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Kawasaki, Yosuke, Shunsuke Mochizuki, and Masaki Takahashi. "ASTRON: Action-Based Spatio-Temporal Robot Navigation." IEEE Access 9 (2021): 141709–24. http://dx.doi.org/10.1109/access.2021.3120216.
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Liu, Yixiao, Lei Zhang, Yixuan Zhou, Qin Xu, Wen Fu, and Tao Shen. "Clustering-Based Decision Tree for Vehicle Routing Spatio-Temporal Selection." Electronics 11, no. 15 (July 29, 2022): 2379. http://dx.doi.org/10.3390/electronics11152379.
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The algorithm of the clustering-based decision tree, which is a methodology of multimodal fusion, has made many achievements in many fields. However, it is not common in the field of transportation, especially in the application of automobile navigation. Meanwhile, the concept of Spatio-temporal data is now widely used. Therefore, we proposed a vehicle routing Spatio-temporal selection system based on a clustering-based decision tree. By screening and clustering Spatio-temporal data, which is a collection of individual point data based on historical driving data, we can identify the routes and many other features. Through the decision tree modeling of the state information of Spatio-temporal data, which includes the features of the historical data and route selection, we can obtain an optimal result, that is, the route selection made by the system. Moreover, all the above calculations and operations are done on the edge, which is different from the vast majority of current cloud computing vehicle navigation. We have also experimented with our system using real vehicle data. The experiments show that it can output path decision results for a given situation, which takes little time and is the same as the approximated case of networked navigation. The experiments yielded satisfactory results. Our system could save a lot of cloud computing power, which might change the current navigation systems.
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Ueda, Naonori. "Proactive People-flow Navigation Based on Spatio-temporal Prediction." Japanese Journal of Applied Statistics 45, no. 3 (2016): 87–102. http://dx.doi.org/10.5023/jappstat.45.87.
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Ferri, Anthony, Monika Popp, and Gebhard Wulfhorst. "Digital Directions:." Interdisciplinary Journal of Signage and Wayfinding 5, no. 2 (December 23, 2021): 7–21. http://dx.doi.org/10.15763/issn.2470-9670.2021.v5.i2.a78.
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Wayfinding in spatially complex public transit environments poses unique navigational challenges. Transfers, delays, barriers, and user capacity all influence the usability of a system. Because of the smartphone, how we navigate through these systems, and interact with the surrounding environment, is changing. The smartphone provides a spatio-temporal strategy that removes the reliance on our immediate environment and personalizes the wayfinding process -- unlike that of transit schedules, signs, and maps. How does smartphone usage influence performance and the wayfinding experience? This paper looks at smartphone usage of twelve participants through a shadowed commented walk, known as a Destination-Task Investigation, in Munich’s public transit system. The study provides insights into the role and the influence of smartphones during the wayfinding process. Furthermore, it shows that Apps providing integrated spatio-temporal information, such as Google, were used most frequently, especially for confirmation during navigation.
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Han, Shi-Yuan, Qi-Wei Sun, Qiang Zhao, Rui-Zhi Han, and Yue-Hui Chen. "Traffic Forecasting Based on Integration of Adaptive Subgraph Reformulation and Spatio-Temporal Deep Learning Model." Electronics 11, no. 6 (March 9, 2022): 861. http://dx.doi.org/10.3390/electronics11060861.
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Traffic forecasting provides the foundational guidance for many typical applications in the smart city management, such as urban traffic control, congestion avoidance, and navigation guidance. Many researchers have focused on the spatio-temporal correlations under fixed topology structure in traffic network to improve the traffic forecasting accuracy. Despite their advantages, the existing approaches are not completely discussed that the association relationship among traffic network nodes are not invariable under different traffic conditions. In this paper, a novel traffic forecasting framework is proposed by integrating the dynamic association of traffic nodes with the spatio-temporal deep learning model. To be specific, an adaptive subgraph reformulation algorithm is designed first based on the specific forecasting interval to reduce the interference of irrelevant spatio-temporal information. After that, by enhancing the attention mechanism with the generative decoder, a spatio-temporal deep learning model with only one forward operation is proposed to avoid the degradation of accuracy in the long-term prediction, in which the spatio-temporal information and the external factors (such as weather and holiday) are fused together to be as an input vector. Based on the reformulated subgraph constructed of traffic nodes with closer spatio-temporal correlation, experiments show that the proposed framework consistently outperforms other GNN (Graph Neural Network)-based state-of-the-art baselines for various forecasting intervals on a real-world dataset.
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Cai, Yifan, Richard Droste, Harshita Sharma, Pierre Chatelain, Lior Drukker, Aris T. Papageorghiou, and J. Alison Noble. "Spatio-temporal visual attention modelling of standard biometry plane-finding navigation." Medical Image Analysis 65 (October 2020): 101762. http://dx.doi.org/10.1016/j.media.2020.101762.
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Samany, Najmeh Neysani, Mahmoud Reza Delavar, Nicholas Chrisman, and Mohammad Reza Malek. "Modelling Spatio-Temporal Relevancy in Urban Context-Aware Pervasive Systems Using Voronoi Continuous Range Query and Multi-Interval Algebra." Mobile Information Systems 9, no. 3 (2013): 189–208. http://dx.doi.org/10.1155/2013/284904.
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Space and time are two dominant factors in context-aware pervasive systems which determine whether an entity is related to the moving user or not. This paper specifically addresses the use of spatio-temporal relations for detecting spatio-temporally relevant contexts to the user. The main contribution of this work is that the proposed model is sensitive to the velocity and direction of the user and applies customized Multi Interval Algebra (MIA) with Voronoi Continuous Range Query (VCRQ) to introduce spatio-temporally relevant contexts according to their arrangement in space. In this implementation the Spatio-Temporal Relevancy Model for Context-Aware Systems (STRMCAS) helps the tourist to find his/her preferred areas that are spatio-temporally relevant. The experimental results in a scenario of tourist navigation are evaluated with respect to the accuracy of the model, performance time and satisfaction of users in 30 iterations of the algorithm. The evaluation process demonstrated the efficiency of the model in real-world applications.
