Forecasting origin–destination (OD) flows can inform accurate transportation resource deployment. To improve the forecasting accuracy, some existing deep learning models are designed to capture spatial heterogeneity and spatial dependence. However, these models are generally developed from a regional perspective and treat origins and destinations separately. Additionally, the existing models capture the spatial heterogeneity via a parameter-sharing mechanism and fail to model the degree and direction of the spatial dependencies, limiting the ability of the models to learn diverse temporal patterns and to capture the deeper spatial dependencies of OD flows. To address these problems, this paper proposes a spatial–temporal graph convolutional network enhanced by geospatial knowledge (GK-STGCN). In this network, a geographical regionalization method that considers the temporal patterns of OD flows and an independent training strategy were designed to model spatial heterogeneity, and a local spatial autocorrelation-guided gating mechanism for the multilayer GCN was designed to model the directions and degrees of the local spatial dependencies. The experimental results on two OD flow datasets demonstrated that the GK-STGCN outperformed seven baselines, and the results from the ablation study demonstrated that incorporating geospatial knowledge into the deep learning model improved the model’s forecasting accuracy.

Analyzing spatiotemporal associations between different types of geographical flows across multiple scales is crucial for understanding the dynamic relationships between them. However, few spatiotemporal association analysis methods exist for geographical flows. Moreover, this analysis also faces the challenge that patterns at larger scales are biased by the cumulative effects from smaller scales if a series of spatial and temporal thresholds at multiple scales is used. Furthermore, spurious results arise if the joint population distribution pattern is not considered. To address these problems, this paper proposes an incremental spatiotemporal flow colocation quotient (ISTFCLQ), which aims to detect spatiotemporal associations between two types of flows across multiple spatial and temporal distance intervals by considering the joint population distribution pattern. The ISTFCLQ designs both global and local indicators for measuring the overall spatiotemporal association patterns of flows and their local association patterns, respectively. Synthetic data tests verified that the ISTFCLQ can effectively reduce cumulative effects and remove biases from the joint population distribution pattern, identifying the exact scale of association patterns. A case study of Xiamen Island taxi and ride-hailing trip data demonstrated the applicability of the ISTFCLQ in analyzing spatiotemporal competition patterns between taxi and ride-hailing services.

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets.

Conditional privacy-preserving authentication (CPPA) has been widely studied to ensure authentication, anonymity, and traceability in vehicle ad hoc networks (VANETs). Among CPPA schemes, certificateless CPPA (CL-CPPA) offers natural advantages for VANETs by avoiding the complex certificate management of PKI-based solutions and the key escrow problem of identity-based systems. However, many existing CL-CPPA schemes overlook secure message communication. When confidentiality is required, they often rely on additional encryption, which adds communication overhead comparable to that of certificate transmission in PKI-based solutions. Anamorphic signatures (proposed at CRYPTO'23) can be adopted to address this issue by embedding sensitive messages within signatures, which eliminates the need for separate encryption. However, existing anamorphic signature schemes are symmetric, requiring extensive key agreements between two entities, which is impractical for the dynamic nature of VANETs. In this paper, we introduce a new cryptographic primitive, asymmetric anamorphic signatures (AAS), which enables secure message sharing in a public-key setting, thus eliminating the need for complex key agreements. We also provide a concrete construction of AAS based on a variant of the Hohenberger-Waters signature scheme. By integrating AAS with CL-CPPA, we proposeSphinx, a scheme that ensures authentication, anonymity, traceability, and confidentiality without the overhead of additional secure transmissions or complex key agreements. Our security proofs and performance evaluations demonstrate the practicality and efficiency ofSphinx, particularly its advantage in reducing communication overhead.

We explore the geolocation problem, aiming to localize ground-view images on cartographic maps, without the need of any GPS priors. This task mimics the human wayfinding ability and offers high scalability and robustness by using the compact and semantic representations of maps. Current methods often rely on 2D maps to encode dense contextual information for ground-to-map matching. In this paper, we lift ground-to-map matching to a 2.5D space, where heights of structures (e.g. buildings) provide richer geometric information to guide the matching process. We propose a new approach to learning representative embeddings from multi-modal data. Specifically, we establish a projection relationship between 2D and 2.5D space. The projection is further used to combine multi-modal features from the 2D and 2.5D maps using an effective pixel-to-point fusion method. By encoding crucial geometric cues, our method learns discriminative location embeddings for matching panoramic images and maps. Additionally, we construct the first large-scale multi-modal geolocation dataset to validate our method and facilitate future research. Both single-image based and route based geolocation experiments are conducted to test our method. Extensive experiments demonstrate that the proposed method achieves significantly higher geolocation accuracy and faster convergence than previous 2D map-based approaches.

