Low or medium spatial resolution satellite images are used for environmental monitoring in remote, extremely cold areas such as Antarctica. However, they cannot provide detailed spatial and spectral information over Antarctic areas. For obtaining this information, we propose an Antarctic land-cover classification method using IKONOS and Hyperion data over Terra Nova Bay, Antarctica. High spatial resolution IKONOS imagery enabled the detection of detailed, accurate boundaries between areas of snow, ice, vegetation, water and rock. Rock types were classified in detail using high spectral resolution Hyperion imagery. Because Antarctic land-cover types exhibit unusual spectral features, a step-by-step classification using various data sets and methods was applied. Five soil types and two rock types were identified by applying spectral linear unmixing to Hyperion data. Our land-cover classification map shows small mossy areas near emperor penguin or skua habitat. Lakes, which are an important factor in environmental monitoring, are also clearly shown. The detailed rock and soil classification map revealed that biotite gneiss and clay soils are dominant. This Antarctic land-cover map is useful for determining a suitable site for an environment-friendly Antarctic station. Among the various land-cover types, the lakes and the vegetation, rock and soil areas are considered to be important environmental factors related to Antarctic living organisms.

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Recently, with the increasing interest in facility management based on indoor spatial information, various studies have been attempted to manage facility conversion between BIM and GIS. Visualization of the geometry data for a large-scale is one of the major issues to the maintenance system. Therefore, this study designed the spatial indexing algorithm through an IFC schema-based scenario for the effective visualization of BIM data based on GIS. A part of the algorithm was developed implementing the OcTree structure and this research has a test for the developed output with IFC sample data. Ultimately, we propose the spatial indexing method for the effective visualization of BIM data based on GIS.