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Putman, Nathan F., Erica S. Jenkins, Catherine G. J. Michielsens, and David L. G. Noakes. "Geomagnetic imprinting predicts spatio-temporal variation in homing migration of pink and sockeye salmon." Journal of The Royal Society Interface 11, no. 99 (October 6, 2014): 20140542. http://dx.doi.org/10.1098/rsif.2014.0542.
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Animals navigate using a variety of sensory cues, but how each is weighted during different phases of movement (e.g. dispersal, foraging, homing) is controversial. Here, we examine the geomagnetic and olfactory imprinting hypotheses of natal homing with datasets that recorded variation in the migratory routes of sockeye ( Oncorhynchus nerka ) and pink ( Oncorhynchus gorbuscha ) salmon returning from the Pacific Ocean to the Fraser River, British Columbia. Drift of the magnetic field (i.e. geomagnetic imprinting) uniquely accounted for 23.2% and 44.0% of the variation in migration routes for sockeye and pink salmon, respectively. Ocean circulation (i.e. olfactory imprinting) predicted 6.1% and 0.1% of the variation in sockeye and pink migration routes, respectively. Sea surface temperature (a variable influencing salmon distribution but not navigation, directly) accounted for 13.0% of the variation in sockeye migration but was unrelated to pink migration. These findings suggest that geomagnetic navigation plays an important role in long-distance homing in salmon and that consideration of navigation mechanisms can aid in the management of migratory fishes by better predicting movement patterns. Finally, given the diversity of animals that use the Earth's magnetic field for navigation, geomagnetic drift may provide a unifying explanation for spatio-temporal variation in the movement patterns of many species.
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Lan, Hai, He Yin, Ying-Yi Hong, Shuli Wen, David C. Yu, and Peng Cheng. "Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route." Applied Energy 211 (February 2018): 15–27. http://dx.doi.org/10.1016/j.apenergy.2017.11.014.
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Shi, Qiang, Wujiao Dai, Rock Santerre, Zhiwei Li, and Ning Liu. "Spatially Heterogeneous Land Surface Deformation Data Fusion Method Based on an Enhanced Spatio-Temporal Random Effect Model." Remote Sensing 11, no. 9 (May 7, 2019): 1084. http://dx.doi.org/10.3390/rs11091084.
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The spatio-temporal random effect (STRE) model, a type of spatio-temporal Kalman filter model, can be used for the fusion of the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data to generate high spatio-temporal resolution deformation series, assuming that the land deformation is spatially homogeneous in the monitoring area. However, when there are multiple deformation sources in the monitoring area, complex spatial heterogeneity will appear. To improve the fusion accuracy, we propose an enhanced STRE fusion method (eSTRE) by taking spatial heterogeneity into consideration. This new method integrates the spatial heterogeneity constraints in the STRE model by constructing extra-constrained spatial bases for the heterogeneous area. The effectiveness of this method is verified by using simulated data and real land surface deformation data. The results show that eSTRE can reduce the root mean square (RMS) of InSAR interpolation results by 14% and 23% on average for a simulation experiment and Los Angeles experiment, respectively, indicating that the new proposed method (eSTRE) is substantially better than the previous STRE fusion model.
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Wei, Xiaojuan, Jinglin Li, Quan Yuan, Kaihui Chen, Ao Zhou, and Fangchun Yang. "Predicting Fine-Grained Traffic Conditions via Spatio-Temporal LSTM." Wireless Communications and Mobile Computing 2019 (January 14, 2019): 1–12. http://dx.doi.org/10.1155/2019/9242598.
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Predicting traffic conditions for road segments is the prelude of working on intelligent transportation. Many existing methods can be used for short-term or long-term traffic prediction, but they focus more on regions than on road segments. The lack of fine-grained traffic predicting approach hinders the development of ITS. Therefore, MapLSTM, a spatio-temporal long short-term memory network preluded by map-matching, is proposed in this paper to predict fine-grained traffic conditions. MapLSTM first obtains the historical and real-time traffic conditions of road segments via map-matching. Then LSTM is used to predict the conditions of the corresponding road segments in the future. Breaking the single-index forecasting, MapLSTM can predict the vehicle speed, traffic volume, and the travel time in different directions of road segments simultaneously. Experiments confirmed MapLSTM can not only achieve prediction for road segments based a large scale of GPS trajectories effectively but also have higher predicting accuracy than GPR and ConvLSTM. Moreover, we demonstrate that MapLSTM can serve various applications in a lightweight way, such as cognizing driving preferences, learning navigation, and inferring traffic emissions.
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Hirel, J., P. Gaussier, M. Quoy, J. P. Banquet, E. Save, and B. Poucet. "The hippocampo-cortical loop: Spatio-temporal learning and goal-oriented planning in navigation." Neural Networks 43 (July 2013): 8–21. http://dx.doi.org/10.1016/j.neunet.2013.01.023.
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Fischer, Colin, Monika Sester, and Steffen Schön. "Spatio-Temporal Research Data Infrastructure in the Context of Autonomous Driving." ISPRS International Journal of Geo-Information 9, no. 11 (October 25, 2020): 626. http://dx.doi.org/10.3390/ijgi9110626.
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In this paper, we present an implementation of a research data management system that features structured data storage for spatio-temporal experimental data (environmental perception and navigation in the framework of autonomous driving), including metadata management and interfaces for visualization and parallel processing. The demands of the research environment, the design of the system, the organization of the data storage, and computational hardware as well as structures and processes related to data collection, preparation, annotation, and storage are described in detail. We provide examples for the handling of datasets, explaining the required data preparation steps for data storage as well as benefits when using the data in the context of scientific tasks.
