Summary
A great deal of geoscience knowledge exists in the form of unstructured text or maps, which are difficult to use by structured models or to process by computers. Thus, it is urgent to transform them to structured knowledge graph (KG). However, the development of geoscience KG (GKG) lags behind the general KG because it involves in the complexity of spatiotemporal relationships and knowledge from multisources. This study constructed a mountain vegetation KG (MVKG) incorporating with vegetation geographical principles, maps, and remote sensing (RS) images with the support of ArcGIS and deep learning method to facilitate the use of vegetation knowledge in various disciplines. The results showed that: 1) for the construction of a GKG, such as the MVKG, it is first necessary to define a strict
Dig deeper with Pulse AI.
Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.