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Many interesting geospatial datasets are publicly accessible on web sites and other online repositories. However, the sheer number of datasets and locations, plus a lack of support for cross-repository search, makes it difficult for researchers to discover and integrate relevant data. We describe here early results from a system, Klimatic, that aims to overcome these barriers to discovery and use by automating the tasks of crawling, indexing, integrating, and distributing geospatial data. Klimatic implements a scalable crawling and processing architecture that uses an elastic container-based model to locate and retrieve relevant datasets and to extract metadata from headers and within files to build a global index of known geospatial data. In so doing, we create an expansive geospatial virtual data lake that records the location, formats, and other characteristics of large numbers of geospatial datasets while also caching popular data subsets for rapid access. A flexible query interface allows users to request data that satisfy supplied type, spatial, temporal, and provider specifications; in processing such queries, the system uses interpolation and aggregation to combine data of different types, data formats, resolutions, and bounds. Klimatic has so far incorporated more than 10,000 datasets from over 120 sources and has been demonstrated to scale well with data size and query complexity.

Klimatic: A Virtual Data Lake for Harvesting and Distribution of Geospatial Data.