1618-2162
    Electronic ISSN: 1610-1995
    Topics: Computer Science
    Published by Springer
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  • 4
    Publication Date: 2020-04-17
    Description: The validation of a simulation model is a crucial task in model development. It involves the comparison of simulation data to observation data and the identification of suitable model parameters. SLIVISU is a Visual Analytics framework that enables geoscientists to perform these tasks for observation data that is sparse and uncertain. Primarily, SLIVISU was designed to evaluate sea level indicators, which are geological or archaeological samples supporting the reconstruction of former sea level over the last ten thousands of years and are compiled in a postgreSQL database system. At the same time, the software aims at supporting the validation of numerical sea-level reconstructions against this data by means of visual analytics.
    Type: Software , NonPeerReviewed
    Format: text
    Format: archive
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  • 5
    Publication Date: 2022-02-01
    Description: Abstract
    Description: The River Plume Workflow is part of the Flood Event Explorer (FEE, Eggert et al., 2022), developed at the GFZ German Research Centre for Geosciences in close collaboration with Helmholtz-Zentrum Hereon. It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). The focus of the River Plume Workflow is the impact of riverine flood events on the marine environment. At the end of a flood event chain, an unusual amount of nutrients and pollutants is washed into the North Sea, which can have consequences, such as increased algae blooms. The workflow aims to enable users to detect a river plume in the North Sea and to determine its spatio-temporal extent. Identifying river plume candidates can either happen manually in the visual interface or also through an automatic anomaly detection algorithm, using Gaussian regression. In both cases a combination of observational data, namely FerryBox transects and satellite data, and model data are used. Once a river plume candidate is found, a statistical analysis supplies additional detail on the anomaly and helps to compare the suspected river plume to the surrounding data. Simulated trajectories of particles starting on the FerryBox transect at the time of the original observation and modelled backwards and forwards in time help to verify the origin of the river plume and allow users to follow the anomaly across the North Sea. An interactive map enables users to load additional observational data into the workflow, such as ocean colour satellite maps, and provides them with an overview of the flood impacts and the river plume’s development on its way through the North Sea. In addition, the workflow offers the functionality to assemble satellite-based chlorophyll observations along model trajectories as a time series. They allow scientists to understand processes inside the river plume and to determine the timescales on which these developments happen. For example, chlorophyll degradation rates in the Elbe river plume are currently investigated using these time series. The workflow's added value lies in the ease with which users can combine observational FerryBox data with relevant model data and other datasets of their choice. Furthermore, the workflow allows users to visually explore the combined data and contains methods to find and highlight anomalies. The workflow’s functionalities also enable users to map the spatio-temporal extent of the river plume and investigate the changes in productivity that occur in the plume. All in all, the River Plume Workflow simplifies the investigation and monitoring of flood events and their impacts in marine environments.
    Description: TechnicalInfo
    Description: Copyright 2022 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DE Flood Event Explorer Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Keywords: Digital Earth ; Flood ; DASF ; Workflow ; river plume ; ferrybox ; impact ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 FLOODS ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION
    Type: Software , Software
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  • 6
    Publication Date: 2022-02-01
    Description: Abstract
    Description: The Socio-Economic Flood Impacts Workflow is part of the Flood Event Explorer (FEE, Eggert et al., 2022), developed at the GFZ German Research Centre for Geosciences . It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). The Socio-Economic Flood Impacts Workflow aims to support the identification of relevant controls and useful indicators for the assessment of flood impacts. It should support answering the question What are useful indicators to assess socio-economic flood impacts?. Floods impact individuals and communities and may have significant social, economic and environmental consequences. These impacts result from the interplay of hazard - the meteo-hydrological processes leading to high water levels and inundation of usually dry land, exposure - the elements affected by flooding such as people, build environment or infrastructure, and vulnerability - the susceptibility of exposed elements to be harmed by flooding. In view of the complex interactions of hazard and impact processes a broad range of data from disparate sources need to be compiled and analysed across the boundaries of climate and atmosphere, catchment and river network, and socio-economic domains. The workflow approaches this problem and supports scientists to integrate observations, model outputs and other datasets for further analysis in the region of interest. The workflow provides functionalities to select the region of interest, access hazard, exposure and vulnerability related data from different sources, identifying flood periods as relevant time ranges, and calculate defined indices. The integrated input data set is further filtered for the relevant flood event periods in the region of interest to obtain a new comprehensive flood data set. This spatio-temporal dataset is analysed using data-science methods such as clustering, classification or correlation algorithms to explore and identify useful indicators for flood impacts. For instance, the importance of different factors or the interrelationships among multiple variables to shape flood impacts can be explored. The added value of the Socio-Economic Flood Impacts Workflow is twofold. First, it integrates scattered data from disparate sources and makes it accessible for further analysis. As such, the effort to compile, harmonize and combine a broad range of spatio-temporal data is clearly reduced. Also, the integration of new datasets from additional sources is much more straightforward. Second, it enables a flexible analysis of multivariate data and by reusing algorithms from other workflows it fosters a more efficient scientific work that can focus on data analysis instead of tedious data wrangling.