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Daria, Vincent R., Michael Lawrence Castañares, and Hans-A. Bachor. "Spatio-temporal parameters for optical probing of neuronal activity." Biophysical Reviews 13, no. 1 (February 2021): 13–33. http://dx.doi.org/10.1007/s12551-021-00780-2.
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AbstractThe challenge to understand the complex neuronal circuit functions in the mammalian brain has brought about a revolution in light-based neurotechnologies and optogenetic tools. However, while recent seminal works have shown excellent insights on the processing of basic functions such as sensory perception, memory, and navigation, understanding more complex brain functions is still unattainable with current technologies. We are just scratching the surface, both literally and figuratively. Yet, the path towards fully understanding the brain is not totally uncertain. Recent rapid technological advancements have allowed us to analyze the processing of signals within dendritic arborizations of single neurons and within neuronal circuits. Understanding the circuit dynamics in the brain requires a good appreciation of the spatial and temporal properties of neuronal activity. Here, we assess the spatio-temporal parameters of neuronal responses and match them with suitable light-based neurotechnologies as well as photochemical and optogenetic tools. We focus on the spatial range that includes dendrites and certain brain regions (e.g., cortex and hippocampus) that constitute neuronal circuits. We also review some temporal characteristics of some proteins and ion channels responsible for certain neuronal functions. With the aid of the photochemical and optogenetic markers, we can use light to visualize the circuit dynamics of a functioning brain. The challenge to understand how the brain works continue to excite scientists as research questions begin to link macroscopic and microscopic units of brain circuits.
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Cheng, Na, Shuli Song, and Wei Li. "Multi-Scale Ionospheric Anomalies Monitoring and Spatio-Temporal Analysis during Intense Storm." Atmosphere 12, no. 2 (February 4, 2021): 215. http://dx.doi.org/10.3390/atmos12020215.
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The ionosphere is a significant component of the geospace environment. Storm-induced ionospheric anomalies severely affect the performance of Global Navigation Satellite System (GNSS) Positioning, Navigation, and Timing (PNT) and human space activities, e.g., the Earth observation, deep space exploration, and space weather monitoring and prediction. In this study, we present and discuss the multi-scale ionospheric anomalies monitoring over China using the GNSS observations from the Crustal Movement Observation Network of China (CMONOC) during the 2015 St. Patrick’s Day storm. Total Electron Content (TEC), Ionospheric Electron Density (IED), and the ionospheric disturbance index are used to monitor the storm-induced ionospheric anomalies. This study finally reveals the occurrence of the large-scale ionospheric storms and small-scale ionospheric scintillation during the storm. The results show that this magnetic storm was accompanied by a positive phase and a negative phase ionospheric storm. At the beginning of the main phase of the magnetic storm, both TEC and IED were significantly enhanced. There was long-duration depletion in the topside ionospheric TEC during the recovery phase of the storm. This study also reveals the response and variations in regional ionosphere scintillation. The Rate of the TEC Index (ROTI) was exploited to investigate the ionospheric scintillation and compared with the temporal dynamics of vertical TEC. The analysis of the ROTI proved these storm-induced TEC depletions, which suppressed the occurrence of the ionospheric scintillation. To improve the spatial resolution for ionospheric anomalies monitoring, the regional Three-Dimensional (3D) ionospheric model is reconstructed by the Computerized Ionospheric Tomography (CIT) technique. The spatial-temporal dynamics of ionospheric anomalies during the severe geomagnetic storm was reflected in detail. The IED varied with latitude and altitude dramatically; the maximum IED decreased, and the area where IEDs were maximum moved southward.
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Macenski, Steve, David Tsai, and Max Feinberg. "Spatio-temporal voxel layer: A view on robot perception for the dynamic world." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142091053. http://dx.doi.org/10.1177/1729881420910530.
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The spatio-temporal voxel grid is an actively maintained open-source project providing an improved three-dimensional environmental representation that has been garnering increased adoption in large, dynamic, and complex environments. We provide a voxel grid and the Costmap 2-D layer plug-in, Spatio-Temporal Voxel Layer, powered by a real-time sparse occupancy grid with constant time access to voxels which does not scale with the environment’s size. We replace ray-casting with a new clearing technique we dub frustum acceleration that does not assume a static environment and in practice, represents moving environments better. Our method operates at nearly 400% less CPU load on average while processing 9 QVGA resolution depth cameras as compared to the voxel layer. This technique also supports sensors such as three-dimensional laser scanners, radars, and additional modern sensors that were previously unsupported in the available ROS Navigation framework that has become staples in the roboticists’ toolbox. These sensors are becoming more widely used in robotics as sensor prices are driven down and mobile compute capabilities improve. The Spatio-Temporal Voxel Layer was developed in the open with community feedback over its development life cycle and continues to have additional features and capabilities added by the community. As of February 2019, the Spatio-Temporal Voxel Layer is being used on over 600 robots worldwide in warehouses, factories, hospitals, hotels, stores, and libraries. The open-source software can be viewed and installed on its GitHub page at https://github.com/SteveMacenski/spatio_temporal_voxel_layer .
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Teng, Wenxin, and Yanhui Wang. "Real-Time Map Matching: A New Algorithm Integrating Spatio-Temporal Proximity and Improved Weighted Circle." Open Geosciences 11, no. 1 (July 8, 2019): 288–97. http://dx.doi.org/10.1515/geo-2019-0023.
