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Introduction
Tinghua Ai currently works at the School of resource and environment sciences, Wuhan University. Their current project is 'On-line map generalization of crowd-souring data'.
Skills and Expertise
Publications
Publications (256)
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 land-forms. This study combines doma...
Road network simplification is a complex decision-making process. Such a multi-factor decision and scaling operation traditionally applied rule-based methods. The establishment and adjustment of these rules involve many human-set parameters and conditions, which makes generalized results closely related to the cartographer’s experience and habits....
The analysis of association rules within ecosystems is crucial for monitoring, managing, and conserving natural resources. As widely adopted approaches for this task, geospatial methods involving spatial co-location pattern mining can reveal distribution rules and inherent associations among diverse geographical elements. Rooted in Tobler’s first l...
Spatial co-location patterns reflect the inherent correlations among geographical elements. Mining co-location patterns of POIs can provide valuable insights for urban planning and resource management. Generally, co-location mining comprises two steps: proximity relationship determination (geospatial analysis) and frequent pattern recursion (logica...
Under the first law of geography, spatial co-location patterns reflect the dependency of different geographic elements’ distribution, satisfying the association discovery of big spatial data analysis. Spatial co-location pattern mining needs to consider the spatial conjunction mechanisms, detect spatial neighborhood relationships and search high-fr...
The intrinsic connections between geographical elements are important for uncovering hidden geo-scientific laws. However, current research on terrain and landform analysis mainly focuses on the landscapes themselves, with insufficient attention to the connections between them. Therefore, this study proposes a knowledge graph approach based on geogr...
Spatial flows represent spatial interactions or movements. Mining colocation patterns of different types of flows may uncover the spatial dependences and associations among flows. Previous studies proposed a flow colocation pattern mining method and established a significance test under the null hypothesis of independence for the results. In fact,...
Coastlines play a crucial role in coastal dynamics, and classifying their shape is an essential requirement for coastal analysis. With the development of Coastal Management Systems (CMS), structured and high-resolution vector-format coastlines have become increasingly available compared to remote sensing image coastlines. However, due to the challe...
Accurately understanding the functions of buildings is crucial for urban monitoring, analysis of urban economic structures, and effectively allocating resources. Previous studies have investigated building function classification using single or dual data sources. However, the complexity of building functions cannot be fully reflected by a limited...
Machine learning is increasingly used as a computing paradigm in cartographic research. In this extended editorial, we provide some background of the papers in the CaGIS special issue Machine Learning in Cartography with a special focus on pattern recognition in maps, cartographic generalization, style transfer, and map labeling. In addition, the p...
Extracting building from remote sensing images is crucial, but the extracted outlines still face issues such as point redundancy and lack of right-angle features, relying on further regularization. This study proposed an integrated regularization method to combine the strengths of multiple algorithms. 22 metrics were calculated to describe the geom...
Deep learning is increasingly being used to improve the intelligence of map generalization. Vector-based map generalization , utilizing deep learning, is an important avenue for research. However, there are three questions: (1) transforming vector data into a deep learning data paradigm; (2) overcoming the limitation of the number of samples; and (...
Computer screens often constrain the level of detail and clarity of displays. High-density data require a predefined strategy to select significant features hierarchically to allow interactive data zooming. Although many methods are available for hierarchically selecting rivers from vector data, some approaches for raster data are better than other...
The consistency of geospatial data is of great significance for the application and updating of geographic information in web maps. Due to the multiple data sources and different temporal versions, the tile web maps usually meet the inconsistency question across different layers. This study tries to develop a method to detect this kind of inconsist...
Similarity measurement has been a prevailing research topic in geographic information science. Geometric similarity measurement in scaling transformation (GSM_ST) is critical to ensure spatial data quality while balancing detailed information with distinctive features. However, GSM_ST is an uncertain problem due to subjective spatial cognition, glo...
For point clusters, the conflict and crowding of map symbols is an inevitable problem during the transition from large to small scales. The cartographic generalization involved in this problem as a spatial decision-making process is usually related to the analysis of spatial context, the choice of abstraction operators, and the judgment of the resu...