    Description: TechnicalInfo
    Description: Copyright 2022 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DE Flood Event Explorer Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Keywords: Digital Earth ; Flood ; DASF ; Workflow ; hydrometeorological controls ; indicators ; impact assessment ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 FLOODS ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION
    Type: Software , Software
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  • 7
    Publication Date: 2022-02-01
    Description: Abstract
    Description: The Flood Similarity Workflow is part of the Flood Event Explorer (FEE, Eggert et al., 2022), developed at the GFZ German Research Centre for Geosciences . It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). River floods and associated adverse consequences are caused by complex interactions of hydro-meteorological and socio-economic pre-conditions and event characteristics. The Flood Similarity Workflow supports the identification, assessment and comparison of hydro-meteorological controls of flood events. The analysis of flood events requires the exploration of discharge time series data for hundreds of gauging stations and their auxiliary data. Data availability and accessibility and standard processing techniques are common challenges in that application and addressed by this workflow. The Flood Similarity Workflow allows the assessment and comparison of arbitrary flood events. The workflow includes around 500 gauging stations in Germany comprising discharge data and the associated extreme value statistics as well as precipitation and soil moisture data. This provides the basis to identify and compare flood events based on antecedent catchment conditions, catchment precipitation, discharge hydrographs, and inundation maps. The workflow also enables the analysis of multidimensional flood characteristics including aggregated indicators (in space and time), spatial patterns and time series signatures. The added value of the Flood Event Explorer comprises two major points. First, scientist work on a common, homogenized database of flood events and their hydro-meteorological controls for a large spatial and temporal domain , with fast and standardized interfaces to access the data. Second, the standardized computation of common flood indicators allows a consistent comparison and exploration of flood events.
    Description: TechnicalInfo
    Description: Copyright 2022 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DE Flood Event Explorer Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Keywords: Digital Earth ; Flood ; DASF ; Workflow ; hydrometeorological controls ; compare ; assess ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 FLOODS ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION
    Type: Software , Software
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  • 8
    Publication Date: 2022-02-01
    Description: Abstract
    Description: The Smart Monitoring Workflow (Tocap) is part of the Flood Event Explorer (FEE, Eggert et al., 2022), developed at the GFZ German Research Centre for Geosciences in close collaboration with the Helmholtz-Centre for Environmental Research UFZ Leipzig. It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). A deeper understanding of the Earth system as a whole and its interacting sub-systems depends not only on accurate mathematical approximations of the physical processes but also on the availability of environmental data across time and spatial scales. Even though advanced numerical simulations and satellite-based remote sensing in conjunction with sophisticated algorithms such as machine learning tools can provide 4D environmental datasets, local and mesoscale measurements continue to be the backbone in many disciplines such as hydrology. Considering the limitations of human and technical resources, monitoring strategies for these types of measurements should be well designed to increase the information gain provided. One helpful set of tools to address these tasks are data exploration frameworks providing qualified data from different sources and tailoring available computational and visual methods to explore and analyse multi-parameter datasets. In this context, we developed a Smart Monitoring Workflow to determine the most suitable time and location for event-driven, ad-hoc monitoring in hydrology using soil moisture measurements as our target variable. The Smart Monitoring Workflow consists of three main steps. First is the identification of the region of interest, either via user selection or recommendation based on spatial environmental parameters provided by the user. Statistical filters and different color schemes can be applied to highlight different regions. The second step is accessing time-dependent environmental parameters (e.g., rainfall and soil moisture estimates of the recent past, weather predictions from numerical weather models and swath forecasts from Earth observation satellites) for the region of interest and visualizing the results. Lastly, a detailed assessment of the region of interest is conducted by applying filter and weight functions in combination with multiple linear regressions on selected input parameters. Depending on the measurement objective (e.g highest/lowest values, highest/lowest change), most suitable areas for monitoring will subsequently be visually highlighted. In combination with the provided background map, an efficient route for monitoring can be planned directly in the exploration environment. The added value of the Smart Monitoring Workflow is multifold. The workflow gives the user a set of tools to visualize and process their data on a background map and in combination with data from public environmental datasets. For raster data from public databases, tailor-made routines are provided to access the data in the spatial-temporal limits required by the user. Aiming to facilitate the design of terrestrial monitoring campaigns, the platform and device-independent approach of the workflow gives the user the flexibility to design a campaign at the desktop computer first and to refine it later in the field using mobile devices. In this context, the ability of the workflow to plot time-series of forecast data for the region of interest empowers the user to react quickly to changing conditions, e.g thunderstorm showers, by adapting the monitoring strategy, if necessary. Finally, the integrated routing algorithm assists to calculate the duration of a planned campaign as well as the optimal driving route between often scattered monitoring locations.
