Big Earth data are produced from satellite observations, Internet-of-Things, model
simulations, and other sources. The data embed unprecedented insights and spatiotemporal …

The present paper provides a review of two research topics that are central to geospatial
semantics: information modeling and elicitation. The first topic deals with the development of …

In a variety of applications relying on geospatial data, getting insights into heterogeneous
geodata sources is crucial for decision making, but often challenging. The reason is that it …

Current search engines in most geospatial data portals tend to induce users to focus on one
single-data characteristic dimension (eg popularity and release date). This approach largely …

The volume, variety, and velocity of different data, eg, simulation data, observation data, and
social media data, are growing ever faster, posing grand challenges for data discovery. An …

Geospatial data sharing is an inevitable requirement for scientific and technological
innovation and economic and social development decisions in the era of big data. With the …

Finding geospatial data has been a big challenge regarding the data size and heterogeneity
across various domains. Previous work has explored using machine learning to improve …

Discovering and accessing geospatial data presents a significant challenge for the Earth
sciences community as massive amounts of data are being produced on a daily basis. In this …

Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge
in the management of geospatial information. This paper proposes a workflow to identify …

For science to reliably support new discoveries, its results must be reproducible. Assessing
reproducibility is a challenge in many fields—including the geosciences—that rely on …