All in One: Encoding spatio-temporal big data in XML, JSON, and RDF without information loss

Baumann P., Hirschorn E., Maso J., Merticariu V., Misev D. (2018) All in One: Encoding spatio-temporal big data in XML, JSON, and RDF without information loss. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. 2018-January: 1-10.
Link
Doi: 10.1109/BigData.2017.8258326

Abstract:

With the unprecedented availability of continuously observed and generated data there is a likewise unprecedented potential for new and timely insights; yet, benefits are not fully leveraged as of today. The plethora of formats in combination with heterogeneous services remains is an obstacle-e.g., image services prefer binary formats, SPARQL endpoints like to think in RDF triples, and browsers integrate JSON data smoothly. We propose a model-based multi-encoding approach for overcoming the limitations of individual formats while still supporting their use. Concretely, this approach is being followed by the OGC Coverage Implementation Schema (CIS) standard which establishes a concrete, interoperable data model unifying n-D spatiotemporal regular and irregular grids, point clouds, and meshes. We describe how independence from data formats is achieved, in particular for three practically relevant formats-XML, JSON, and RDF-, thereby fostering integration of hitherto rather separate application domains. © 2017 IEEE.

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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.
Link
Doi: 10.5194/isprs-archives-XLII-4-403-2018

Abstract:

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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