Land type authenticity check of vector patches using a self-trained deep learning model

Quality checks are important to ensure the accuracy and reliability of spatial data. However, current methods primarily focus on attributes and geometric information, and methods to verify the accuracy of land-type classification for vector patches are lacking. Therefore, in this paper, we proposed a framework for the automatic verification of the land type of vector patches, including the segmentation of complex vector patches, automatic acquisition of training samples, and automatic extraction of suspicious patches based on deep learning scene interpretation results. First, an innovative method for segmentation based on an oriented bounding box was proposed. Then, scene interpretation from the segmented processing units was performed. Finally, the scene interpretation results and the original data for vector patches were combined, and segmentation units that do not match the category information were automatically identified. The suspicious patches were extracted, and the land-type authenticity of vector patches was automatically checked. Experimental results showed that, in the study area, the accuracy, precision, recall, and F1 values for the model based on first-level land categories were 0.989, 0.842, 0.906, and 0.873, respectively, and those for second-level land categories were 0.994, 0.770, 0.891, and 0.826, respectively. Accordingly, the newly developed method provides reliable technical support for checking the land-type authenticity of vector patches.