Numerous methods based on square rasters have been proposed for polygon generalization. However, these methods ignore the inconsistent distance measurement among neighborhoods of squares, which may result in an imbalanced generalization in different directions. As an alternative raster, a hexagon has consistent connectivity and isotropic neighborho...
Drainage pattern (DP) recognition is critical in hydrographic analysis, topography identification, and drainage characteristic detection. The traditional method is based on rule computation and self-similarity idea preliminarily performing the DP classification. However, DP segmentation is an uncertain spatial cognitive problem affected by enormous...
Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learnin...
The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classifica...
Characterizing activity pattern such as behavior preferences or habits is crucial in many fields. However, existing studies mainly focus on the spatial-temporal dimensions of raw trajectory, but ignore the context information in multi-aspect trajectory that affects behavior significantly. In this paper, we present a data-driven framework to charact...
Detecting interchanges in road networks benefit many applica-
tions,
such
as
vehicle
navigation
and
map
generalization.
Traditional approaches use manually defined rules based on geo-
metric, topological, or both properties, and thus can present chal-
lenges for structurally complex interchange. To overcome this
drawback, we propose a graph-based d...
The COVID-19 pandemic breaking out at the end of 2019 has seriously impacted urban human mobility and poses great challenges for traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we propose a multiscale geospatial network framework for the analysis of bike-sharing...
Drainage pattern recognition (DPR) is a classic and challenging problem in hydrographic system analysis, topographical knowledge mining, and map generalization. An outstanding issue for traditional DPR methods is that the rules used to extract patterns based on certain geometric measures are limited, not accessing the effects of manual recognition....
Multi-scale digital elevation model (DEM) is an important content of digital terrain analysis. To generate multi-scale DEM, this study reports a heuristic DEM generalization method. The key of DEM generalization is to maintain the main topographic features and remove the minor topographic features. Our method takes catchment as generalizing object,...
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 characteristics. During the past few decades, many methods for vector building selection have been developed; however, pixel-based methods are relatively lacking....
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 co...
Remote sensing mapping plays an important role in understanding regional development and geographical environment characteristics. Traditional remote sensing mapping at different levels usually fails to consider the shape, quantity, distribution, and position features of map objects. Therefore, a multilevel representation of urban buildings is real...
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 account for either the spatial structures or the non-spatial similarities of objects, but few consider both. By combining the spatial and non-spatial informati...
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...
Identifying the spatial configurations of buildings and grouping them reasonably is an important task in cartography. This study developed a grouping approach using graph deep learning by integrating multiple cognitive features and manual cartographic experiences. Taking building center points as nodes, adjacent buildings were organized as a graph...
Owing to map scale reduction and other cartographic generalization operations, spatial conflicts may occur between buildings and other features in automatic cartographic generalization. Displacement is an effective map generalization operation to resolve these spatial conflicts to guarantee map clarity and legibility. In this paper, a novel buildin...
In traditional change detection methods, remote sensing images are the primary raster data for change detection, and the changes produced from cartography generalization in multi-scale maps are not considered. The aim of this research was to use a new kind of raster data, named map tile data, to detect the change information of a multi-scale water...
A tile map in image format is one of the most important tools that people can use to acquire multiscale geographic information on the Internet. Traditional methods of typification in map generalization are used to handle traditional vector-based buildings and linear drainages such as rivers and ditches. In this paper, a new raster-tile-based method...
Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic da...
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 details over multiple scales. Statistical filtering and structural analysis methods are commonly used for this process. The structural analysis method perform...
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 are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a le...
In the era of big data, a large volume of location-related semantic data needs to be visualized. The geographic information service needs to consider multi-level requests that can be adaptable to different users. Location-related semantic data can be represented in a thematic map. However, the traditional thematic map visualization can only visuali...
