Evaluation on geospatial information extraction and retrieval: Mining thematic maps from web source

The World Wide Web easily becomes the largest repository of natural language text data. We are particularly interested in state-of-the-art methods in exploiting geospatial information the web. The survey is done in the context of its extraction methods, retrieval, visualization, and further possible mining or knowledge discovery scenarios in order to produce thematic maps automatically from the web corpus. We found that Web-based Geographic Information Retrieval (GIR) methods that returns selected relevant area instead of points is still lacking, even though area modeling is common in GIS. We also found that most GIR methods is still focused on places and buildings instead of theme or information around some area. Thus it indicates that the state of the art GIR methods are not yet sufficient for thematic extraction and retrieval to generate thematic maps from web corpus. Bayesian topic models such as Latent Dirichlet Allocation may serve as a good basis to serve such use cases.