GeoNode is an open source framework designed to build geospatial content management systems (GeoCMS) and spatial data infrastructure (SDI) nodes. Its development was initiated by the Global Facility for Disaster Reduction and Recovery (GFDRR) in 2009 and adopted by a large number of organizations in the following years. Using an open source stack based on mature and robust frameworks and software like Django, OpenLayers, PostGIS, GeoServer and pycsw, an organization can build on top of GeoNode its SDI or geospatial open data portal. GeoNode provides a large number of user friendly capabilities, broad interoperability using Open Geospatial Consortium (OGC) standards, and a powerful authentication/authorization mechanism. Supported by a vast, diverse and global open source community, GeoNode is an official project of the Open Source Geospatial Foundation (OSGeo).

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On the web there is a large number of useful geospatial datasets available, exposed via web map services using open standards or open protocols. Just as web search engines enable users to reliably search and find relevant documents, a similar capability is needed to return the most useful and reliable geospatial datasets. Hypermap Registry is an open source platform, developed by the Center for Geographic Analysis (CGA) of Harvard University, which attempts to address the general problem of geospatial data search and discovery.

A spatial data infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide an efficient and flexible way to use spatial information. One of the key software components of an SDI is the catalogue service which is needed to discover, query and manage the metadata. Catalogue services in an SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard which defines common interfaces for accessing the metadata information. A search engine is a software system capable of supporting fast and reliable search, which may use 'any means necessary' to get users to the resources they need quickly and efficiently. These techniques may include full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting, recommendations and many others. In this paper we present an example of a search engine being added to an SDI to improve search against large collections of geospatial datasets. The Centre for Geographic Analysis (CGA) at Harvard University re-engineered the search component of its public domain SDI (Harvard WorldMap) which is based on the GeoNode platform. A search engine was added to the SDI stack to enhance the CSW catalogue discovery abilities. It is now possible to discover spatial datasets from metadata by using the standard search operations of the catalogue and to take advantage of the new abilities of the search engine, to return relevant and reliable content to SDI users.