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Abstract Previous real-time map matching algorithms for in-vehicle navigation systems had some efficiencies and defects on time lagging and low accuracy. As a response, this paper proposes a new algorithm that integrates STP (spatio-temporal proximity) and IWC (improved weighted circle), in which the new algorithm proposes STP to dynamically refine candidate matching roads, and IWC to adaptively identify the optimal matching road. Specifically, three spatio-temporal proximity indicators are defined in STP to build a three-dimensional stereoscopic cone, and then the two-dimensional projection of the cone are adopted to dynamically select the candidate matching roads. Further, by adaptively setting the weight, the IWC algorithm is developed to integrate three new parameters to adaptively determine the optimal matching road. The test results show that the matching accuracy of the algorithm is over 95%, much higher than that of the existing algorithm, which demonstrates the feasibility and efficiency of the new algorithm.
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Mufti, Faisal, Robert Mahony, and Jochen Heinzmann. "Robust estimation of planar surfaces using spatio-temporal RANSAC for applications in autonomous vehicle navigation." Robotics and Autonomous Systems 60, no. 1 (January 2012): 16–28. http://dx.doi.org/10.1016/j.robot.2011.08.009.
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Nguyen, Vu Anh, Janusz A. Starzyk, and Wooi-Boon Goh. "A spatio-temporal Long-term Memory approach for visual place recognition in mobile robotic navigation." Robotics and Autonomous Systems 61, no. 12 (December 2013): 1744–58. http://dx.doi.org/10.1016/j.robot.2012.12.004.
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Holst, Christoph, and Hans Neuner. "Spatio-Temporal Models for Vibration Monitoring of Elongated Structures Using Profile Laser Scans." Remote Sensing 13, no. 7 (April 2, 2021): 1369. http://dx.doi.org/10.3390/rs13071369.
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Vibration monitoring is a frequent task within the general topic of Structural Health Monitoring. For this monitoring, usually accelerometers, strain gauges, fibre optic sensors or Global Navigation Satellite System (GNSS) receivers are placed on pre-selected positions on the structure and the point-wise measurements are individually processed to estimate the relevant modal parameters, for example, oscillating amplitudes and natural frequencies. If laser scanners were used for vibration monitoring, the analyses could be performed with a significantly higher spatial resolution that would be beneficial especially for locating structural weaknesses. However, to apply laser scanners rigorously to vibration monitoring, spatio-temporal models need to be set up. With this study, we develop and discuss four spatio-temporal models applied to the simulated vibration monitoring of a bridge deck. Therefore, we formulate either functional as well as stochastic connections between neighbored measurement positions within the estimation of the parameters of a harmonic oscillation. We reveal that those models allow an improved parameter estimation compared to the usually used strategies—even at lower measurement frequencies and shorter observation lengths.
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Ahmed, Irfan, Indika Kumara, Vahideh Reshadat, A. S. M. Kayes, Willem-Jan van den Heuvel, and Damian A. Tamburri. "Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study." Electronics 11, no. 1 (December 29, 2021): 106. http://dx.doi.org/10.3390/electronics11010106.
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Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time.
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Donker, Jasper, Marcel van Maarseveen, and Gerben Ruessink. "Spatio-Temporal Variations in Foredune Dynamics Determined with Mobile Laser Scanning." Journal of Marine Science and Engineering 6, no. 4 (October 30, 2018): 126. http://dx.doi.org/10.3390/jmse6040126.
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Coastal foredunes are highly dynamic landforms because of rapid erosion by waves and currents during storm surges in combination with gradual accretion by aeolian transport during more quiescent conditions. While our knowledge into the mechanisms behind foredune erosion have reached considerable maturity, this is not the case for foredune growth. High resolution spatio-temporal data sets of beach and foredune topography, which are needed to increase our understanding of mechanisms behind aeolian transport in coastal environments and to develop predictive dune-accretion models, are scarce. Here we aim to illustrate that repeated Mobile Laser Scanning (MLS) surveys provide an accurate and robust method to study detailed changes in dune volume on the timescales of months to years. An MLS system attached to an inertial navigation system with RTK-GPS (INS-GPS) was used to carry out 13 surveys along a 3.5-km Dutch beach over a 2.5-year period. The height observations were post-processed and averaged into 1 × 1 m Digital Elevation Models (DEMs). Comparison with airborne LiDAR and RTK-GPS data revealed that the obtained DEMs were accurate and robust up to a height of 15 m in the foredune above which dense vegetation hampers the MLS to see the sand surface. Estimates of dune volume change of the lower 13 m of the foredune have an uncertainty of about 0.25 m 3 /m. Time series of dune volume change show that at our study site the foredune accretes throughout the year at similar rates (10 m 3 /m/year), while marine erosion is obviously confined to storm surges. Foredune accretion and erosion vary spatially, which can, in part, be related to variations in beach width.
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Stilla, Donato, Mehrez Zribi, Nazzareno Pierdicca, Nicolas Baghdadi, and Mireille Huc. "Desert Roughness Retrieval Using CYGNSS GNSS-R Data." Remote Sensing 12, no. 4 (February 24, 2020): 743. http://dx.doi.org/10.3390/rs12040743.
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The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (Γ). In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS Γ measurements has been developed, at the relatively fine resolution of 0.03° × 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coefficient are compared and found to be strongly correlated. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness.
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Eroglu, Orhan, Mehmet Kurum, Dylan Boyd, and Ali Cafer Gurbuz. "High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks." Remote Sensing 11, no. 19 (September 28, 2019): 2272. http://dx.doi.org/10.3390/rs11192272.
