The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conducted to learn the feature representations of the buildings and recognize their shapes based on graph neural networks. Due to the principles of graph neural networks, it is necessary to construct a graph to represent the adjacency relationships between the points (i.e., the vertices of the polygons shaping the buildings), and extract a list of geometric features for each point. This paper proposes a deep point convolutional network to recognize building shapes, which executes the convolution directly on the points of the buildings without constructing the graphs and extracting the geometric features of the points. A new convolution operator named TriangleConv was designed to learn the feature representations of each point by aggregating the features of the point and the local triangle constructed by the point and its two adjacency points. The proposed method was evaluated and compared with related methods based on a dataset consisting of 5010 vector buildings. In terms of accuracy, macro-precision, macro-recall, and macro-F1, the results show that the proposed method has comparable performance with typical graph neural networks of GCN, GAT, and GraphSAGE, and point cloud neural networks of PointNet, PointNet++, and DGCNN in the task of recognizing and classifying building shapes in map space.

Buildings are important entity objects of cities, and the classification of building shapes plays an indispensable role in the cognition and planning of the urban structure. In recent years, some deep learning methods have been proposed for recognizing the shapes of building footprints in modern electronic maps. Furthermore, their performance depends on enough labeled samples for each class of building footprints. However, it is impractical to label enough samples for each type of building footprint shapes. Therefore, the deep learning methods using few labeled samples are more preferable to recognize and classify the building footprint shapes. In this paper, we propose a relation network based method for the recognization of building footprint shapes with few labeled samples. Relation network, composed of embedding module and relation module, is a metric based few-shot method which aims to learn a generalized metric function and predict the types of the new samples according to their relation with the prototypes of these few labeled samples. To better extract the shape features of the building footprints in the form of vector polygons, we have taken the TriangleConv embedding module to act as the embedding module of the relation network. We validate the effectiveness of our method based on a building footprint dataset with 10 typical shapes and compare it with three classical few-shot learning methods in accuracy. The results show that our method performs better for the classification of building footprint shapes with few labeled samples. For example, the accuracy reached 89.40% for the 2-way 5-shot classification task where there are only two classes of samples in the task and five labeled samples for each class.

Vegetation cover in the Loess Plateau region is an important component of ecological protection in the Yellow River Basin, and this study provides a scientific reference for further vegetation restoration. Based on Landsat images and related data, we utilized the dimidiate pixel model and Geodetector method to study the vegetation cover in the Wuding River Basin from 2000 to 2022. The results indicated the spatial and temporal distribution of the vegetation cover and its changes over the study period. Additionally, the driving factors influencing its spatial changes were also uncovered. We also propose a land use shift vegetation cover contribution formula to quantify the effect of land type change on the FVC. The study showed that (1) the overall vegetation cover of the watershed increased significantly, and the FVC showed an increasing trend from 2000 to 2013 and a slow decline from 2013 to 2022, with the gradual transformation of low-graded FVC into a higher graded one. (2) The FVC increased spatially from northwest to southeast, and the trend of future changes is mainly decreasing. (3) The strongest explanatory power for the FVC change is the land use type and its interactive combination with rainfall. (4) The conversion of grassland to cropland contributes the most to the vegetation cover at 1.52%, and the increase in the cropland area is more conducive to the increase in the vegetation cover.

Simplifying building contours involves reducing data volume while preserving the continuity, accuracy, and essential characteristics of building shapes. This presents significant challenges for sequence representation and generation. Traditional methods often rely on complex rule design, feature engineering, and iterative optimization. To overcome these limitations, this study proposes a Transformer-based Polygon Simplification Model (TPSM) for the end-to-end vector simplification of building contours. TPSM processes ordered vertex coordinate sequences of building contours, leveraging the inherent sequence modeling capabilities of the Transformer architecture to directly generate simplified coordinate sequences. To enhance spatial understanding, positional encoding is embedded within the multihead self-attention mechanism, allowing the TPSM to effectively capture relative vertex positions. Additionally, a self-supervised reconstruction mechanism is introduced, where random perturbations are applied to input sequences, and the model learns to reconstruct the original contours. This mechanism enables TPSM to better understand underlying geometric relationships and implicit simplification rules. Experiments were conducted using a 1:10,000 building dataset from Shenzhen, China, targeting a simplification scale of 1:25,000. The results demonstrate that TPSM outperforms five established simplification algorithms in controlling changes to building area, orientation, and shape fidelity, achieving an average intersection over union (IoU) of 0.901 and a complexity-aware IoU (C-IoU) of 0.735.

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tailored to specific types of line and polygon elements, limiting their general applicability. This study introduces a novel approach called “Pre-Trained Shape Feature Representations from Transformers (PSRT)”, which utilizes transformer encoders designed with three self-supervised pre-training tasks: coordinate masking prediction, coordinate offset correction, and coordinate sequence rearrangement. This approach enables the extraction of general shape features applicable to both line and polygon elements, generating high-dimensional embedded feature vectors. These vectors facilitate downstream tasks like shape classification, pattern recognition, and cartographic generalization. Our experimental results show that PSRT can extract vector shape features effectively without needing labeled samples and is adaptable to various types of vector features. Compared to the methods without pre-training, PSRT enhances training efficiency by over five times and improves accuracy by 5–10% in tasks such as line element matching and polygon shape classification. This innovative approach offers a more unified, efficient solution for processing vector shape data across different applications.