dataesr/scientific-paragraphs-categorization huggingface dataset:
GeoNode GeoNode is another open-source tool (OSGeo Project) that supports the development of SDI, among others. For this purpose, it includes components for the creation and management of GI-metadata for the documentation and discovery of geographic information and geographic Web services. Using this platform, users can browse and retrieve geographic information available in the SDI. Geographic information, as well as its associated metadata, is downloadable. Various formats are supported for both. Especially for the case of GI-metadata, metadata of data layer can be downloaded in compliance with ATOM, DIF, Dublin Core, EbRim, FGDC, and ISO/TC 211  [46]. More information is available on GeoNode at http://geonode. org/ The French INSPIRE SDI is composed of two main systems, the Géocatalogue and the Géoportail  [47]  (https://www.
geoportail. gouv. fr). The Géoportail is a web interface for viewing and accessing spatial information from public authorities, operated by the Institut Géographique National (IGN) of France, whereas the Géocatalogue is the catalogue presenting the different data and services concerned by the INSPIRE Directive and is operated by the French Geological Survey  [48]. The Géocatalogue was developed by the BRGM using an n-tier architecture based on a PostgresSQL database, java APIs, and a Solr search engine  [49]. It is populated by continuously harvesting member administration catalogues (IGN, local authorities, etc. ) which mainly used Geonetwork, which provides a built-in INSPIRE compliant metadata editor and cataloguing service. The Géocatalogue implements the INSPIRE profile of ISO 19115:2003.
Discussion In this paper, we provide a review of the development and evolution of metadata standards for geospatial information. While metadata for maps have been around for centuries, the advent of digital geographic information brought new challenges and opportunities. The importance of metadata standards is confirmed by the fact that internationally (and in many countries) this was one of the first standards to be developed for digital geospatial information. The need and usefulness of metadata standards are further illustrated by the many profiles and implementations of metadata standards. ISO/TC 211 was built on existing metadata standards and initiatives, but is also extensible, so that nations or organizations can develop profiles (specializations and/or extensions of a standard) for their own specific needs.
The review further shows that apart from the 'basic' metadata elements, such as those included in Dublin Core, today, GI-metadata standards cover a vast variety of specialized metadata elements, describing, for example, lineage, imagery, quality, preservation, services, and quality assurance. Metadata (and other) standards need to constantly evolve and expand in response to changing user requirements and technological advances. Recent developments provide advanced features, such as cultural and linguistic adaptability. In the face of constant change, it is also important to consider backward compatibility and to ensure long term preservation of data and metadata. These are addressed in various ways in the GI-metadata standards. ISO compliant metadata is essential for discovery of geospatial data and its appropriate use.
Ennis et al. [52]  describe an automated approach for creating semantic geospatial metadata for photographs, which can facilitate photograph search and discovery. Others have explored ways of assessing and improving the quality of metadata, e. g. , by assessing metadata for standards compliance  [53]  and by making users aware of the quality of geospatial metadata during the collection of VGI  [54]. An analysis of the metadata elements collected through these different automation approaches will help to identify metadata elements that can be collected automatically. Such information would be useful for anyone developing an ISO 19115 profile that requires no (or at least minimal) human intervention. Once the metadata is available, the next challenge lies in making it discoverable. GI-metadata are often available through geoportals that are known primarily in geoinformation communities.
Additionally, the information in a geoportal is not readily available for indexing by web crawlers. Researchers have proposed various ways of overcoming this challenge. One approach is to enhance web crawler functionality so that it can discover geospatial metadata in geoportals  [55, 56]  or enhance a search engine's capabilities for assessing the fitness of use of geospatial datasets  [57]. Another approach is to include geospatial metadata in HTML pages, which are readily accessible by web crawlers  [58, 59]. Such pages could be automatically generated, as Fugazza et. al. [60]  have done. In addition to 'traditional' metadata that are processable by traditional geospatial catalogs, semantics-aware RDF representations were generated and could be queried as part of the Semantic Web. For these approaches, it is necessary to map ISO 19115 and its profiles to other metadata standards, such as Dublin Core.
A variety of such mappings have been done  [61]. ISO/TC 211 has also recognized the need to support the Web of data (i. e. , the Semantic Web)  [26]. OWL ontologies have been derived for the ISO GI-metadata standards (and others) and are accessible by anyone in the geographic information community (http://def. isotc211. org/), enabling them to describe, share, and give access to geographic information resources on the Semantic Web.
