Convolutional neural networks (CNNs) perform well in tasks of segmenting buildings from remote sensing images. However, the intraclass heterogeneity of buildings is high in images, while the interclass homogeneity between buildings and other nonbuilding objects is low. This leads to an inaccurate distinction between buildings and complex backgrounds. To overcome this challenge, we propose an Attentional Feature Learning Network (AFL-Net) that can accurately extract buildings from remote sensing images. We designed an attentional multiscale feature fusion (AMFF) module and a shape feature refinement (SFR) module to improve building recognition accuracy in complex environments. The AMFF module adaptively adjusts the weights of multi-scale features through the attention mechanism, which enhances the global perception and ensures the integrity of building segmentation results. The SFR module captures the shape features of the buildings, which enhances the network capability for identifying the area between building edges and surrounding nonbuilding objects and reduces the over-segmentation of buildings. An ablation study was conducted with both qualitative and quantitative analyses, verifying the effectiveness of the AMFF and SFR modules. The proposed AFL-Net achieved 91.37, 82.10, 73.27, and 79.81% intersection over union (IoU) values on the WHU Building Aerial Imagery, Inria Aerial Image Labeling, Massachusetts Buildings, and Building Instances of Typical Cities in China datasets, respectively. Thus, the AFL-Net offers the prospect of application for successful extraction of buildings from remote sensing images.

In the era of information explosion, Chinese social media has become a repository for massive geographic information; however, its unique unstructured nature and diverse expressions are challenging to toponym entity recognition. To address this problem, we propose a Chinese social media named entity recognition (CSMNER) model to improve the accuracy and robustness of toponym recognition in Chinese social media texts. By combining the BERT (Bidirectional Encoder Representations from Transformers) pre-trained model with an improved IDCNN-BiLSTM-CRF (Iterated Dilated Convolutional Neural Network- Bidirectional Long Short-Term Memory- Conditional Random Field) architecture, this study innovatively incorporates a boundary extension module to effectively extract the local boundary features and contextual semantic features of the toponym, successfully addressing the recognition challenges posed by noise interference and language expression variability. To verify the effectiveness of the model, experiments were carried out on three datasets: WeiboNER, MSRA, and the Chinese social named entity recognition (CSNER) dataset, a self-built named entity recognition dataset. Compared with the existing models, CSMNER achieves significant performance improvement in toponym recognition tasks.

Building simplification is an important research area in automatic map generalization. Up to now, many approaches have been proposed by scholars. However, in the continuous transformation of scales for buildings, keeping the main shape characteristics, area, and orthogonality of buildings are always the key and difficult points. Therefore, this paper proposes a method of progressive simplification for buildings based on structural subdivision. In this paper, iterative simplification is adopted, which transforms the problem of building simplification into the simplification of the minimum details of building outlines. Firstly, a top priority structure (TPS) is determined, which represents the smallest detail in the outline of the building. Then, according to the orthogonality and concave–convex characteristics, the TPS are classified as 62 subdivisions, which cover the local structure of the building polygon. Then, the subdivisions are divided into four simplification types. The building is simplified to eliminate the TPS continuously, retaining the right-angle characteristics and area as much as possible, until the results satisfy the constraints and rules of simplification. A topographic dataset (1:1 K) collected from Kadaster was used for our experiments. In order to evaluate the algorithm, many tests were undertaken, including tests of multi-scale simplification and simplification of typical buildings, which indicate that this method can realize multi-scale presentation of buildings. Compared with the existing simplification methods, the comparison results show that the proposed method can simplify buildings effectively, which has certain advantages in keeping shape characteristics, area, and rectangularity.

The radial drainage network, which has a typical spatial distribution pattern, is a crucial component for reflecting regional geographical landscapes. Identification of radial drainage networks mainly involves the extraction of drainage segments that constitute the entire drainage network, which is conducive to enhancing the existing data and obtaining map knowledge. Previous studies on the spatial distribution of drainage networks are primarily focused on pattern identification and generalization for single-connection drainage networks. However, radial drainage networks, which normally contain multiple local drainage networks, have typical regional and combinatorial features. Thus, identifying them using available methods is challenging. In this study, a method for identifying radial drainage networks is proposed by using dual constraints on topography and geometry. First, mountain peaks that satisfy the distribution criteria are identified from the window and contour features. Then, various parameters are designed to distinguish the features of drainage segments and basins, and the components of radial drainage networks are determined. Finally, through topological operation and the distance metric, the boundary segments of the radial drainage networks are divided into different groups, and the corresponding radial drainage networks are identified. The test results demonstrate that compared with manual identification, the proposed method has achieved over 90% identification accuracy, recall rate, and F1 value.