    Description: TechnicalInfo
    Description: Copyright 2022 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DE Flood Event Explorer Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Keywords: Digital Earth ; Flood ; DASF ; Workflow ; smart monitoring ; campaign planning ; tocap ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 FLOODS ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION
    Type: Software , Software
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  • 9
    Publication Date: 2022-02-01
    Description: Abstract
    Description: The Climate Change Workflow is part of the Flood Event Explorer (FEE, Eggert et al., 2022), developed at the GFZ German Research Centre for Geosciences in close collaboration with Helmholtz-Zentrum Hereon , Climate Service Center Germany. It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). The goal of the Climate Change Workflow is to support the analysis of climate-driven changes in flood-generating climate variables, such as precipitation or soil moisture, using regional climate model simulations from the Earth System Grid Federation (ESGF) data archive. It should support to answer the geoscientific question How does precipitation change over the course of the 21st century under different climate scenarios, compared to a 30-year reference period over a certain region? Extraction of locally relevant data over a region of interest (ROI) requires climate expert knowledge and data processing training to correctly process large ensembles of climate model simulations, the Climate Change Workflow tackles this problem. It supports scientists to define the regions of interest, customize their ensembles from the climate model simulations available on the Earth System Grid Federation (ESGF), define variables of interest, and relevant time ranges. The Climate Change Workflow provides: (1) a weighted mask of the ROI ; (2) weighted climate data of the ROI; (3) time series evolution of the climate over the ROI for each ensemble member; (4) ensemble statistics of the projected change; and lastly, (5) an interactive visualization of the region’s precipitation change projected by the ensemble of selected climate model simulations for different Representative Concentration Pathways (RCPs). The visualization includes the temporal evolution of precipitation change over the course of the 21st century and statistical characteristics of the ensembles for two selected 30 year time periods for the mid and the end of the 21st century (e.g. median and various percentiles). The added value of the Climate Change Workflow is threefold. First, there is a reduction in the number of different software programs necessary to extract locally relevant data. Second, the intuitive generation and access to the weighted mask allows for the further development of locally relevant climate indices. Third, by allowing access to the locally relevant data at different stages of the data processing chain, scientists can work with a vastly reduced data volume allowing for a greater number of climate model ensembles to be studied; which translates into greater scientific robustness. Thus, the Climate Change Workflow provides much easier access to an ensemble of high-resolution simulations of precipitation, over a given ROI, presenting the region’s projected precipitation change using standardized approaches and supporting the development of additional locally relevant climate indices.
    Description: TechnicalInfo
    Description: Copyright 2022 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DE Flood Event Explorer Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Keywords: Digital Earth ; Flood ; DASF ; Workflow ; Climate Change ; ESGF ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 FLOODS ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION
    Type: Software , Software
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  • 10
    Publication Date: 2022-02-01
    Description: Abstract
    Description: The Digital Earth Flood Event Explorer supports geoscientists and experts to analyse flood events along the process cascade event generation, evolution and impact across atmospheric, terrestrial, and marine disciplines. It applies the concept of scientific workflows and the component-based Data Analytics Software Framework (DASF, Eggert and Dransch, 2021) to an exemplary showcase. It aims at answering the following geoscientific questions: - How does precipitation change over the course of the 21st century under different climate scenarios over a certain region? - What are the main hydro-meteorological controls of a specific flood event? - What are useful indicators to assess socio-economic flood impacts? - How do flood events impact the marine environment? - What are the best monitoring sites for upcoming flood events? The Flood Event Explorer developed scientific workflows for each geoscientific question providing enhanced analysis methods from statistics, machine learning, and visual data exploration that are implemented in different languages and software environments, and that access data form a variety of distributed databases. The collaborating scientists are from different Helmholtz research centers and belong to different scientific fields such as hydrology, climate-, marine-, and environmental science, and computer- and data science. It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/).
    Description: TechnicalInfo
    Description: Copyright 2022 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DE Flood Event Explorer Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Keywords: Digital Earth ; Flood ; DASF ; Workflows ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 FLOODS ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION
    Type: Software , Software
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