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Abstract
In urbanized areas of the developed world, light detection and ranging (LiDAR) exists alongside a wealth of other geospatial information. Despite this bounty, high-resolution land cover is still lacking in many urban areas. This can be attributed to the complexity of many landscapes, the volume of available data and the challenges associated with combining data that were acquired over differing time periods using inconsistent standards. Object-based approaches are ideal for overcoming these limitations. We describe the design, development and deployment of an object-based system that incorporated LiDAR, imagery and vector data sets to develop a comprehensive, multibillion-pixel land-cover data set for the City of Philadelphia. A novel approach using parallel processing allowed us to distribute the feature extraction load to multiple cores, providing massive gains in efficiency and permitting continual modification of the expert system until the accuracy goals of the project were achieved.