Road network selection is critical in map generalization. While machine learning-based models improve selection performance, their increased complexity reduces explainability, which complicates the explicit description of the relationship between input road features and selection decisions. To address this issue, we propose an explainable artificial intelligence (XAI) approach for road network selection that treats road strokes as processing units and extracts the descriptive features for each stroke. A neural additive model (NAM) that consists of several independent feature networks was used to analyze descriptive features to determine whether each stroke should be retained. The explainability of the XAI approach was driven by the learning of linearly combined feature networks in the NAM, which clearly differentiates the contribution of each stroke’s features to the final selection results. To balance explainability and performance, knowledge distillation was employed to transfer knowledge from a teacher model to the NAM student model. Experiments on datasets showed that the XAI approach achieved over 87% consistency with manual selection. Notably, it provided a mechanism for both global and local explainability analyses, thereby improving our understanding of why certain strokes are retained or deleted. These insights into the model’s decision-making process help advance the automation of map generalization.

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.

Plenty of effort has been devoted to solving the nonlinear radiation distortions (NRDs) in planetary image matching. The mainstream solutions convert multimodal images into “single” modal images, which requires building the intermediate modalities of images. Phase congruency (PC) features have been widely used to construct intermediate modalities due to their excellent structure extraction capabilities and have proven their effectiveness on Earth remote sensing images. However, when dealing with large-scale planetary remote sensing images (PRSIs), traditional PC features constructed based on the log-Gabor filter take considerable time, counterproductive to global topographic mapping. To address the efficiency issue, this work proposes a fast planetary image-matching method based on efficient PC-based feature transform (EPCFT). Specifically, we introduce a method to calculate PC using Gaussian first- and second-order derivatives, called efficient PC (EPC). Different from the log-Gabor filter, which is sensitive to structures in a single direction,$\rm EPC$uses circularly symmetric filters to equally process changes in all directions. The experiments with 100 image pairs show that compared with other methods, the efficiency of our method is nearly doubled without loss of accuracy.

Accurate lunar rock detection is vital for lunar exploration. However, the existing methods are sensitive to factors, such as the uneven lighting and terrain relief. To address these issues, a novel method combining multiscale phase feature type maps (PFTMs) and phase congruency moment maps (PCMMs) is proposed. First, rock seeds are detected through phase congruency and gradient analysis. Second, a strategy called the local scale saliency score (LSSS) is proposed to adaptively estimate the optimal scale layer for candidate rock detection. Within this layer, the specifically designed local-contextual and global-contextual (LGC) features are employed to identify the regions of interests (ROIs) for rocks. Subsequently, the process of filtering false positives (FPs) involves the utilization of geometric metrics and scale feature analysis. Finally, a specially designed edge detector named the bilateral local maximum PCMM-PFTM path is proposed to describe the edges of the rocks. Tests on Chang’E-3 and Chang’E-5 Landing Camera (LCAM) images show the proposed method’s robustness in detecting lunar rocks of varying sizes and reflectance, achieving F1-scores ranging from 0.919 to 0.947.

The investigation of small lunar craters holds scientific and engineering significance. This paper presents a novel method for detecting small lunar craters. It consists of three stages: seed detection, candidate crater detection, and crater evaluation. Firstly, crater seeds are identified through morphological operations as pixels with the highest local gradient and specific gradient direction. Secondly, the optimal scale for each seed is estimated based on the maximum response of the established phase congruency maximum moment (PCMM) scale space. For detecting very small craters, a crater detector called statistical morphological constraint path-sets (SMPS), which leverages image spatial domain features, is proposed. It configures the image as a weighted directed graph, using path-sets centered on seeds to flexibly detect highlights and shadow regions of craters. For detecting craters with larger optimal scale, another crater detector named structural consistency constrained multi-paths (SCMP) is proposed, utilizing the frequency phase features. The core idea of SCMP is to configure the phase feature type (PFT) map with the optimal scale as a directed graph. Centered on the seed, the multi-path operator is designed to detect craters. Unsupervised discriminative correlation filters (UDCFs) are trained with HOG features from images or PFT maps to validate candidate craters. The results indicate that for images with a resolution of 0.5-2m/pixel, the proposed method demonstrates good detection performance for small craters with diameters of less than 5 m, 5-10 m, and greater than 10 m, with an average detection rate of 0.89, 0.91, and 0.92, respectively.

Detecting interchanges in road networks benefit many applications, such as vehicle navigation and map generalization. Traditional approaches use manually defined rules based on geometric, topological, or both properties, and thus can present challenges for structurally complex interchange. To overcome this drawback, we propose a graph-based deep learning approach for interchange detection. First, we model the road network as a graph in which the nodes represent road segments, and the edges represent their connections. The proposed approach computes the shape measures and contextual properties of individual road segments for features characterizing the associated nodes in the graph. Next, a semi-supervised approach uses these features and limited labeled interchanges to train a graph convolutional network that classifies these road segments into an interchange and non-interchange segments. Finally, an adaptive clustering approach groups the detected interchange segments into interchanges. Our experiment with the road networks of Beijing and Wuhan achieved a classification accuracy >95% at a label rate of 10%. Moreover, the interchange detection precision and recall were 79.6 and 75.7% on the Beijing dataset and 80.6 and 74.8% on the Wuhan dataset, respectively, which were 18.3–36.1 and 17.4–19.4% higher than those of the existing approaches based on characteristic node clustering.

Research has developed numerous algorithms to simplify building data. Each algorithm has strengths and weaknesses in addressing shape characteristics, but no single algorithm can appropriately simplify all buildings. This study proposes a hybrid approach that identifies the best simplified representation of a building among four existing algorithms. The proposed approach applies the four algorithms to generate simplification candidates. With a backpropagation neural network, an evaluator is built through supervised learning based on measurements describing the changes in position, size, orientation, and shape between the original building and the candidates of its simplified representations. The evaluator determines the most appropriate candidate. Experiments on buildings from residential and commercial areas in Shenzhen city show that the hybrid approach can combine the advantages of different algorithms. The percentages of unreasonable simplified buildings in the results obtained using the hybrid algorithm are 3.8% in the residential area and 0 in the commercial area, respectively, which are significantly lower than those in the results of standalone applications of the four algorithms. Furthermore, comparison with the simplification algorithm in the popular software, ArcGIS, confirms that our approach shows better results in terms of corner squaring and maintaining the regional characteristics of buildings.

Crater detection from planetary images is a challenging issue due to the complicated variations in geometry shape, illumination, and scale. An automatic crater detection algorithm (CDA) that is robust to these factors is, therefore, necessary. In this article, a novel automatic CDA that is robust to these factors is proposed to detect the multiscale craters of the Moon. The proposed method consists of two main steps: 1) in the hypothesis generation (HG) step, a novel feature operator called the path-profile, which is constructed based on the self-defined adjacency graph and a path descriptor, is presented to derive the highlight-shadow feature of craters for detecting candidate craters. 2) In the hypothesis verification (HV) step, based on the idea of anomaly detection, the isolation forest algorithm which is an unsupervised learning anomaly detection method is applied to eliminate falsely detected craters. Lunar Reconnaissance Orbiter Camera Wide Angle Camera and Narrow Angle Camera images and Chang’E-4 landing camera images were used to test the accuracy and robustness of the proposed method. The experimental results indicate that: on average, the accuracy of the detection result of the HG step is about 90%, and the HV step can further improve this by 3%–4%. The proposed method is a reliable way to detect multiscale lunar craters for various resolutions images with diameters ranging from five pixels to hundreds of pixels, and it is robust to the different terrains and illumination conditions on the Moon.

The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching.