Drive-by sensing, using vehicles as mobile sensors to collect environmental data, offers high spatiotemporal resolution monitoring. In drive-by sensing tasks, urban areas with intense human activity or pollution – termed high-priority hotspots – demand frequent sensing for adequate data collection. However, prior studies rarely address optimizing vehicle selection to enhance hotspot coverage frequency while maintaining non-hotspot coverage. This study formalized this problem as the Maximal Hotspot Regular Coverage Problem and proposed an Adaptive Level-Aware Vehicle Selection algorithm. Using air pollution hotspot sensing in Beijing as an empirical case, results show the advantage of the proposed algorithm over other baselines: Random selection (RS), Non-hotspot Greedy Adding and Multi-type hotspot Greedy MaxMin, covering 29.99% of hotspots and 77.08% non-hotspot zones with 1000 sensors on average. The spatial distribution of covered area and statistical distribution of average visits per hour reveals a large spatial range and high revisit times of the method, and its advantage over baselines in a hybrid sensing scenario is also proven (with 54.18% daily average coverage among all zones and 31.22% on hotspots). This study provides a method to advance the coverage of hotspots in single-type and hybrid sensing scenarios, inspiring a future extension of optimization based on the proposed algorithm.

About 30 years have passed since Michael F. Goodchild proposed the term geographical information science (GIScience) in 1992. In the past 30 years, GIScience has made great progress in expanding research findings and perfecting theories and methods. To understand the development progress of GIScience, this research conducts a bibliometric analysis of 9400 publications between 1991 and 2020 in 10 international refereed journals and 2 international conferences of GIScience. We analyze the publication statistics and trends in GIScience from two aspects of journals/conferences and countries/territories. Based on the community detection of the citation network, we extract 15 research themes and show their leading authors and highly cited articles. Furthermore, the change of publication number in different themes over time can indicate the evolution of some research focuses in GIScience. The results demonstrate that the publication proportions of some themes grow rapidly, such as “moving object,” “volunteered geographic information,” and “geographically weight regression,” while the publication proportions of some themes are decreasing, such as “digital elevation model,” “planning support system,” and “ontology.” In the discussion, the journal distribution of papers on different themes is discussed. Moreover, we suggest a few research directions that are worthy of attention in the future.

Big geo-data are often aggregated according to spatio-temporal units for analyzing human activities and urban environments. Many applications categorize such data into groups and compare the characteristics across groups. The intergroup differences vary with spatio-temporal units, and the essential is to identify the spatio-temporal units with apparently different data characteristics. However, spatio-temporal dependence, data variety, and the complexity of tasks impede an effective unit assessment. Inspired by the applications to extract critical image components based on explainable artificial intelligence (XAI), we propose a spatio-temporal layer-wise relevance propagation method to assess spatio-temporal units as a general solution. The method organizes input data into an extensible three-dimensional tensor form. We provide two means of labeling the spatio-temporal tensor data for typical geographical applications, using temporally or spatially relevant information. Neural network training proceeds to extract the global and local characteristics of data for corresponding analytical tasks. Then the method propagates classification results backward into units as obtained task-specific importance. A case study with taxi trajectory data in Beijing validates the method. The results prove that the proposed method can evaluate the task-specific importance of spatio-temporal units with dependence. This study also attempts to discover task-related knowledge using XAI.

Spatial interactions underlying consecutive sequential snapshots of spatial distributions, such as the migration flows underlying temporal population snapshots, can reflect the details of spatial evolution processes. In the era of big data, we have access to individual-level data, but the acquisition of high-quality spatial interaction data remains a challenging problem. Most research has been focused on distributions of movable objects or the modelling of spatial interaction patterns, with few attempts to identify hidden spatial interaction patterns from temporal transitions of spatial distributions. In this article, we introduced an approach to infer spatial interaction patterns from sequential snapshots of spatial population distributions by incorporating linear programming and the spatial constraints of human movement. Experiments using synthetic data were conducted using four simple scenarios to explore the characteristics of our method. The proposed method was used to extract interurban flows of migrants during the Chinese Spring Festival in 2016. Our research demonstrated the feasibility of using discrete multi-temporal snapshots of population distributions in space to infer spatial interaction patterns and offered a general analytical framework from snapshot data to spatial interaction patterns.

