*Abstract* Maps are widely available for areas around the globe and are an important source of geospatial data. Due to the popularity of Geographic Information System (GIS), high quality scanners, and Internet, we can now obtain various maps in raster format. Comparing to other geospatial data, raster maps are easily accessible and provide geographic features that are difficult to find elsewhere, such as landmarks in historical maps. Moreover, for certain types of geographic features, raster maps contain the most complete set of data, such as the United States Geological Survey (USGS) topographic maps that have the contour lines of the entire United States in various scales. We can exploit the geographic features in raster maps (e.g., roads, text labels, etc.) to provide additional knowledge for viewing and understanding other geospatial data. For example, we can produce map context by extracting layers of geographic features (e.g., text layers) and recognizing the features (e.g., text labels) from raster maps. The map context then can be used for fusing the maps with other geospatial data and further for indexing and retrieval of the maps and the other geospatial data that are aligned to the raster maps. For instance, we can create a keyword-search function for imagery by exploiting the recognized text labels from raster maps that are aligned to the imagery. Harvesting the geographic features in raster maps is a challenging task because of the varying image quality (e.g., scanned maps with poor image quality and digital generated map with good image quality), the complexity of maps (i.e., overlapping features in maps), and the typical lack of metadata (e.g., map geocoordinates, map source, original vector data, etc.). To overcome these difficulties, I present two map decomposition techniques, each requiring a different amount of user input to first decompose raster maps with varying image quality into feature layers (i.e., a feature layer is an images of a particular geographic feature). Second, I present feature recognition techniques that convert the feature layers into machine-editable map context, such as extracting road vector data from a road layer. We can then fuse the extracted features layers and recognized features with other geospatial data to generate a hybrid view and create context of the integrated data, such as an notating roads by aligning an extracted text layer from a street map to imagery. In conclusion, my approach enables us to make use of the geospatial information of heterogeneous maps locked in raster format. *BIO* Yao-Yi Chiang is currently a Ph.D. candidate at the University of Southern California. He received his M.S. degree in Computer Science from the University of Southern California in December 2004; and his Bachelor degree in Information Management from the National Taiwan University in June 2000. His research interests are on the automatic fusion of geographical data. He has worked extensively on the problem of automatically utilize raster maps for understanding other geospatial data. He has written and co-authored several papers on automatically fusing map and imagery as well as automatic map processing. Prior to his doctoral study at USC, Yao-Yi worked as a Research Scientist for Information Sciences Institute and Geosemble Technologies.