Real-time online trajectory map-matching has emerged as a critical component in the era of location-based services (LBS) and intelligent transportation systems (ITS). It refers to the process of aligning a user’s GPS trajectory data with the corresponding road network in real-time. This technology has significant implications for various industries and applications. As our reliance on LBS and ITS continues to grow, the demand for faster, more accurate, and more reliable trajectory map-matching methods becomes increasingly important. Contemporary online map-matching predominantly employs stream processing techniques. Based on stream processing frameworks, we propose a heterogeneous hybrid architecture for map-matching. The architecture integrates Spark Streaming and graphics processing unit (GPU) heterogeneous computing for the first time. The hidden Markov model is employed as the map-matching algorithm, and Spark Streaming serves as the distributed processing platform. We conduct map-matching experiments using a GPS taxi trajectory dataset in Beijing’s Haidian District. The results demonstrate that in comparison to other analogous research, our framework’s performance has increased by over ten times, possessing a superior data processing capability and lower latency. This research provides a novel approach of stream-based heterogeneous computation for processing large-scale geographic data.

Illegal construction is a common problem often encountered by cities with rapid development, which is hard to deal with for multiple reasons. Though these illegal buildings are primarily defined by laws and regulations, they still have physical characteristics in common that makes them identifiable. In this study, we propose an illegal building monitoring method based on satellite images and deep learning techniques, named Illegal Building Monitoring Network (IBMNet), to improve the data collection capacity for monitoring new illegal buildings. IBMNet is an end-to-end pixel-wise segmentation network with two flows: the Segment Flow, which includes a Siamese encoder and an Attention Fusion Module (AFM), and the Edge Flow, which uses Gated Convolutional Layers to extract edge information. We implement and evaluate our model in China, a country with fast development and struggling with illegal buildings. In addition to the conventional metrics, we propose a set of specialized metrics to evaluate the model’s ability to discriminate illegal buildings and legal buildings(OAB, F1Band IoUB). The model achieves great results on the Illegal Building Monitoring Dataset (IBMD) with an F1 score of 0.7990 and IoUBof 0.7449, showing its great ability in detecting illegal buildings in various scenarios and distinguishing them from legal buildings. Compared to existing methods based on urban database, IBMNet has a higher time resolution and a larger space coverage, making it more accessible in data and cost-effective for governments. The proposed method are also promising in other cities and countries with similar problems.

A high-resolution remote sensing image (HRSI) scene typically contains multiple geo-objects, and geospatial relations among these geo-objects are obvious. As the important information conveyed by HRSI, the intelligent expression of geospatial relation is helpful in understanding HRSI scenes. Previous HRSI semantic understanding was mainly based on image captions that only generate one sentence to describe image content, thereby resulting in insufficient understanding of the scene. Thus, the present letter proposes an approach to represent geospatial relations in an HRSI scene with structured form of$\langle $subject, geospatial relation, object$\rangle $. A geospatial relation triplet representation data set that contains visual and semantic information, such as category, location, and geospatial relations of the geo-objects, is constructed first. An “object-relation” message-passing mechanism is adopted to enhance the information exchange between the geo-objects and geospatial relations to predict triplets accurately. The experimental results show that the proposed method can effectively predict the geospatial relation in a HRSI scene.

Semantic segmentation is an important task of analysis and understanding of high-resolution remote sensing images (HRSIs). The deep convolutional neural network (DCNN)-based model shows their excellent performance in remote sensing image semantic segmentation. Most of the existing HRSI semantic segmentation methods are only designed for a very limited data domain, that is, the training and test images are from the same dataset. The accuracy drops sharply once a model trained on a certain dataset is used for cross-domain prediction due to the difference in feature distribution of the dataset. To this end, this article proposes an unsupervised domain adaptation framework based on adversarial learning for HRSI semantic segmentation. This framework uses high-level feature alignment to narrow the difference between the source and target domains at the semantic level. It uses the category-certainty attention module to reduce the attention of the classifier on category-level aligned features and increase the attention on category-level unaligned features. Experimental results show that the proposed method performs favorably against the state-of-the-art methods in cross-domain segmentation.

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.

This article provides a novel and practical approach for investigating the characteristics of intercity telecommunication network whose overall and complete information is unavailable. Using a mobile phone call data set covering 4.39 million subscribers registered in a particular region, we construct two intercity mobile communication subnets and infer characteristics of the whole intercity mobile communication network of China. Results confirm that intercity communication intensity is characterized by the gravity model. The communication intensity based on mobile call number decreases along the distance with a scaling exponent 0.5, whereas the scaling exponent for the communication intensity based on mobile call duration is 0.4. Moreover, we uncover the rank-size distribution of tie strength (mobile call number and duration) between a city and its neighbours. The rank-size law of tie strengths between cities is mainly determined by the rank-size distribution of cities. The distance between cities plays a less decisive role than the size distribution in the network, but significantly impacts mobile communication patterns. The call duration of individual intercity mobile communication is generally positively correlated to the communication distance, explaining why the distance decay of communication intensity based on call durations is slower than that based on call numbers. The contribution of this research is twofold. First, we identify the distance decay effect in intercity mobile communications of China and uncover the dominant impact of the rank-size distribution of cities. Second, a method for estimating the properties of the whole network according to the observed interactions of its subnets is developed.

Data quality and integrity is a critical issue of creating and maintaining a spatial database. It is possible to improve data quality by imposing constraints upon data entered into the database. Due to the complexity of spatial data and spatial relationships, existing data integrity technologies are inadequate and insufficient for spatial data. Introducing a spatial data integrity ensuring mechanism to SDBMS will bring much convenience to GIS applications, especially in department of data manufacture and management. This paper is aimed to seek for a proper solution for building such a spatial data integrity ensuring mechanism into SDBMS. From the result of our experiments and comparisons, a SDBMS supporting spatial integrity constraints has been proved to be much more intelligent and efficient in managing the spatial data.

Spatial database has become the dominant techniques to manage spatial data in geographical information systems, and geographic structured query language (GSQL) is the key of spatial database design and implementation. At present, GSQL for vector data has been widely studied. However, when the raster data are considered, there is little specific research. On the basis of corresponding ADTs (abstract data type), the GSQL-R is defined and the instances are described. The definition includes three parts, i.e., data definition language, data table schema and data manipulation language. The example statements of GSQL-R demonstrate that it provides a convenient way for raster data access and manipulation and supports vector data seamlessly

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.