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This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals to its highest potential within a machine learning framework. The methodology employs a fully connected Artificial Neural Network (ANN) regression model to perform SM predictions through learning the nonlinear relations of SM and other land geophysical parameters to the CYGNSS observables. In situ SM measurements from several International SM Network (ISMN) sites are used as reference labels; CYGNSS incidence angles, derived reflectivity and trailing edge slope (TES) values, as well as ancillary data, are exploited as input features for training and validation of the ANN model. In particular, the utilized ancillary data consist of normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness). Land cover classification and inland water body masks are also used for the intermediate derivations and quality control purposes. The proposed algorithm assumes uniform SM over a 0.0833 ∘ × 0.0833 ∘ (approximately 9 km × 9 km around the equator) lat/lon grid for any CYGNSS observation that falls within this window. The proposed technique is capable of generating sub-daily and high-resolution SM predictions as it does not rely on time-series or spatial averaging of the CYGNSS observations. Once trained on the data from ISMN sites, the model is independent from other SM sources for retrieval. The estimation results obtained over unseen test data are promising: SM predictions with an unbiased root mean squared error of 0.0544 cm 3 /cm 3 and Pearson correlation coefficient of 0.9009 are reported for 2017 and 2018.
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Alam, Md Rakibul, Arif Mohaimin Sadri, and Xia Jin. "Identifying Public Perceptions toward Emerging Transportation Trends through Social Media-Based Interactions." Future Transportation 1, no. 3 (December 15, 2021): 794–813. http://dx.doi.org/10.3390/futuretransp1030044.
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The objective of this study is to mine and analyze large-scale social media data (rich spatio-temporal data unlike traditional surveys) and develop comparative infographics of emerging transportation trends and mobility indicators by adopting natural language processing and data-driven techniques. As such, first, around 13 million tweets for about 20 days (16 December 2019–4 January 2020) from North America were collected, and tweets closely aligned with emerging transportation and mobility trends (such as shared mobility, vehicle technology, built environment, user fees, telecommuting, and e-commerce) were identified. Data analytics captured spatio-temporal differences in social media user interactions and concerns about such trends, as well as topics of discussions formed through such interactions. California, Florida, Georgia, Illinois, New York are among the highly visible cities discussing such trends. Being positive overall, people carried more positive views on shared mobility, vehicle technology, telecommuting, and e-commerce, while being more negative on user fees, and the built environment. Ride-hailing, fuel efficiency, trip navigation, daily as well as shopping and recreational activities, gas price, tax, and product delivery were among the emergent topics. The social media data-driven framework would allow real-time monitoring of transportation trends by agencies, researchers, and professionals.
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Liu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu, and Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning." Proceedings of the VLDB Endowment 14, no. 3 (November 2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.
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Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.
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Kou, Ruixiong, Bisheng Yang, Zhen Dong, Fuxun Liang, and Shuwen Yang. "Mapping the spatio-temporal visibility of global navigation satellites in the urban road areas based on panoramic imagery." International Journal of Digital Earth 14, no. 7 (February 14, 2021): 807–20. http://dx.doi.org/10.1080/17538947.2021.1886357.
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Ma, Xiaojun, Bin Liu, Wujiao Dai, Cuilin Kuang, and Xuemin Xing. "Potential Contributors to Common Mode Error in Array GPS Displacement Fields in Taiwan Island." Remote Sensing 13, no. 21 (October 21, 2021): 4221. http://dx.doi.org/10.3390/rs13214221.
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The existence of the common mode error (CME) in the continuous global navigation satellite system (GNSS) coordinate time series affects geophysical studies that use GNSS observations. To understand the potential contributors of CME in GNSS networks in Taiwan and their effect on velocity estimations, we used the principal component analysis (PCA) and independent component analysis (ICA) to filter the vertical coordinate time series from 44 high-quality GNSS stations in Taiwan island in China, with a span of 10 years. The filtering effects have been evaluated and the potential causes of the CME are analyzed. The root-mean-square values decreased by approximately 14% and 17% after spatio-temporal filtering using PCA and ICA, respectively. We then discuss the relationship between the CME sources obtained by ICA and the environmental loads. The results reveal that the independent displacements extracted by ICA correlate with the atmospheric mass loading (ATML) and land water storage mass loading (LWS) of Taiwan in terms of both its amplitude and phase. We then use the white noise plus power law noise model to quantitatively estimate the noise characteristics of the pre- and post-filtered coordinate time series based on the maximum likelihood estimation criterion. The results indicate that spatio-temporal filtering reduces the amplitude of the PL and the periodic terms in the GPS time series.
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Senyurek, Volkan, Fangni Lei, Dylan Boyd, Ali Cafer Gurbuz, Mehmet Kurum, and Robert Moorhead. "Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations." Remote Sensing 12, no. 21 (October 25, 2020): 3503. http://dx.doi.org/10.3390/rs12213503.
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This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.
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Han, Yi, Jia Luo, and Xiaohua Xu. "On the Constellation Design of Multi-GNSS Reflectometry Mission Using the Particle Swarm Optimization Algorithm." Atmosphere 10, no. 12 (December 13, 2019): 807. http://dx.doi.org/10.3390/atmos10120807.
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Due to the great success of the CYclone Global Navigation Satellite System (CYGNSS) mission, the follow-on GNSS Reflectometry (GNSS-R) missions are being planned. In the perceivable future, signal sources for GNSS-R missions can originate from multiple global navigation satellite systems (GNSSs) including Global Positioning System (GPS), Galileo, GLONASS, and BeiDou. On the other hand, to facilitate the operational capability for sensing ocean, land, and ice features globally, multi-satellite low Earth orbit (LEO) constellations with global coverage and high spatio-temporal resolutions should be considered in the design of the follow-on GNSS-R constellation. In the present study, the particle swarm optimization (PSO) algorithm was applied to seek the optimal configuration parameters of 2D-lattice flower constellations (2D-LFCs) composed of 8, 24, 60, and 120 satellites, respectively, for global GNSS-R observations, and the fitness function was defined as the length of the time for the percentage coverage of the reflection observations reaches 90% of the globe. The configuration parameters for the optimal constellations are presented, and the performances of the optimal constellations for GNSS-R observations including the visited and the revisited coverages, and the spatial and temporal distributions of the reflections were further compared. Although the results showed that all four optimized constellations could observe GNSS reflections with proper temporal and spatial distributions, we recommend the optimal 24- and 60-satellite 2D-LFCs for future GNSS-R missions, taking into account both the performance and efficiency for the deployment of the GNSS-R missions.