Recent GI-metadata standards introduced cultural and linguistic adaptability, which is essential for today's global use of geographic information. ISO geographic information standards are increasingly modularized, and ISO/TC 211 is more and more adapted to delivering suitable solutions, as such, further developments in geographic information standards can also address more specific aspects with special metadata properties and still be connected with the fundamental standards. Currently, there are standardization activities at ISO/TC 211 on transportation, ubiquitous geographic information, land administration, addressing, land cover and land use, coverages, services, etc. , all of which may require special attention for metadata. New technologies will also challenge ISO/TC 211 to deliver not only standards but also resources to make standards applicable and implementable in various contexts (e.
Summary of the classes of ISO 19115-2 structure for acquisition and processing. Class Description MI_AcquisitionInformation designations for the measuring instruments, the platform carrying them, and the mission to which the data contributes MI_Metadata root entity that defines information about acquisition and processing of geographic information (MD_Metadata extended) MI_Platform designation of the platform used to acquire the data set MI_Instrument list of events related to platform/instrument/sensor MI_Sensor specific type of instrument MI_Objective describes the characteristics, spatial and temporal extent of the intended object to be observed MI_EnvironmentalRecord information about the environmental conditions during the acquisition MI_Requirement requirement to be satisfied by the planned data acquisition MI_Operation designations for the operation used to acquire the dataset MI_Plan designations for the planning information related to meeting the data acquisition requirements 3.
3. 1. 6. ISO 19157:2013, Geographic Information-Data Quality Geospatial data quality is defined as the characteristic of a geospatial resource that allows it to satisfy stated and implied needs. Data producers provide data quality information to state how well a dataset represents the real world as defined by a data product specification. Users can then evaluate the data quality information to determine a dataset's fitness for use. In 2013, ISO 19157 replaced ISO 19113:2002, Geographic information-Quality principles, ISO 19114:2003, Geographic information-Quality evaluation procedures, and ISO 19138:2006, Geographic information-Data quality measures. First, ISO 19157 defines principles for describing the quality of geographic information and specifies metadata elements for reporting it by:.
2003 1st Withdrawn, superseded by ISO 19157 ISO 19115, Geographic information-Metadata Defines metadata elements and schema describing geospatial datasets. 2003 1st Withdrawn, superseded by ISO 19115-1:2014 ISO 19115-1, Geographic information-Metadata-Part 1: Fundamentals Defines metadata elements and schema describing geospatial datasets. 2014 2nd Published ISO 19115-2, Geographic information-Metadata-Part 2: Extensions for imagery and gridded data Extends ISO 19115 (original metadata standard) by defining additional metadata elements and schema describing imagery and gridded geospatial datasets. 2009 1st Withdrawn, superseded by ISO 19115-2:2019 Extends ISO 19115-1 (revised metadata standard) by defining the schema ISO 19115-2, Geographic required for an enhanced description of the acquisition and processing of information-Metadata-Part 2: Extensions for geographic information, including imagery.
• Common : Common Additional set of metadata elements for a more complete description of any type of resource supported by DMF. It extends DMF/Core. • Services: Extension of DMF/Core for service metadata that can be used together with DMF/Common (i. e. , it is possible to use DMF/Services on top of DMF/Core if DMF/Common elements are not needed or on top of DMF/Common for an extensive profile). • Data: extension of DMF/Common for data related resources (i. e. , dataset, series, and tile). • Data+: Extension of DMF/Data for the implementation of coverage quality results and other metadata elements introduced in ISO 19115-2 but with some applications for any geospatial products. • Sensor: Extension of DMF/Data for sensors. • Defence: Extension of DMF/Common for NATO and more generally military oriented needs (particularly for security).
</dc:source> <dc:format>Ocean Data View ASCII input</dc:format> <ows:BoundingBox crs="urn:ogc:def:crs:EPSG:6. 6:4326"> <ows:LowerCorner>-3. 88585 48. 6959</ows:LowerCorner> <ows:UpperCorner>-3. 89569 48. 70609</ows:UpperCorner> </ows:BoundingBox> <dc:URI protocol="HTTP-DOWNLOAD">http://www. sdn-taskmanager. org/</dc:URI> </csw:Record> </csw:GetRecordByIdResponse> • Support collaboration and cooperation among departments and organizations by facilitating the sharing of geospatial resources regardless of the GIS platform, • Gain an enterprise-level awareness of disparate geospatial data, Web services, and activities, • Leverage existing geospatial resources so the organization does not duplicate those resources or the effort to create them, • Ensure the use of approved, high-quality datasets, and • Reduce the time users spend trying to find relevant, usable geospatial resources.
A Modularity-based Preprocessor to Detect Relevant Learned Clauses Learning new clauses during the execution of the SAT solver is undoubtedly a crucial component of state-of-the-art CDCL SAT solvers; their success would not have been possibly achieved without the incorporation of clause learning within them. In fact, Sakallah and Marques-Silva (2011) conducted an analysis to determine which CDCL component contributes in a higher degree to the success of these solvers, and concluded it is indeed clause learning. Clause learning can be seen as a two-sided effect. On the one hand, adding new redundant 9 clauses avoids the solver to explore the same subspace(s) in the future. However, this makes the management of the clause database harder, since it can grow exponentially. This especially affects Unit Propagation (UP), since it is more costly to detect which clauses may propagate at each step.
