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  • German  (3)
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  • 1
    Publication Date: 2020-02-12
    Description: Remote sensing data analysis retrieves spatial-temporal information about the Earth‘s surface from remotely sensed optical and radar images. For this purpose accurate and efficient classification or parameter quantification techniques must be used. Consequently, there exists a long tradition in remote sensing to employ methods and techniques from the field of machine learning. They can be regarded as „universal function approximators“ that are able to link any data in order to derive connections, conclusions and predictions efficiently using different learning strategies. In the following, current research topics of the Remote Sensing section of the GFZ are presented, in which different forms of machine learning are used.
    Language: German
    Type: info:eu-repo/semantics/article
    Format: application/pdf
    Format: application/pdf
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  • 2
    Publication Date: 2020-02-12
    Description: Satellite remote sensing offers the possibility to continuously, globally collect information about Earth’s surface properties, and thus possesses an enormous scientific and economic potential. The spatio-temporal recording of information opens up a wide range of possibilities for large-scale monitoring of changes and influencing factors, e.g. in atmospheric research, agriculture and forestry, geology, urban areas and the analysis of natural hazards. The European Union‘s Copernicus programme marks the beginning of a new era in satellite-based Earth observation by designing and launching the Sentinel series, an operational fleet of high-continuity, multi-combinable observation systems. This enables completely new objects of investigation, but also requires a new level of automated data processing in order to make the huge amounts of data available for analysis. This article provides an insight into current remote sensing research at the GFZ. First, it presents the activities related to the creation of preprocessing standards and interfaces for data evaluation, geo-data management and visualization. In the following, examples for multi-sensor analysis of land use potentials and natural hazards are shown, in order to finally discuss the role of the GFZ in the development of hyperspectral satellite missions and related data analysis.
    Language: German
    Type: info:eu-repo/semantics/article
    Format: application/pdf
    Format: application/pdf
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  • 3
    Publication Date: 2020-02-12
    Description: Progress in sensor systems and computer simulation create large volumes of data with a variety of parameters. This development brings Big Data and the related challenges for data processing and data analysis also into the focus of geoscience. Computer science has developed concepts and technologies to handle Big Data. Geoscience can benefit from them since they facilitate efficient information extraction from big data, such as data from satellite-based remote sensing systems, or data from seismological or meteorological observation systems. To make use of computer science concepts and technologies, they have to be adapted into geoscience. Examples for this adaption are the development of efficient scalable geoscientific analysis methods by applying the divide-and-recombine concept, or the adaption of geoscientific methods to existing Big Data technologies.
    Language: German
    Type: info:eu-repo/semantics/article
    Format: application/pdf
    Format: application/pdf
    Location Call Number Limitation Availability
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