High resolution satellite imagery and 3D laser point cloud data provide precise geometry, rich spectral information and clear texture of feature. The segmentation of high resolution remote sensing images and 3D laser point cloud is the basis of object-oriented remote sensing image analysis, for the segmentation results will directly influence the accuracy of subsequent analysis and discrimination. Currently, there still lacks a common segmentation theory to support these algorithms. So when we face a specific problem, we should determine applicability of the segmentation method through segmentation accuracy assessment, and then determine an optimal segmentation. To today, the most common method for evaluating the effectiveness of a segmentation method is subjective evaluation and supervised evaluation. For providing a more objective evaluation result, we have carried out following work. Analysis and comparison previous proposed image segmentation accuracy evaluation methods, which are area-based metrics, location-based metrics and combinations metrics. 3D point cloud data, which was gathered by Reigl VZ1000, was used to make two-dimensional transformation of point cloud data. The object-oriented segmentation result of aquaculture farm, building and farmland polygons were used as test object and adopted to evaluate segmentation accuracy.