Digital elevation models (DEMs) are crucial geographical data source whereas the resolution of commonly used DEM products is low and cannot meet requirement of some detailed geo-related applications. Deep learning-based methods have been demonstrated to be effective in super-resolution (SR) techniques, which reconstruct high-resolution (HR) images from low-resolution (LR) images. However, existing deep learning methods have not fully considered the multi-scale spatial heterogeneity and topographic knowledge of DEM data that differentiate them from traditional images. These inevitably lead to the localized smoothing of the reconstructed DEM and influence the reliability downstream geographical analysis. This study proposes a transformer-based topography neural network (TTSR) for DEM SR incorporating a local-global deformable block (LGDB) for capturing the multi-scale spatial heterogeneity and topographic knowledge, a spatio-channel coupled channel attention (SimAM) mechanism for reallocating channel weights and providing a supplement of the global spatial features, and an improved terrain loss function (iLoss) for mitigating noise across datasets. TTSR was validated using two publicly available real-world DEM datasets for recovering DEM from 30 m to 10 m. The root mean square error (RMSE) of the proposed method was reduced by approximately 6-30%, 4-16%, and 1-9% in elevation accuracy, slope accuracy, and aspect accuracy, respectively, compared to the best one of those state-of-the-art methods. This research provides new insights for improving the accuracy of the DEM SR, which will help generate global high-resolution terrain products to geographical studies in the future.

Discovering frequent trends in time series is a critical task in data mining. Recently, order-preserving matching was proposed to find all occurrences of a pattern in a time series, where the pattern is a relative order (regarded as a trend) and an occurrence is a sub-time series whose relative order coincides with the pattern. Inspired by the order-preserving matching, the existing order-preserving pattern (OPP) mining algorithm employs order-preserving matching to calculate the support, which leads to low efficiency. To address this deficiency, this paper proposes an algorithm called efficient frequent OPP miner (EFO-Miner) to find all frequent OPPs. EFO-Miner is composed of four parts: a pattern fusion strategy to generate candidate patterns, a matching process for the results of sub-patterns to calculate the support of super-patterns, a screening strategy to dynamically reduce the size of prefix and suffix arrays, and a pruning strategy to further dynamically prune candidate patterns. Moreover, this paper explores the order-preserving rule (OPR) mining and proposes an algorithm called OPR-Miner to discover strong rules from all frequent OPPs using EFO-Miner. Experimental results verify that OPR-Miner gives better performance than other competitive algorithms. More importantly, clustering and classification experiments further validate that OPR-Miner achieves good performance.

Under the heavy management on the increasing 3D models, the topic of image-based 3D model retrieval which organizes unlabeled 3D models based on abundant knowledge learned from labeled 2D images has drawn attention. However, prior methods are limited in aligning semantically at corresponding categories of two domains due to the lack of label information in the 3D domain. To this end, this paper proposes an improved semantic representation learning by multiple clustering approach, which improves the reliability of pseudo labels for 3D models, so as to achieve class-level semantic alignment. Specifically, this paper first extracts features for 2D images and 3D models. Then it clusters combining the 3D features with the semantic information from multiple clustering on 3D model features to obtain more reliable target pseudo labels. Extensive experiments have shown that the proposed method has achieved the gain of 3.0%-205.0% averagely for popular retrieval metrics on the benchmark of monocular image-based 3D object retrieval (MI3DOR), and 1.3%-69.7% on another advanced benchmark, MI3DOR-2.