This paper proposes a model to quantify the multiscale representation of a polyline based on iterative head/tail breaks. A polyline is first transformed into a corresponding Fourier descriptor consisting of normalized Fourier-series-expansion coefficients. Then, the most significant finite components of the Fourier descriptor are selected and ranke...
With the rapid development of the internet and information technology, visualization techniques for mobile and interactive web maps have developed different requirements. Small screens make it difficult to simultaneously present information details and the surrounding context. Aiming at this problem, this paper proposes a novel variable-scale metho...
As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of m...
Colocation mining is useful for understanding the interactions or dependencies that occur among geographic phenomena. Most colocation mining methods are based on planar space. However, in urban spaces, many human-related activities are constrained by a road network. Planar colocation mining methods are not suitable for studying the concerning geogr...
During map generalization, the collapse of geometry, which is also called geometric dimension reduction, is a basic generalization operation. When the map scale decreases, rivers with long, shallow polygonal shapes, usually require their dual-line representation to be collapsed to a single line. This study presents a new algorithm called superpixel...
The automatic extraction of valley lines (VLs) from digital elevation models (DEMs) has had a long history in the GIS and hydrology fields. The quality of the extracted results relies on the geometrical shape, spatial tessellation, and placement of the grids in the DEM structure. The traditional DEM structure consists of square grids with an eight‐...
This paper presents a new method for describing the complexity of geographic line elements.
Firstly, the Fourier series is used to transform the geographic line elements from the spatial domain to the
frequency domain for analysis. Fourier expansions are performed on different geographic line elements to
obtain different Fourier descriptors, then w...
Semantic information without spatial characteristics can also be visualised in the form of a map. This map type is usually called map-like visualisation; it is not a representation of a real geo-entity but borrows the map metaphor. In current map-like visualisation works, the manipulation of the cartographic process is poor, and there is a consider...
Vector tile mapping is an important issue in web map research. At present, vector tile mapping requires the symbolization of geographic information, as supported by cartographic software, and the development of a corresponding symbolic database when web map services are provided for users. The development of PDF (portable document format) mapping m...
As one of the major concerns in cartographic generalization, road network generalization aims at maintaining the patterns of road networks across map scales. Previous methods define the pattern of road networks mainly from the perspectives of geometry and topology. However, for navigation purposes, traffic flow information is also important to gene...
Neighborhood relationship plays an important role in spatial analysis, map generalization, co-location data mining and other applications. From the perspective of computation, the formal model of neighborhood representation is a challenging question. This study presents a formal spatial data model for representing the planar spatial field with the...
The past few decades have seen the development of automatically feature labelling when manual label placement was thought to be time and labour consuming. Emerging techniques like volunteered geographic information (VGI) collection are making label placement more complexed with many features in a limited space, especially for points of interest (PO...
As an important representation of LBS (Location Based Service), the indoor map provides users with facility location, indoor navigation, wayfinding and other necessary services. Compared with the common map, the indoor map has characteristics of detailed representation, 3D visualization, and real-time reaction. As far as POI (Point of Interest) rep...
Compared with regular quadrilateral grid, regular hexagonal grid is isotropy and has higher cell compactness and sampling density. This gives regular hexagonal grid advantages in visual display, spatial analysis, and many other aspects. However, the studies of raster data mainly focus on regular quadrilateral grid, and various encoding methods are...
This article mainly introduces a class of encoding and compression methods for hexagonal raster data. A new encoding mode is established with the introduction of the Gosper curve, which has good spatial aggregation. On this basis, straightforward encoding, lossless coding compression, and lossy coding compression can be carried out. First, the bidi...
Building simplification is an important research area in automated map generalization. In the last several decades, various methods for building simplification have been proposed by scholars, most of which have concentrated on vector data. However, with the continuous development of computer vision and artificial intelligence technology, some advan...
Machine learning methods, specifically, convolutional neural networks (CNNs), have emerged as an integral part of scientific research in many disciplines. However, these powerful methods often fail to perform pattern analysis and knowledge mining with spatial vector data because in most cases, such data are not underlying grid-like or array structu...
