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Detecting topographic changes in the urban environment has always been animportant task for urban planning and monitoring. In practice, remote sensingdata are often available in different modalities and at different time epochs.Change detection between multimodal data can be very challenging since the datashow different characteristics. Given 3D laser scanning point clouds and 2Dimagery from different epochs, this paper presents a framework to detectbuilding and tree changes. First, the 2D and 3D data are transformed to imagepatches, respectively. A Siamese CNN is then employed to detect candidatechanges between the two epochs. Finally, the candidate patch-based changes aregrouped and verified as individual object changes. Experiments on the urbandata show that 86.4\% of patch pairs can be correctly classified by the model.

Change Detection between Multimodal Remote Sensing Data Using Siamese CNN