Urban land use classification plays a significant role in urban studies and provides key guidance for urban development. However, existing methods predominantly rely on either raster structure deep features through convolutional neural networks (CNNs) or topological structure deep features through graph neural networks (GNNs), making it challenging to comprehensively capture the rich semantic information in remote sensing images. To address this limitation, we propose a novel urban land use classification model by integrating both raster and topological structure deep features to enhance the accuracy and robustness of the classification model. First, we divide the urban area into block units based on road network data and further subdivide these units using the fractal network evolution algorithm (FNEA). Next, the K-nearest neighbors (KNN) graph construction method with adaptive fusion coefficients is employed to generate both global and local graphs of the blocks and sub-units. The spectral features and subgraph features are then constructed, and a graph convolutional network (GCN) is utilized to extract the node relational features from both the global and local graphs, forming the topological structure deep features while aggregating local features into global ones. Subsequently, VGG-16 (Visual Geometry Group 16) is used to extract the image convolutional features of the block units, obtaining the raster structure deep features. Finally, the transformer is used to fuse both topological and raster structure deep features, and land use classification is completed using the softmax function. Experiments were conducted using high-resolution Google images and Open Street Map (OSM) data, with study areas on the third ring road of Shenyang and the fourth ring road of Chengdu. The results demonstrate that the proposed method improves the overall accuracy and Kappa coefficient by 9.32% and 0.17, respectively, compared to single deep learning models. Incorporating subgraph structure features further enhances the overall accuracy and Kappa by 1.13% and 0.1. The adaptive KNN graph construction method achieves accuracy comparable to that of the empirical threshold method. This study enables accurate large-scale urban land use classification with reduced manual intervention, improving urban planning efficiency. The experimental results verify the effectiveness of the proposed method, particularly in terms of classification accuracy and feature representation completeness.

Urban functional zone (UFZ) classification can provide important data support for urban management. Existing studies often use multimodal data to improve UFZ classification, but the spatial context information is not sufficiently mined. An end-to-end multilevel graph neural network (MGNN) is proposed for the problem. We optimize UFZ classification using POI (Point of Interest) data and remote sensing images by fully exploiting the spatial context features within and between zones. We utilize Delaunay triangulation to construct remote sensing image graphs (IGs) and POI graphs (PGs) within zones at the local level, and use graph neural network (GNN) to extract graph embeddings. The embedded of IGs and PGs are interacted and fused by image-POI contrastive learning-based auxiliary (IPCA)and multitask weight-adaptive determination strategy (MWAS). The block graph (BG) between zones at the global level is constructed by K-nearest neighbor, and the spatial context features of the zones themselves and their neighbors are aggregated by GraphSAGE (Graph Sample and Aggregate), and the classification probability is predicted by SoftMax. Taking Shenyang and Chengdu as the study area, the overall accuracy and Kappa of MGNN are at least 5.27% and 8.06% higher than other methods. Our method provides an effective tool for UFZ classification.

Change detection via remote sensing data is a popular method for monitoring land cover/land use. Graph attention network (GAT) is a method that can improve the change detection performance of land cover/land use monitoring by enhancing the feature representation of remote sensing images. However, the shortcomings of connection sparsity and insufficient sample feature mining of the GAT affect the application of these methods in change detection. This paper proposes a Siamese autoencoder graph attention networks for object-oriented land cover/land use change detection via high-resolution remote sensing, which is useful for semi-supervised problem methods with poor simplicity. First, we reduce the pressure of graph network model operations and obtain multidimensional features via the growing multilevel segmentation strategy (GMSS). The adjacency matrix is established by adding strongly connected edges via the weighted difference similarity matching method (WDSM) with image objects as nodes. Second, we use the Siamese autoencoder to pretrain the node features and migrate the weight parameters to the GAT feature extraction layer. Finally, a small number of samples are selected to train the GAT and predict all nodes. The experimental results show that the average overall accuracy of the proposed method is 95.41%, and the average F1 score is 89.35%, which are at least 5.04% and 10.84% better than those of other typical methods, such as the graph convolutional network (GCN). In particular, detecting roads and bare soil is significantly better than that of other methods.