Methods based on computational geometry and statistics were proposed to detect the level of detail (LOD) of features and to evaluate the LOD consistency of the features on digital vector maps. Both natural curves and building polygons were used as examples to demonstrate the proposed method. For natural curves, Delaunay triangulation were adopted t...
As the map scale decreases, conflicts can appear among polygonal features such as water areas and buildings.
Aggregation is usually employed to clearly represent polygonal features on small-scale maps. Over the past
several decades, a number of polygon aggregation algorithms based on vector data have been proposed by
various scholars. In contrast,...
By introducing the Gosper curve into hexagonal grid, a new form of run length coding is established. Based on this, the lossless compression coding and loss compression coding of raster data are carried out. First, the bidirectional correspondence between Gosper curve and hexagonal raster data is established to provide guidance and support for data...
Extracting centrelines from dual-line roads is very important in urban spatial analysis and infrastructure planning. In recent decades, numerous algorithms for road centreline extraction based on vector data have been proposed by various scholars. However, with the continual development of computer vision technology, advances in the corresponding t...
Colocation mining is one of the major spatial data mining tasks. When discovering colocation patterns, spatial statistics or data mining approaches are commonly used. Colocation mining results are typically presented in a textual form and do not provide any spatial information; thus, the results lack an intuitive approach to obtain cognition of col...
Traditional methods treat track points (lines) equally to extract road data, which ignores the spatial distribution disparity and restricts its application. Therefore, this paper proposes a new approach for map construction based on trajectory segmentation and layer fusion from vehicle tracks. First, track line subset is selected through the segmen...
The spatial accessibility to urban health services is a key issue for urban environment and public health studies especially among developing countries with explosive population growth and limited urban land space. Chinese cities have experienced rapid growth and obtained remarkable economic achievements in the last three decades, while this also b...
The data quality problems of OpenStreetMap(OSM) data, which is a typical kind of volunteered geographic information data, restrict the extraction of arterial road. Firstly, this paper presents a method to identify the morphological feature of arterial road through calculating road line density change rate and length of edges using Delaunay triangul...
Crowd-sourced geographic information is becoming increasingly available, providing diverse and timely sources for updating existing spatial databases to facilitate urban studies, geoinformatics, and real estate practices. However, the discrepancies between heterogeneous datasets present challenges for automated change detection. In this paper, we i...
Machine learning methods such as convolutional neural networks (CNNs) are becoming an integral part of scientific research in many disciplines, spatial vector data often fail to be analyzed using these powerful learning methods because of its irregularities. With the aid of graph Fourier transform and convolution theorem, it is possible to convert...
The automatic extraction of valleys or ridges from DEM is a long term topic in the GIS and hydrology fields and a number of algorithms have been developed. The quality of drainage networks extraction depends on many impacts such as data source, DEM resolution and extraction algorithms. However, little consideration has been paid to the influence of...
Indoor route planning, affected by many constraints, needs to take consideration of both the spatial geometry, environmental attribute information of the scene and the application preferences. Thus, it requires a data model that can integrate multiple constraints to model the indoor scene; meanwhile, it’s also necessary to take into account the iso...
In the area of cartography and geographic information science, the center points of area features are related to many fields. The centroid is a conventional choice of center point of area feature. However, it is not suitable for features with a complex shape for the center point may be outside the area or not fit the visual center so well. This pap...
Knowledge or rule-based approaches are needed for quality assessment and assurance in professional or crowdsourced geographic data. Nevertheless, many types of geographic knowledge are statistical in nature and are therefore difficult to derive rules that are meaningful for this purpose. The rules of continuity and symmetry considered in this paper...
Distributions of nr − Jrr for typical cities.
nr − Jrr = 1 indicates the strongest form of spatial order.
(TIF)
Distributions of the number of segments in natural streets for typical cities.
(TIF)
Typical situations where parallel road detection algorithm gives acceptable and unsatisfactory results.
Recognizing divided highways in cases like (g) is highly challenging even for a human subject.
(TIF)