Abstract We present a novel approach to structure from motion that can deal with missing data and outliers with an affine camera. We model the corruptions as sparse error. Therefore the structure from motion problem is reduced to the problem of recovering a low-rank matrix from corrupted observations. We first decompose the matrix of trajectories of features into low-rank and sparse components by nuclear-norm and ℓ 1-norm minimization, and then obtain the motion and structure from the low-rank components by the classical factorization method. Unlike pervious methods, which have some drawbacks such as depending on the initial value selection and being sensitive to the large magnitude errors, our method uses a convex optimization technique that is guaranteed to recover the low-rank matrix from highly corrupted and incomplete observations. Experimental results demonstrate that the proposed approach is more efficient and robust to large-scale outliers.

Over recent years, massive geospatial information has been produced at a prodigious rate, and is usually geographically distributed across the Internet. Grid computing, as a recent development in the landscape of distributed computing, is deemed as a good solution for distributed geospatial data management and manipulation. Thus, the Grid computing technology can be applied to integrate various distributed resources into a ‘super-computer’ that enables efficient distributed geospatial query processing. In order to realize this vision, an effective mechanism for building the distributed geospatial query workflow in the Grid environment needs to be elaborately designed. The workflow-building technology aims to automatically transform the global geospatial query into an equivalent distributed query process in the Grid. In response to this goal, detailed steps and algorithms for building the distributed geospatial query workflow in the Grid environment are discussed in this article. Moreover, we develop corresponding software tools that enable Grid-based geospatial queries to be run against multiple data resources. Experimental results demonstrate that the proposed methodology is feasible and correct.

We describe a method of morphometric characterisation of landform from digital elevation models (DEMs). The method is implemented first by classifying every location into morphometric classes based on the mathematical shape of a locally fitted quadratic surface and its positional relationship with the analysis window. Single‐scale fuzzy terrain indices of peakness, pitness, passness, ridgeness, and valleyness are then calculated based on the distance of the analysis location from the ideal cases. These can then be combined into multi‐scale terrain indices to summarise terrain information across different operational scales. The algorithm has four characteristics: (1) the ideal cases of different geomorphometric features are simply and clearly defined; (2) the output is spatially continuous to reflect the inherent fuzziness of geomorphometric features; (3) the output is easily combined into a multi‐scale index across a range of operational scales; and (4) the standard general morphometric parameters are quantified as the first and second order derivatives of the quadratic surface. An additional benefit of the quadratic surface is the derivation of the R 2 goodness of fit statistic, which allows an assessment of both the reliability of the results and the complexity of the terrain. An application of the method using a test DEM indicates that the single‐ and multi‐scale terrain indices perform well when characterising the different geomorphometric features.

This paper presents an algorithm based on the method of supervised machine learning and multi-keyframes to achieve markerless augmented reality (AR) application when there is a locally planar object in the scene. The main goal is to solve the problem of AR tracking in outdoor environment by only using vision and natural features. Instead of tracking fiducial markers, we track natural keypoints, during which the point correspondences are established from the classification perspective. The tracking range is able to be extended by employing many reference images. These results in a promising algorithm that successfully tested on a touring guide system, which provides views of virtual original appearance superimposed to ruins of Yuanmingyuan archaeological site of China. Comparisons are also made between ARToolkit and the proposed algorithm in indoor environment. Experimental results demonstrated that our algorithm is characterized by fairly robustness and high time efficiency in both indoor and outdoor application.