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Wu, Huisheng, Zhaoli Liu, Shuwen Zhang, and Xiuling Zuo. "A spatio-temporal data model for road network in data center based on incremental updating in vehicle navigation system." Chinese Geographical Science 21, no. 3 (January 8, 2011): 346–53. http://dx.doi.org/10.1007/s11769-011-0446-4.
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Ravanelli, R., and M. Crespi. "ANALYSIS OF THE FLOATING CAR DATA OF TURIN PUBLIC TRANSPORTATION SYSTEM: FIRST RESULTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 515–21. http://dx.doi.org/10.5194/isprs-archives-xlii-4-515-2018.
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<p><strong>Abstract.</strong> Global Navigation Satellite System (GNSS) sensors represent nowadays a mature technology, low-cost and efficient, to collect large spatio-temporal datasets (Geo Big Data) of vehicle movements in urban environments. Anyway, to extract the mobility information from such Floating Car Data (FCD), specific analysis methodologies are required. In this work, the first attempts to analyse the FCD of the Turin Public Transportation system are presented. Specifically, a preliminary methodology was implemented, in view of an automatic and possible real-time impedance map generation. The FCD acquired by all the vehicles of the Gruppo Torinese Trasporti (GTT) company in the month of April 2017 were thus processed to compute their velocities and a visualization approach based on Osmnx library was adopted. Furthermore, a preliminary temporal analysis was carried out, showing higher velocities in weekend days and not peak hours, as could be expected. Finally, a method to assign the velocities to the line network topology was developed and some tests carried out.</p>
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Manning, Jaime K., Eloise S. Fogarty, Mark G. Trotter, Derek A. Schneider, Peter C. Thomson, Russell D. Bush, and Greg M. Cronin. "A pilot study into the use of global navigation satellite system technology to quantify the behavioural responses of sheep during simulated dog predation events." Animal Production Science 54, no. 10 (2014): 1676. http://dx.doi.org/10.1071/an14221.
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The predation of sheep (Ovis aries) by wild and domestic dogs (Canis lupis) is a major issue in Australia, causing serious welfare issues to inflicted animals. The estimated cost of sheep and cattle production losses caused by wild dogs when combined with an extensive range of control measures, costs the Australian economy AU$66 million annually. Spatio-temporal data derived from global navigation satellite system (GNSS) devices were used to quantify the behavioural responses of two flocks of 15 Merino ewes ranging from 2 to 8 years old (average 4.5 years) during simulated dog predation events. Each sheep was fitted with a GNSS collar, and the behavioural responses of the sheep were video recorded during six trials (three per flock). The behavioural data collated from video recordings were then compared with the movement metrics derived from the GNSS collars. Derived metrics include the spatial distribution of flock members, speed of animal movement and specific behavioural changes including centripetal rotation (circling behaviour of the flock, with individual sheep seeking the centre). While the spatial distribution data did not appear to be specific enough to enable identification of a predation event, the velocity of sheep was higher (P < 0.001) during compared with before and after a simulated dog predation event. Centripetal rotation occurred in 80% of the simulated predation events during this study, and may provide a means for identifying predation. The spatio-temporal data from GNSS devices have potential as a research tool to assist in understanding sheep movement patterns during a dog attack. While further research and mathematical modelling of predation events is clearly required, the application of remote sensing technology has the potential to improve future livestock monitoring.
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Lacerda, Bruno, Fatma Faruq, David Parker, and Nick Hawes. "Probabilistic planning with formal performance guarantees for mobile service robots." International Journal of Robotics Research 38, no. 9 (June 16, 2019): 1098–123. http://dx.doi.org/10.1177/0278364919856695.
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We present a framework for mobile service robot task planning and execution, based on the use of probabilistic verification techniques for the generation of optimal policies with attached formal performance guarantees. Our approach is based on a Markov decision process model of the robot in its environment, encompassing a topological map where nodes represent relevant locations in the environment, and a range of tasks that can be executed in different locations. The navigation in the topological map is modeled stochastically for a specific time of day. This is done by using spatio-temporal models that provide, for a given time of day, the probability of successfully navigating between two topological nodes, and the expected time to do so. We then present a methodology to generate cost optimal policies for tasks specified in co-safe linear temporal logic. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We introduce a task progression function and present an approach to generate policies that are formally guaranteed to, in decreasing order of priority: maximize the probability of finishing the task; maximize progress towards completion, if this is not possible; and minimize the expected time or cost required. We illustrate and evaluate our approach with a scalability evaluation in a simulated scenario, and report on its implementation in a robot performing service tasks in an office environment for long periods of time.
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Gignac, Charles, Monique Bernier, and Karem Chokmani. "IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area." Cryosphere 13, no. 2 (February 6, 2019): 451–68. http://dx.doi.org/10.5194/tc-13-451-2019.