Acknowledgments This work is partially supported by the  EU H2020 Research and Innovation Programme  under the  LOGISTAR  project (Grant Agreement No. 769142 ),  MINECO-FEDER  projects  RASO  ( TIN2015-71799-C2-1-P/2-P ) and  TASSAT3  ( TIN2016-76573-C2-2-P ), the  Spanish Ministerio de Economía y Competitividad  under the  EXASOCO  project (ref. PGC2018-101216-B-I00 ), including  European Regional Development Funds (ERDF). The third author is also supported by a  MICINN Juan de la Cierva  fellowship (ref. FJCI-2017-32420 ).
Vicarious calibration approaches using in situ measurements saw first use in the early 1980s and have since improved to keep pace with the evolution of the radiometric requirements of the sensors that are being calibrated. The advantage of in situ measurements for vicarious calibration is that they can be carried out with traceable and quantifiable accuracy, making them ideal for interconsistency studies of on-orbit sensors. The recent development of automated sites to collect the in situ data has led to an increase in the available number of datasets for sensor calibration. The current work describes the Radiometric Calibration Network (RadCalNet) that is an effort to provide automated surface and atmosphere in situ data as part of a network including multiple sites for the purpose of optical imager radiometric calibration in the visible to shortwave infrared spectral range.
The key goals of RadCalNet are to standardize protocols for collecting data, process to top-of-atmosphere reflectance, and provide uncertainty budgets for automated sites traceable to the international system of units. RadCalNet is the result of efforts by the RadCalNet Working Group under the umbrella of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and the Infrared Visible Optical Sensors (IVOS). Four radiometric calibration instrumented sites located in the USA, France, China, and Namibia are presented here that were used as initial sites for prototyping and demonstrating RadCalNet. All four sites rely on collection of data for assessing the surface reflectance as well as atmospheric data over that site. The data are converted to top-of-atmosphere reflectance within RadCalNet and provided through a web portal to allow users to either radiometrically calibrate or verify the calibration of their sensors of interest.
Introduction Earth observation (EO) is used for a wide range of societal and commercial applications. Increasingly users are combining data from different sensors, either to provide long-term records of environmental change that require time series greater than the lifetime of a single sensor, or to provide operational services using data from different sensors (from different satellites) to maximize coverage. Both the space agencies and commercial operators are launching a wider range of sensors with different spectral bands, and increasingly small, relatively cheap sensors are being launched with no on-board calibration (e. g. , onboard CubeSats). Post-launch radiometric performance assessment of Earth observation optical sensors is essential to understand changes occurring after preflight laboratory characterizations and calibrations.
One of the first applications of such techniques dates to  [7] , who measured the radiance above a ground target from a high-altitude aircraft to verify the degradation of the response of the Coastal Zone Color Scanner's shorter wavelength bands  [8]. Sensors on an Earth Resources (ER-2) aircraft provided cross-calibration of the Advanced Very High Resolution Radiometer (AVHRR) via sensors onboard the ER-2 aircraft  [9]. The basic concept of such comparison involves two sensors viewing the same instrumented site at the same time from the same viewing geometry with identical spectral bands. Teillet et al. developed a variation of the approach to account for the unavoidable small differences in view and solar geometry  [10]. The method relies on spatially and spectrally characterizing the surface reflectance from aircraft data and using the reflectance with coincident atmospheric data to predict the at-sensor radiance.
Such an approach was used to compare data from a wide array of sensors viewing the Railroad Valley instrumented site on a single day  [11]  and has been used to cross-compare data from sensors viewing the site on different dates  [12]. The reflectance-based approach was developed in the 1980s for the calibration of Landsat 4 and 5 Thematic Mapper  [13, 14]. The reflectance-based method relies on measurements of surface reflectance and atmospheric conditions at a selected instrumented site nearly coincident in time with the overpass of an imager to be calibrated. The use of a radiative transfer code permits the calculation of the top-of-atmosphere (TOA) radiance that is used to evaluate the radiometric calibration of the sensor being evaluated. The method has been successfully used for many sensors relying on several instrumented sites with data collected by groups in multiple countries  [15] [16] [17] [18] [19] [20] [21]  One drawback of the reflectance-based approach is that poor weather, budget limitations, and personnel constraints can limit the number of successful data collections.
