Due to the dynamic character of urban land use (e.g. urban sprawl) there is a demand for frequent updates for monitoring, modeling,<br />and controlling purposes. Urban land use is an added value that can be indirectly derived with the help of various properties of land<br />cover classes that describe a certain area and create a distinguishable structure. The goal of this project is to extract land use (LU)<br />classes out of a structure of land cover (LC) classes from high resolution Quickbird data and additional LiDAR building height models.<br />The study area is Rostock, a German city with more than 200.000 inhabitants. To model the properties of urban land use a graph based<br />approach is adapted from other disciplines (industrial image processing, medicine, informatics). A graph consists of nodes and edges<br />while nodes describe the land cover and edges define the relationship of neighboring objects. To calculate the adjacency that describes<br />which nodes are combined with an edge several distance ranges and building height properties are tested. Furthermore the information<br />value of planar versus non-planar graph types is analyzed. After creating the graphs specific indices are computed that evaluate how<br />compact or connected the graphs are. In this work several graph indices are explained and applied to training areas. Results show that<br />the distance of buildings and building height are reliable indicators for LU-categories. The separability of LU-classes improves when<br />properties of land cover classes and graph indices are combined to a LU-signature.
Walde, I., Hese, S., Berger, C., & Schmullius, C. (2012). GRAPH-BASED URBAN LAND USE MAPPING from HIGH RESOLUTION SATELLITE IMAGES. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. 1, pp. 119–124). Copernicus GmbH. https://doi.org/10.5194/isprsannals-I-4-119-2012