Geographical or spatial information is now included in most of exchanged data. Sometimes, it is directly provided through metadata, but it is very often hidden and it becomes crucial to automatically discover it.

Natural Language Processing (NLP) and Data Mining communities have thus merged their efforts in order to extract geospatial information from textual documents, web pages, field data, and so forth. In this way, recent researches take into account the content of documents (e.g. terms) to identify geospatial data or to predict its geographic location.

Nevertheless, spatial information has some specificities that make discovering spatial information and/or spatial correlations from large amount of data still challenging. In this context, some proposals have been focused on the formalization of geospatial concepts and relationships, on the extraction of geospatial relations (e.g. rivers / body of water, town / suburb) in free texts to offer to the database community a unified framework for geodata discovery.

This workshop aims at discussing and assessing some of these strategies, involving NLP or Data Mining techniques, covering all or part of the issues mentioned above.

Topics of interest but not limited to:
- Geospatial information retrieval in documens
- Geospatial knowledge acquisition from documents
- Classification of geospatial documents
- Geospatial analysis of textual data
- Integration of geospatial documents
- Extraction of geospatial information from documents
- Geospatial theasurus/ontology building from documents
- Quality of extracted geospatial information
- Analysis and integration of geospatial data from web documents
- Visualization of geospatial information from documents