An indoor tracking system based on primary and assistant infrared markers is presented in this paper. The system can track the user's head in a large area with high stability and accuracy. And the price of the system is very low. The novel assistant infrared markers with particular spatial characteristic and the primary infrared markers with particular spatio-temporal characteristic are proposed, which can avoid the synchronization between infrared markers and user system. Various numbers of users can be supported by the proposed system and experimental result shows the effectiveness and robustness of the system.

It becomes more and more important for next generation of GIS to develop cognition-accordant data models which is properly guided by the way how human actually thinks about geographic space. Thus, a new method named landmark-based qualitative reference (LBQR) system is introduced to express qualitative positional information in this paper. Qualitative position can be determined by one or several landmarks which are calculated adaptively according to the target object under LBQR framework. In LBQR framework, qualitative coordinates (QC) are used to represent the position of the target object. QC is defined based on cardinal direction relations between target object and one or several landmarks adjacent to it. At first, Voronoi model is used to determine the reference objects for each target object. Then cardinal direction relations are modeled by minimal bounding rectangle method or cone-based method according to the geometry type of each reference object respectively. Last, an experiment is carried out to evaluate whether the Voronoi model conforms to human spatial cognition.

Digital elevation model (DEM) data are widely used in many research areas such as hydrological analysis and soil erosion models. To generate an accurate DEM is very important to the validity of such researches. Generating DEM from contour lines is now a main method to get grid DEM. In the implementation of GIS software, usually the elevation of a cell with only one contour line (SVC) is simply assigned with the contour line elevation when discretizing vector contour lines. But most of the time, the elevation of the contour line can't characterize the elevation of the SVC accurately. In this paper, we choose different resolutions (cell size) to investigate the frequency of SVCs during DEM generating and the errors created by simply assigning their elevations with the contour line elevations. A new method is given, in which we analyze the spatial relationship between the SVC and the corresponding contour line. And at last, a zone of Inner Mongolia is chosen to prove the validity of the new method.

Modern Catchment Geomorphic Evolution Model Services (MCGEMS) is the key elements to achieve the calculation of MCGE issues by services integration based on Web services. This paper mainly focuses on MCGEMS design and implementation. The design of MCGEMS including MCGEMS components' partition and combination and MCGEMS interface definition and WSDL document are described in detail. The methods to implement MCGEMS, WCS and integration between MCGEMS and WCS on .NET platform are given. This article can be a reference for other services' design and implementation.

Satellite-based spectra are a complicated function of imaging time, imaging azimuth, imaging channel, atmospheric status and ground object category. This complicated function accordingly leads to the ambiguity and uncertainty of satellite-based spectra and image classification. The uncertainty of satellite-based spectra can be divided into two categories. One is background noise; the other is SODS (same object different spectrum) and DOSS (different object similar spectrum). A method to analyze and lower the DOSS uncertainty of satellite-based spectra based on Fourier waveform analysis is discussed in this paper

Vegetation inventory and management requires a range of fine-scale information regarding tree attributes. High spatial resolution remote sensing images can provide such information efficiently. However, tree crowns should be detected and delineated accurately beforehand. This work proposes a texture analysis based tree crown detection and delineation algorithm, which can recognize tree crown from a complicate scene. The main idea and fundamental process of the algorithm are described, image examples and performance are given, and applicable conditions and limitations are discussed.

With mass production and widespread application of remote sensing (RS) image, the management of RS data and its processing theories, techniques and algorithms need a new breakthrough. Different levels of knowledge from very large volume of RS data will be applied to RS image classification, so as to improve the efficiency and accuracy of RS image analysis and to establish an intelligent GIS based on RS images. Difficulties in RS images data mining are listed And a prototype system of RS image data mining is designed. Besides experiments are made with RSIDMM-DTM, RS image data mining classification model, based on the Microsoft decision tree mining algorithm. By comparison with ERDAS IMAGINE Expert Classifier's effects, the experiments show that the RSIDMM-DTM takes spatial relationships and other contextual information into account. In addition, the acquisition, presentation and application of knowledge are highly automatic, and the classification is rather accurate.