Remote sensing analytical geospatial operations directly in the web browser

Masó J., Zabala A., Serral I., Pons X. (2018) Remote sensing analytical geospatial operations directly in the web browser. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 42: 475-482.
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Doi: 10.5194/isprs-archives-XLII-4-403-2018

Resum:

Current map viewers that run on modern web browsers are mainly requesting images generated on the fly in the server side and transferred in pictorial format that they can display (PNG or JPEG). In OGC WMS standard this is done for the whole map view while in WMTS is done per tiles. The user cannot fine tune personalized visualization or data analysis in the client side. Remote sensing data is structured in bands that are visualize individually (manually adjusting contrast), create RGB combinations or present spectral indices. When these operations are not available in map browsers professional are forced to download hundreds of gigabytes of remote sensing imagery to take a good look at the data before deciding if it fits for a purpose. A possible solution is to create a web service that is able to perform these operations on the server side (https://www.sentinel-hub.com). This paper proposes that the server should communicate the data values to the client in a format that the client can directly process using two new additions in HTML5: canvas edition and array buffers. In the client side, the user can interact with a JavaScript interface changing symbolizations and doing some analytical operations without having to request any data again to the server. As a bonus, the user is able to perform queries to the data in a more dynamic way, applying spatial filters, creating histograms, generating animations of a time series or performing complex calculations among bands of the different loaded datasets. © Authors 2018.

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Rubric-Q: Adding quality-related elements to the GEOSS clearinghouse datasets

Zabala A., Riverola A., Serral I., Diaz P., Lush V., Maso J., Pons X., Habermann T. (2013) Rubric-Q: Adding quality-related elements to the GEOSS clearinghouse datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6: 1676-1687.
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Doi: 10.1109/JSTARS.2013.2259580

Resum:

Geospatial data have become a crucial input for the scientific community for understanding the environment and developing environmental management policies. The Global Earth Observation System of Systems (GEOSS) Clearinghouse is a catalogue and search engine that provides access to the Earth Observation metadata. However, metadata are often not easily understood by users, especially when presented in ISO XML encoding. Data quality included in the metadata is basic for users to select datasets suitable for them. This work aims to help users to understand the quality information held in metadata records and to provide the results to geospatial users in an understandable and comparable way. Thus, we have developed an enhanced tool (Rubric-Q) for visually assessing the metadata quality information and quantifying the degree of metadata population. Rubric-Q is an extension of a previous NOAA Rubric tool used as a metadata training and improvement instrument. The paper also presents a thorough assessment of the quality information by applying the Rubric-Q to all dataset metadata records available in the GEOSS Clearinghouse. The results reveal that just 8.7% of the datasets have some quality element described in the metadata, 63.4% have some lineage element documented, and merely 1.2% has some usage element described. © 2013 IEEE.

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Emerging data quality from GEOSS integrated clearinghouses

Serral I., Diaz P., Maso J., Pons X. (2012) Emerging data quality from GEOSS integrated clearinghouses. International Geoscience and Remote Sensing Symposium (IGARSS). : 2744-2747.
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Doi: 10.1109/IGARSS.2012.6350358

Resum:

The GEOSS Common Infrastructure (GCI) provides a Clearinghouse (the GEOSS registry and metadata catalogue) and a GEOPortal to discover and visualize EO data in an integrated, standardized and interactive way, as well as broadly use it by the scientific community when dealing with representation and modeling of Earth Systems. EO data sources are ideally elaborated following quality assessment procedures, resulting in quality estimates and other related indicators. The objective of this indicators is to allow users deciding about data fitness for a purpose, but in practice systems providing methods to distribute, show and exploit this producer quality information in a standard and interoperable way are rarely used. This work aims to extract information about data quality from GCI metadata and analyze the obtained results. Additionally, an XML specific tool is able to quick and visually punctuating the metadata that refers to quality. © 2012 IEEE.

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