Urban internal function and structure which is represented by intra-urban land use information is important spa- tial-explicitly information in urban geography research and urban planning or management; however

its extraction of fine-scale spatial information is still a difficult question in geographic information science （GIS）. To overcome the difficulty in detecting intra-urban land use types using remotely sensed data alone and the positioning errors of mismatch in overlaying multi-temporal images

we have developed a digital reconstructing method to classify and detect intra-urban land use change through combining the "hierarchical classification" and "object-oriented segmentation" methods. This paper traces back to the historical developing process of Changchun city based on the above methods to classify and detect the detail intra-urban land use types including residential land

commercial land

industrial land

roads

water body and urban green space. The study has resolved the key problems on accurate spatial positioning from multi-scale and different data sources

classifying intra-urban land from function characteristics and detecting historical developing process of metropolis. Furthermore

we combined the SPOT5 imagery

1:10000 topographic maps

historical maps

urban planning map and other auxiliary data of Changchun City to classify and detect its intra-urban land use change from 1905 to 2003. The results indicate that the methods are significant and effective in classifying and detecting urban land use spatial information through combining the human-computer interactive interpretation with the expert-based knowledge from different source data. The methods can not only enhance the accuracy of urban land use classification

but also improve spatial information extraction efficiency and positioning accuracy of multi-temporal spatial overlaying analysis.