We present a purely vision based geolocation method for aircraft flying over urban and suburban environments. The method is based on matching aerial images with geolocated map tiles using a shared low dimensional embedded space of descriptors. The Euclidean distance between descriptors is used as a similarity measure between domains. The similarity between the observation and map locations is then integrated with visual odometry to track the aircraft’s position and yaw using a particle filter. Furthermore, we propose an efficient method to generate map descriptors in testing time based on interpolation, allowing compact representation of large areas giving the potential for high levels of scalability. We experimented in different cities with areas above 20 km2in size and preliminary results based on a database of aerial imagery demonstrate that the method gives good results.

The ability to localise is key for robot navigation. We describe an efficient method for vision-based localisation, which combines sequential Monte Carlo tracking with matching ground-level images to 2-D cartographic maps such as OpenStreetMaps. The matching is based on a learned embedded space representation linking images and map tiles, encoding the common semantic information present in both and providing potential for invariance to changing conditions. Moreover, the compactness of 2-D maps supports scalability. This contrasts with the majority of previous approaches based on matching with single-shot geo-referenced images or 3-D reconstructions. We present experiments using the StreetLearn and Oxford RobotCar datasets and demonstrate that the method is highly effective, giving high accuracy and fast convergence.

Despite the potential to revolutionise disease diagnosis by performing data-driven classification, clinical interpretability of ConvNet remains challenging. In this paper, a novel clinical interpretable ConvNet architecture is proposed not only for accurate glaucoma diagnosis but also for the more transparent interpretation by highlighting the distinct regions recognised by the network. To the best of our knowledge, this is the first work of providing the interpretable diagnosis of glaucoma with the popular deep learning model. We propose a novel scheme for aggregating features from different scales to promote the performance of glaucoma diagnosis, which we refer to as M-LAP. Moreover, by modelling the correspondence from binary diagnosis information to the spatial pixels, the proposed scheme generates glaucoma activations, which bridge the gap between global semantical diagnosis and precise location. In contrast to previous works, it can discover the distinguish local regions in fundus images as evidence for clinical interpretable glaucoma diagnosis. Experimental results, performed on the challenging ORIGA datasets, show that our method on glaucoma diagnosis outperforms state-of-the-art methods with the highest AUC (0.88). Remarkably, the extensive results, optic disc segmentation (dice of 0.9) and local disease focus localization based on the evidence map, demonstrate the effectiveness of our methods on clinical interpretability.

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 colocation rules. Here, we propose a visualization approach to discover colocation patterns for two independent point distributions and generate visual results. This approach makes use of the ability of human color perception. For two geographic features, our approach first generates density surfaces of the input features and then visualizes the density surfaces using a red or green light with different intensities. Then, based on the law of additive color mixing, our approach mixes the colors of the two density surfaces to generate a colocation rule map. The visualization approach can also provide local details of colocation and be used for local colocation analysis. Users can detect colocation patterns and their distribution from the colocation rule maps. We use both synthetic data and real data to test the performance of our approach.

Most existing works investigate consensus with noise following a certain distribution, e.g., Gaussian distribution, with fixed expectation and variance, which may not be satisfied in practical applications. This paper investigates the discrete system consensus under bounded noise, which is important and practical problem. We first provide necessary and sufficient conditions for the convergence of consensus under bounded noise. To be more general, we derive an analytical bound to show the max-min difference between the nodes' states when the general consensus algorithm converges to a stable state. Then, a novel consensus algorithm, fast consensus under bounded noise (FCBN), is proposed to eliminate the accumulative error caused by the bounded noise. It is proved that FCBN has a faster convergence speed and a higher consensus accuracy than general consensus algorithms. Extensive simulations demonstrate the effectiveness of the proposed algorithm.

The identification of vessel collision risk for a Maritime Intelligent Transport System (MITS) is crucial for maritime safety and management. This paper considers the identification of the Systematic Collision Risk (SCR) for an MITS based on AIS data, which is obtained by wireless communication among vessels and between vessels and shore-based stations. SCR is modeled as a function of the collision risk of each vessel. A computing method for the SCR of a two-vessel case is proposed. Meanwhile, a hierarchical clustering based simplification algorithm is provided and applied to transform the topology of an MITS, thus simplifying the computing of the SCR. Based on the two-vessel case and transformation, a bottom-to-top weighted fusion method is employed to calculate the SCR for an MITS. Extensive numerical examples of simulative and real AIS data verify the effectiveness of our modeling and computing.