Working Group III/4

Remote sensing in urban areas has been a challenge for quite some time due to complexity and fragmentation of objects and the combination of man-made features and natural features. Airborne laser altimetry data offer possibilities for feature extraction and spatial modelling in urban areas. There are many approaches of deriving buildings and other features currently available in literatures. However, there are many cases, which are still difficult for particular features to be extracted by using these approaches. For instance, in an urban area where many roads are raised above ground level with special characters similar to buildings. Building extraction in such a complicated urban context is still a difficult task for these available approaches. The proposed approach was developed to solve this type of cases. It tries to extract buildings through reasoning in a layer space in general. In the proposed approach, airborne laser altimetry data in raster format was segmented by using several thresholds with 1-meter interval of altitude. These image segments were then labelled and assigned with unique label values, which are treated as image objects. Hence, a number of properties can be derived based on labelled segments (image objects) such as size, shape, orientation etc. These properties are used for reasoning in the layer space. The layer space is defined as such that use altitude with 1-meter interval as a variable in X-axis and use these properties as functions of altitude in Y-axis. Vertically segmented image objects are linked and inferred vertically as well. A tree structure was created using links between different layers of segments vertically. Reasoning is based on patterns of these properties on the paths of each branch of searching tree in the layer space. Sequential experiments have been tested in a study area, southeast of Amsterdam based on the proposed approach. The test results show that the proposed approach is a robust and reliabl