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Abstract. A reliable knowledge and assessment of the sea ice conditions and their evolution in time is a priority for numerous decision makers in the domains of coastal and offshore management and engineering as well as in commercial navigation. As of today, countless research projects aimed at both modelling and mapping past, actual and future sea ice conditions were completed using sea ice numerical models, statistical models, educated guesses or remote sensing imagery. From this research, reliable information helping to understand sea ice evolution in space and in time is available to stakeholders. However, no research has, until present, assessed the evolution of sea ice cover with a frequency modelling approach, by identifying the underlying theoretical distribution describing the sea ice behaviour at a given point in space and time. This project suggests the development of a probabilistic tool, named IcePAC, based on frequency modelling of historical 1978–2015 passive microwave sea ice concentrations maps from the EUMETSAT OSI-409 product, to study the sea ice spatio-temporal behaviour in the waters of the Hudson Bay system in northeast Canada. Grid-cell-scale models are based on the generalized beta distribution and generated at a weekly temporal resolution. Results showed coherence with the Canadian Ice Service 1981–2010 Sea Ice Climatic Atlas average freeze-up and melt-out dates for numerous coastal communities in the study area and showed that it is possible to evaluate a range of plausible events, such as the shortest and longest probable ice-free season duration, for any given location in the simulation domain. Results obtained in this project pave the way towards various analyses on sea ice concentration spatio-temporal distribution patterns that would gain in terms of information content and value by relying on the kind of probabilistic information and simulation data available from the IcePAC tool.
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Gramann, K., H. J. Müller, B. Schönebeck, and G. Debus. "The neural basis of ego- and allocentric reference frames in spatial navigation: Evidence from spatio-temporal coupled current density reconstruction." Brain Research 1118, no. 1 (November 2006): 116–29. http://dx.doi.org/10.1016/j.brainres.2006.08.005.
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Palamà, Riccardo, Michele Crosetto, Jacek Rapinski, Anna Barra, María Cuevas-González, Oriol Monserrat, Bruno Crippa, Natalia Kotulak, Marek Mróz, and Magdalena Mleczko. "A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring." Remote Sensing 14, no. 9 (May 2, 2022): 2182. http://dx.doi.org/10.3390/rs14092182.
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This work addresses a methodology based on the interferometric synthetic aperture radar (InSAR) applied to analyze and monitor ground-motion phenomena induced by underground mining activities in the Legnica-Glogow copper district, south-western Poland. The adopted technique employs an InSAR processing chain that exploits a stack of Sentinel-1 synthetic aperture radar (SAR) images using a small baseline multitemporal approach. Interferograms with small temporal baselines are first selected, then their network is optimized and reduced to eliminate noisy data, in order to mitigate the effect of decorrelation sources related to seasonal phenomena, i.e., snow and vegetation growth, and to the radar acquisition geometry. The atmospheric disturbance is mitigated using a spatio-temporal filter based on the nonequispaced fast Fourier transform. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against global navigation satellite system (GNSS) measurements. In this context, a significant threat to the built environment is represented by seismic tremors triggered by underground mining activities, which are analyzed using the proposed method to integrate the information gathered by in situ seismometer devices.
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Xu, Xiaohua, Yi Han, Jia Luo, Jens Wickert, and Milad Asgarimehr. "Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms." Remote Sensing 11, no. 5 (March 8, 2019): 571. http://dx.doi.org/10.3390/rs11050571.
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Given the great achievements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission in providing huge amount of GPS radio occultation (RO) data for weather forecasting, climate research, and ionosphere monitoring, further Global Navigation Satellite System (GNSS) RO missions are being followingly planned. Higher spatial and also temporal sampling rates of RO observations, achievable with higher number of GNSS/receiver satellites or optimization of the Low Earth Orbit (LEO) constellation, are being studied by high number of researches. The objective of this study is to design GNSS RO missions which provide multi-GNSS RO events (ROEs) with the optimal performance over the globe. The navigation signals from GPS, GLONASS, BDS, Galileo, and QZSS are exploited and two constellation patterns, the 2D-lattice flower constellation (2D-LFC) and the 3D-lattice flower constellation (3D-LFC), are used to develop the LEO constellations. To be more specific, two evolutionary algorithms, including the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used for searching the optimal constellation parameters. The fitness function of the evolutionary algorithms takes into account the spatio-temporal sampling rate. The optimal RO constellations are obtained for which consisting of 6–12 LEO satellites. The optimality of the LEO constellations is evaluated in terms of the number of global ROEs observed during 24 h and the coefficient value of variation (COV) representing the uniformity of the point-to-point distributions of ROEs. It is found that for a certain number of LEO satellites, the PSO algorithm generally performs better than the GA, and the optimal 2D-LFC generally outperforms the optimal 3D-LFC with respect to the uniformity of the spatial and temporal distributions of ROEs.
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Ushio, Shuki. "Factors affecting fast-ice break-up frequency in Lützow-Holm Bay, Antarctica." Annals of Glaciology 44 (2006): 177–82. http://dx.doi.org/10.3189/172756406781811835.
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AbstractAntarctic fast-ice variation is investigated using satellite images and ship’s ice navigation logs, focusing on break-up phenomena in Lützow-Holm Bay. Although spatio-temporal scales for breakup events vary somewhat for each event, their commencement is generally in autumn and almost always in the same region. Specifically, the 1997/98 break-up event occurred over a wide area and continued for a long time after the initial break-up. Since then, break-ups have recurred until 2004, and a total of 20 annual events have been detected and monitored since 1980. Moreover, information from icebreaker navigation logs shows that unstable fast-ice conditions occurred in the 1980s and after the late 1990s. From the analysis of surface meteorological data and the offshore pack-ice distribution, anomalously shallow snow-cover depths and a peculiar retreat pattern of the ice edge are found to be factors that favour fast-ice break-up. The pack-ice distribution controls the propagation of ocean swell inside the bay; encroaching swells are likely to mechanically disintegrate fast-ice during autumn prior to the annual formation of the protective pack-ice cover to the north. Less snow cover also leads to fast-ice weakening as the melt season progresses and broken floes are then transported offshore by prevailing southerly winds.
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Vaquero-Martínez, Javier, and Manuel Antón. "Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics." Remote Sensing 13, no. 12 (June 11, 2021): 2287. http://dx.doi.org/10.3390/rs13122287.
