The 2019 ESIP Winter Meeting has passed. See session descriptions to access meeting content, including presentations, recordings, and key takeaways. See here for info on upcoming meetings.
Session Abstracts: This session encourages use of semantic technologies which further the use and value of earth science data through linkage, interoperability and standardization. Sessions content will be organized as follows
Speaker: Dr. Dalia E. Varanka, Research Geographer. U.S. Geological Survey.
Title: Advances in Geospatial Semantics and Ontology of the National Hydrography Dataset Abstract: The study of surface water is a primary global priority, leading to the development of many perspectives about the way that representational data is collected, and how surface water interacts with other geographic phenomena. It is sometimes unclear how these perspectives reconcile to enable collaborative work and data sharing. A study of the semantics of the National Hydrography Dataset (NHD) of the U.S. Geological Survey has resulted in a formal specification of NHD as Web Ontology Language (OWL) files, a form of Resource Description Framework (RDF). The motivation for publishing NHD as RDF is for its topographic mapping context; RDF offers the opportunity to easily integrate hydro data with other geospatial features, such as transportation, structures, or administrative units for base map data applications. The semantics of these large, national databases were originally standardized in field survey observations of the nineteenth and twentieth centuries and converted to feature-based digital databases. Updated data model designs inscribed NHD semantics as geographic information systems (GIS) products. An automated mass-conversion of GIS attribute table data to RDF of sample NHD datasets resulted in RDF linked data. The ‘bottom-up’ approach of defining NHD semantics as prototype ontologies began with those converted data with manually re-aligned categories along upper ontology principles using Basic Formal Ontology (BFO). This first version was called the Surface Water Ontology (SWO) and was developed for comparative research, including hydro ontology alignment. Other USGS-funded research has contributed to solutions to efficiently handle coordinate geometry objects as linked open data. Ontology development is in progress for the National Hydrography Dataset Plus Elevation – High Resolution (NHDPLus HR) that combines NHD integrated with LiDAR elevation and watershed boundary data for applications at multiple, including local, geographic scales (USGS). This set of ontologies involves a surface water hydrology module that can be aligned along domain reference ontologies such as Hydro Foundational Ontology (HyFO); a geospatial science module for spatial concepts, and the specifications of the GIS-based NHDPlus HR data model for feature instances. Speaker(s): Dr. Chris Lynnes, NASA EOSDIS System Architect & Mr. Doug Newman, NASA EOSDIS - Raytheon Title: Smart Hand Offs & Earthdata Search Abstract: We will describe the concept of the ‘smart handoff’. This idea leverages the schema.org’s searchAction concept using simple markup to facilitate the transfer of user context between tools and services to provide an efficient workflow from discovery right through to acquisition. Finally, we will talk about how this concept could be expanded to other tools and services.
Speaker: Dr. Chris Lynnes, NASA EOSDIS System Architect & Mr. Doug Newman, NASA EOSDIS - Raytheon Title: Google Dataset Search & CMR Abstract: Describing the work NASA Earthdata CMR has done to leverage Google Dataset Search capabilities with respect to CMR HTML landing pages leveraging schema.org markup. We will demonstrate the markup we exploit and the way it is used to discovery and visualize results in Google Dataset Search. Finally, we will touch on areas where we think our interaction with Google Dataset Search can improve and grow.
Speaker: Dr. Lewis J. McGibbney, Data Scientist, Jet Propulsion Laboratory/California Institute of Technology Title: Publishing Geospatial Data as Linked Data: Graph Processing Techniques for Automated Feature Detection and Resolution within Hydrography GIS Products Abstract: Interesting, largely unexplored data analysis and information retrieval opportunities exist for GIS data. In their current form, traditional data usage patterns for data persisted in shapefiles or spatially-enabled relational databases are limited. Opportunities exist to achieve ESIP’s Winter 2019 theme of ‘increasing the use and value of Earth science data and information’ by transforming geospatial data from their original formats into their Resource Description Framework (RDF) manifestation. This work establishes an innovative workflow enabling the publication for Geospatial data persisted in geospatially enabled databases (PostGIS and MonetDB), ESRI shapefiles and XML, GML, KML, JSON, GeoJSON and CSV documents as graphs of linked open geospatial data. This affords the capability to identify implicit connections between related data that wasn't previously linked e.g. automating the detection of features present within large hydrography datasets as well as smaller regional examples and resolving features in a consistent fashion. This previously unavailable capability is achieved through the use of a semantic technology stack which leverages well matured standards within the Semantic Web space such as RDF as the data model, GeoSPARQL as the data access language and International Resource Identifier’s (IRI) for uniquely identifying and referencing entities such as rivers, streams and other water bodies. In anticipation of NASA’s forthcoming Surface Water Ocean Topography (SWOT – https://swot.jpl.nasa.gov) mission, which once launched in 2021 will make NASA’s first-ever global survey of Earth’s surface water, this work uses Hydrography data products (USGS’s National Hydrography Dataset and other topically relevant examples) as the topic matter. The compelling result is a new, innovative data analysis and information retrieval capability which will increases the use and value of Earth science data (GIS) and information.
Session Takeaways (post-meeting): 1) Adding geospatial metadata could allow for spatial queries among datasets, and is currently underutilized. Leveraging implementation of new large datasets may help rectify this. 2) End to End user-ship within geospatial data can integrate both the ESIP’s COS as well as google's non-spatial parsing.
