Vectorized building extraction from high-resolution remote sensing images using spatial cognitive graph convolution model

Traditional approach from source image to application vectors in building extraction needs additional complex regularization of converted intermediate raster results. While in conversion, the lost detailed artifacts, unnecessary nodes, and messy paths would be labor-intensive to repair errors and topological issues, even aside the inherent problems of blob-like objects and blurry, jagged edges in first-stage extraction. This research explores new graph convolution-driven solution, the spatial-cognitive shaping model (SCShaping), to directly access vectorization form of individual buildings through spatial cognitive approximation to coordinates that form building boundaries. To strengthen graph nodes expressivity, this method enriches topological feature embedding travelling along the model architecture along with features contributed from convolutional neural network (CNN) extractor. To stimulate the neighboring aggregation in graphs, Graph-Encoder-Decoder mechanism is introduced to augment feature reuse integrating complementary graph convolution layers. The strong embedding guarantees effective feature tapping and the robust structure guarantees the feature mining. Comparative studies have been conducted between the proposed approach with five other methods on three challenging datasets. The results demonstrate the proposed approach yields unanimous and significant improvements in mask-wise metrics, which evaluate object integrity and accuracy, as well as edge-wise metrics, which assess contour regularity and precision. The outperformance also indicates better multi-scale object adaptability of SCShaping. The obtain-and-play SCShaping commands a pleasurable implementation way to advance ideal man–machine collaboration.