Understanding individual building functions is crucial for analyzing broader urban dynamics. Existing studies have incorporated multi-source data to predict building functions but often neglect the spatial and semantic relations between buildings and their surrounding geographic entities. This study proposes a knowledge graph-based approach for geographic context-aware building function prediction to address this gap. Based on multi-source data, we first constructed a building knowledge graph (BuildingKG), where nodes represent buildings and other related entities, and edges denote their spatial and semantic relations. Subsequently, we applied a knowledge graph embedding technique to the BuildingKG to capture the structural knowledge of entities, which encodes the interactions between them. The structural knowledge, combined with the attributes of individual entities, was concatenated to form the descriptive features of the entity nodes. Finally, we designed a meta-path-based graph transformer neural network model to process the graph, comprising nodes and edges from the BuildingKG, to classify building function types using a supervised learning method. Experiments demonstrated that our approach achieved an overall accuracy of 93.05%, outperforming baseline models with superior computational efficiency. Moreover, interpretability analysis revealed different entity features’ global and local contributions to the prediction results, underscoring the importance of considering geographic context.

Landform type recognition presents significant implications for understanding landform origins, evolutionary mechanisms, and morphological differences. Artificial intelligence (AI) techniques based on sample learning often lead to unsatisfactory outcomes due to the intricate genesis and regional heterogeneity of landforms. This study combines domain knowledge with a deep learning (DL) model to improve landform type recognition. Contour data serves as a valuable resource, offering rich morphological information across horizontal, vertical, local, and macro scales. Our approach incorporated morphological knowledge and proximity relationships derived from contours into a graph convolutional network using the DiffPool technique (GCN-DP). Guided by the First Law of Geography, contours within each landform unit were represented as graphs, incorporating morphological knowledge as node features. The GCN-DP model then employed convolution and pooling to extract hierarchical features from these graphs for landform type recognition. A performance evaluation demonstrated the effectiveness of our method with an F1-score of 87.40%, surpassing RF and GCN methods by 5.24–12.50%, respectively. Ablation experiments confirmed the usefulness of morphological knowledge. This study offers an efficient strategy for landform type recognition, improving the level of intelligent mining using contour data.

Drainage pattern (DP) recognition is critical in hydrographic analysis, topography identification, and drainage characteristic detection. The traditional method is based on rule computation and self-similarity idea preliminarily performing the DP classification. However, DP segmentation is an uncertain spatial cognitive problem affected by enormous factors. To settle such a multi-conditions decision question, this study takes the segmentation of parallel drainage pattern (SPDP) as an example presenting a deep learning method, namely the graph convolution neural network (GCNN) based on Graph SAmple and aggreGatE (GraphSAGE). First, a directed graph and dual graph were used to construct a dual drainage graph recording spatial-cognition features of drainage. Second, nine drainage features were built to define the graph description from three perspectives: topological connectivity, meandering equilibrium, and directional unity. Finally, the GraphSAGE model was designed for SPDP and trained by typical samples to finish the segmentation works. The experiment examined the optimal feature combination and hyperparameter sensitivity, which can provide sufficient information for SPDP supported by GraphSAGE. Besides, our model outperformed other machine learning methods and GCNNs driven by a fixed quantity sampling mechanism and hydrological knowledge. This work provides a vital reference for hydrology research supported by combing hydrological knowledge with GCNNs.

Stream programming model has been productively applied to a number of important application domains. Software pipelining is an important code scheduling technique for stream programs. However, the multicore evolution has presented a new dimension of challenges: that is how to orchestrate the best software pipelining schedule in the face of resource constrained architectures (e.g., number of cores, available memory, and bandwidth)? In this paper, we proposed a new solution methodology to address the problem above. Our main contributions include the following. A unified Integer Linear Programming (ILP) formulation has been proposed that combines the requirement of both rate-optimal software pipelining and the minimization of intercore communication overhead. Next, an extended formulation has been proposed to formulate the schedule under memory size constrained systems. It orchestrates the rate-optimal software pipelining execution for stream programs with strict memory, processor cores, and communication constraints. A solution testbed has been implemented for the proposed problem formulations. This has been realized by extending the Brook programming environment with our software pipelining support-named DFBrook. An experimental study has been conducted to verify the effectiveness of the proposed solutions.

Stream programming model has been productively applied to a number of important applications domains. Software pipelining is an important code scheduling technique for stream programs. However, the multi-/many-core evolution has presented a new dimension of challenges: that is while searching a best software pipelining schedule how to ensure the communications between processing cores are also minimized? In this paper, we proposed a new solution methodology to address the above problem. Our main contributions include the following. A unified formulation has been proposed that combines the requirement of both rate-optimal software pipelining and the minimization of inter-core communication overhead. This formulation has been developed based on a synchronized dataflow graph model, and is expressed as an integer linear programming problem. A solution testbed has been implemented for the proposed problem formulation on the IBM Cell architecture. This has been realized by extending the Brook stream programming environment with our software pipelining support -- named DFBrook. An experimental study has been conducted to verify the effectiveness of the proposed solution. And a comparison of other scheduling methods has demon-strated the performance superiority of our proposed method.