Showing papers by "Tinghua Ai published in 2021"
TL;DR: This study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation, and shows that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes.
Abstract: 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 a...
35 citations
TL;DR: Research has developed numerous algorithms to simplify building data, but no single algorithm can appropriately serve as a guide for solving shape characteristics problems.
Abstract: 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 simp...
10 citations
TL;DR: The experimental results with a real-world dataset show that the proposed method can inherit the merits of both structural analysis and statistical filter in preserving terrain features for multi-scale DEM representations.
Abstract: As a key focus of cartography and terrain analysis, the simplification of a digital elevation model (DEM) is used to preserve the pattern features of the terrain surface while suppressing its detai...
7 citations
TL;DR: This study uses a parallel annotation to conduct point label placement on hexagonal grids, which is efficient to figure out high-quality label placement for points of interest (POI) and maintains good readability due to the parallel annotation.
Abstract: Point feature label placement (PFLP) has been a fundamental problem in automatic cartography over decades. In this research, labels are approximated by individual characters to offer more freedom o...
3 citations
TL;DR: The sparse reconstruction technique from the signal processing domain is introduced, which aims to remove trivial relationships in a dataset by assuring three principles in spatial data, including retention of the correlation of data in the non-spatial attribute domain, preservation of local dependencies in the spatial domain, and removal of trivial relationships.
Abstract: Previous research has tended to use a global threshold of proximity to determine neighbors, neglecting spatial heterogeneity. Flexible thresholds implemented by adaptive search radii methods accoun...
2 citations
TL;DR: In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution character as mentioned in this paper, where a selection operator is used to improve the legibility of the map.
Abstract: In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution character...
1 citations
TL;DR: This study takes hierarchical data as the research object and expresses both the data structure and relationship from a spatial perspective by means of a dimensionality reduction algorithm and a multi-scale terrain model built with the help of level of detail technology.
Abstract: Metaphor are commonly used rhetorical devices in linguistics. Among the various types, spatial metaphors are relatively common because of their intuitive and sensible nature. There are also many studies that use spatial metaphors to express non-location data in the field of visualization. For instance, some virtual terrains can be built based on computer technologies and visualization methods. In virtual terrains, the original abstract data can obtain specific positions, shapes, colors, etc. and people’s visual and image thinking can play a role. In addition, the theories and methods used in the space field could be applied to help people observe and analyze abstract data. However, current research has limited the use of these space theories and methods. For instance, many existing map theories and methods are not well combined. In addition, it is difficult to fully display data in virtual terrains, such as showing the structure and relationship at the same time. Facing the above problems, this study takes hierarchical data as the research object and expresses both the data structure and relationship from a spatial perspective. First, the conversion from high-dimensional non-location data to two-dimensional discrete points is achieved by a dimensionality reduction algorithm to reflect the data relationship. Based on this, kernel density estimation interpolation and fractal noise algorithms are used to construct terrain features in the virtual terrains. Under the control of the kernel density search radius and noise proportion, a multi-scale terrain model is built with the help of level of detail (LOD) technology to express the hierarchical structure and support the multi-scale analysis of data. Finally, experiments with actual data are carried out to verify the proposed method.