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Obtaining surface spatiotemporal data rapidly, automatically and accurately is an important issue in agriculture informationization and intellectualization. Samples obtained by manual and conventional manual visual interpretation are difficult to adapt the demands of current agricultural land resources information automatic extraction. At the same time, low altitude remote sensing technology as a kind of emerging technology for earth observation in recent years, with its flexibility, high efficiency, low cost, was widely used in the investigation of all kinds of resources. If only extraction information from single phase image, regardless of the historical image data set information extraction has been completed, it will cause information waste and repeated work. Based on this, spatiotemporal data mining technology was introduced, and related knowledge transfer learning mechanism was used, a novel landuse information classification method based on knowledge transfer learning (KTLC) was proposed. Firstly, new image was segmented by improved mean shift algorithm to obtain image objects. Secondly, the vector boundary of the objects and former historical landuse thematic map were matched and nested, invariant objects were obtained through overlay analysis, and purification of invariant object was finished by spectral and spatial information threshold filtering. The historical features category knowledge of thematic map was transferred to the new image objects. Finally, current images classification mapping was completed based on decision tree, and landuse classification mapping results were completed by the KTLC and eCognition for landuse information mapping classification (EC). The experimental results showed that KTLC could obtain accuracies equivalent to EC, and also outperforms EC in terms of efficiency.