A knowledge-based, transferable approach for block-based urban land-use classification

In this work we propose a knowledge-based approach for land-use classification of city blocks through the automatic interpretation of very-high-resolution remote-sensing imagery. Our approach is founded on geographic object-based image analysis (GEOBIA) concepts and is concerned with transferability across distinct knowledge representation formalisms. This paper therefore investigates the viability of translating a high-level description of the interpretation problem into the particular knowledge representation structures and interpretation strategies of two different software platforms, namely the proprietary Definiens Developer system and the open-source InterIMAGE system. Initially, textual descriptions of the land-use classes of interest were created by photo interpreters. Then, generic class descriptions were defined as a system-independent knowledge model, which was subsequently translated into interpretation projects in the different systems. Altogether 49 blocks located on two different test-sites in the city of Sao Paulo (Brazil) were considered in the experiments. Although the classification results from the Definiens Developer system were slightly better than those obtained with the InterIMAGE system, we concluded that both systems have been shown to be equally qualified to implement the target application properly through adaptation of the generic knowledge model.