This paper describes the overall Radiometric Calibration Network (RadCalNet) concept, including descriptions of the initial four sites that are part of the network. The processing approach for RadCalNet is presented along with sample uncertainties, and more importantly, the approaches that are being implemented within RadCalNet to ensure that network members are following appropriate methods for determining uncertainties. The last section of the paper shows an example interconsistency study between two sensors and two independent instrumented sites to illustrate how RadCalNet users will benefit from the data available from the network.
RadCalNet Overview RadCalNet, the Radiometric Calibration Network, is a network of sites that can be used to compare different satellite sensors to each other and to a common reference. RadCalNet was proposed and implemented within the Infrared Visible Optical Sensors (IVOS) Subgroup of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and was made publicly available in June 2018 as a culmination of almost two decades' effort within WGCV and IVOS, beginning with the definition of the original LandNet sites  [23]. RadCalNet is based on the reflectance-based approach described above, with continuous deployment of automated instrumentation that is calibrated traceably to SI and with known, and peer-reviewed, uncertainties. Results from previous automated sites  [24]  and  [25]  have shown that they can achieve uncertainties similar to collection  [26]  The RadCalNet concept has been developed to address temporal sampling issues, and to provide global consistency, by networking the measurements from several automated sites and making these data widely available to teams responsible for the in-flight monitoring of sensors as well as to users of those sensors.
Data from the different automated instrumented sites are quality controlled and processed to TOA reflectances (see Section 2. 3) that are suitable for vicarious calibration or radiometric monitoring of a sensor viewing the sites. There are admittedly limitations as to how these data can be used for the in-flight assessment of spaceborne sensors as they are provided only at a 30-min interval for a nadir viewing configuration only. Although this limits the possibilities of matching up the data with spaceborne sensors with off-nadir viewing angles, it should allow for intercomparison with sensors viewing any of the sites with low viewing angles. While additional data might be gathered by site owners at each site allowing for off-nadir and higher temporal or spectral resolution TOA reflectance simulations, these are not available through RadCalNet (site owners should be contacted directly).
The data are made available through a web portal to registered users  [28]. This paper describes this networked approach along with the methods applied to ensure consistency of the results from the different sites and the SI-traceability of the data. There are six parts of RadCalNet: 1) Instrumented sites and their operators; 2) RadCalNet Quality Control and Processing; 3) RadCalNet Data Distribution; 4) RadCalNet Working Group; 5) RadCalNet Admission Review Panel; and 6) RadCalNet users. Figure  1  illustrates the relationships between the different parts. The instrumented sites and their operators are the basis of the data that is the key to RadCalNet. Participation in RadCalNet is on a voluntary basis, and as part of membership in RadCalNet, the instrumented sites agree to provide their data and uncertainties in a specific format and undergo peer review evaluation of the uncertainties associated to their data.
The initial instrumented site membership includes the four sites described below. initial instrumented site membership includes the four sites described below. The data from the sites are provided to the RadCalNet Processor: a central facility that ensures the data from the individual sites are processed with the same approach to provide TOA reflectances and associated uncertainties based on the inputs from the instrumented site. The results from the RadCalNet processing are provided to the RadCalNet Data Distribution that provides the interface to the user community to distribute the results. The RadCalNet Data Distribution also provides quality assurance (QA) on the products and provides metadata and documentation of the RadCalNet approach. It is expected that new sites will seek inclusion in RadCalNet.
All sites currently sample the reflectance at the requested times (and, in reality, at higher temporal frequency), and no interpolation for time is required for the typical case where all data are successfully acquired by the instrumentation. Three of the sites collect the reflectance data at multispectral wavelengths, and these data are combined with representative spectra derived from site measurements to provide the 10 nm hyperspectral data needed by RadCalNet. RadCalNet requires the following atmospheric information: aerosol optical depth at 550 nm, Angstrom coefficient, column water vapor, and column ozone. An aerosol type corresponding to one of the standard inputs that are part of the MODTRAN radiative transfer code is also needed. The aerosol parameters are derived from the closest measurement in time at the sites, but this difference is typically less than a few minutes at each of the sites.
Ideally, one would have the same number of data points when comparing the impact of site-related uncertainties, but weather conditions and other factors limit the opportunity for such a controlled evaluation. Of course, the discussion above depends on there being no systematic differences larger than absolute uncertainties at the multiple sites. Visual inspection of the data in Figure  7  indicates that the results from Site B appear to have a lower ratio than those from Site A. The difference is readily apparent in the data from 2015. A first evaluation of the two datasets examined whether either site indicates a trend in the sensor's radiometric response or possibly a nonlinear response in relation to the surface reflectance levels of site A and B. As mentioned previously, the data from Site A for this spectral band near 650 nm do not indicate a temporal change in the ratio that is statistically different from zero at the 5% confidence level.