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After 30 years since the beginning of the Global Positioning System (GPS), or, more generally, Global Navigation Satellite System (GNSS) meteorology, this technique has proven to be a reliable method for retrieving atmospheric water vapor; it is low-cost, weather independent, with high temporal resolution and is highly accurate and precise. GNSS ground-based networks are becoming denser, and the first stations installed have now quite long time-series that allow the study of the temporal features of water vapor and its relevant role inside the climate system. In this review, the different GNSS methodologies to retrieve atmospheric water vapor content re-examined, such as tomography, conversion of GNSS tropospheric delay to water vapor estimates, analyses of errors, and combinations of GNSS with other sources to enhance water vapor information. Moreover, the use of these data in different kinds of studies is discussed. For instance, the GNSS technique is commonly used as a reference tool for validating other water vapor products (e.g., radiosounding, radiometers onboard satellite platforms or ground-based instruments). Additionally, GNSS retrievals are largely used in order to determine the high spatio-temporal variability and long-term trends of atmospheric water vapor or in models with the goal of determining its notable influence on the climate system (e.g., assimilation in numerical prediction, as input to radiative transfer models, study of circulation patterns, etc.).
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Durand, J. M. "Satellite Navigation: GPS Inadequacies: Comparative Study into Solutions for Civil Aviation." Journal of Navigation 43, no. 1 (January 1990): 8–17. http://dx.doi.org/10.1017/s037346330001376x.
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The Global Positioning System (GPS) will be an extremely high-performance satellite-based navigation system which is expected to provide a sole means air navigation service for most aeronautical flight phases. It will be particularly suitable for ‘en route’, ‘terminal’ and ‘non-precision approach’ phases, thus providing substantial savings on aircraft operating costs.However, GPS has three major disadvantages for civil aviation: (1) Insufficient system integrity, since satellites can transmit erroneous information for two hours before being repaired or neutralized. In such an event, the many simultaneous users of the satellite that has lost its integrity can derive false positions and remain unaware of the problem. (2) Availability constrained by the limited number of satellites. Users are then unable to obtain a position fix or else obtain a result with significantly degraded performance. (3) Deliberate spatio-temporal degradation (selective availability) of system performance, the characteristics of which are not fully known or defined.Many solutions to these problems have been put forward. One concept uses the redundancy of the GPS system itself (receiver autonomous integrity monitoring). Another set of solutions is based on complementary information from autonomous navigation equipment (altimeter, clock, inertial system) or external navigation systems already available or being developed (Omega, Loran-C, GLONASS). A third type of solution is to implement a system by which to monitor the status of the GPS satellites and broadcast the information to users.This paper reports on the different techniques put forward and uses different qualitative criteria (technical feasibility, cost, political independence, etc.) to assess their suitability for civil aviation applications. The comparison leads to the recommendation of a system to monitor the status of the GPS satellites and broadcast the information to users. The characteristics of such messages would be as similar as possible to those of GPS messages.
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Shi, Xiaodan, Xiaowei Shao, Zipei Fan, Renhe Jiang, Haoran Zhang, Zhiling Guo, Guangming Wu, Wei Yuan, and Ryosuke Shibasaki. "Multimodal Interaction-Aware Trajectory Prediction in Crowded Space." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11982–89. http://dx.doi.org/10.1609/aaai.v34i07.6874.
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Accurate human path forecasting in complex and crowded scenarios is critical for collision avoidance of autonomous driving and social robots navigation. It still remains as a challenging problem because of dynamic human interaction and intrinsic multimodality of human motion. Given the observation, there is a rich set of plausible ways for an agent to walk through the circumstance. To address those issues, we propose a spatio-temporal model that can aggregate the information from socially interacting agents and capture the multimodality of the motion patterns. We use mixture density functions to describe the human path and predict the distribution of future paths with explicit density. To integrate more factors to model interacting people, we further introduce a coordinate transformation to represent the relative motion between people. Extensive experiments over several trajectory prediction benchmarks demonstrate that our method is able to forecast various plausible futures in complex scenarios and achieves state-of-the-art performance.
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Hartmann, Dirk, and Peter Hasel. "Efficient Dynamic Floor Field Methods for Microscopic Pedestrian Crowd Simulations." Communications in Computational Physics 16, no. 1 (July 2014): 264–86. http://dx.doi.org/10.4208/cicp.200513.290114a.
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AbstractFloor field methods are one of the most popular medium-scale navigation concepts in microscopic pedestrian simulators. Recently introduced dynamic floor field methods have significantly increased the realism of such simulations, i.e. agreement of spatio-temporal patterns of pedestrian densities in simulations with real world observations. These methods update floor fields continuously taking other pedestrians into account. This implies that computational times are mainly determined by the calculation of floor fields. In this work, we propose a new computational approach for the construction of dynamic floor fields. The approach is based on the one hand on adaptive grid concepts and on the other hand on a directed calculation of floor fields, i.e. the calculation is restricted to the domain of interest. Combining both techniques the computational complexity can be reduced by a factor of 10 as demonstrated by several realistic scenarios. Thus on-line simulations, a requirement of many applications, are possible for moderate realistic scenarios.
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Pang, Yueyong, Lizhi Miao, Liangchen Zhou, and Guonian Lv. "An Indoor Space Model of Building Considering Multi-Type Segmentation." ISPRS International Journal of Geo-Information 11, no. 7 (June 28, 2022): 367. http://dx.doi.org/10.3390/ijgi11070367.
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Indoor space is a core part of supporting indoor applications. Most of the existing indoor space models are expressed from three space scales: building, floor, and room, and the granularity is not fine enough, lacking the expression of each functional subspace inside the room. In this study, we first analyzed the spatio-temporal segmentation characteristics of indoor space, and proposed a multi-level indoor space model framework that takes into account multiple types of segmentation. As well, based on the IFC (Industry Foundation Classes) standard, the extension of the indoor functional subspace was realized. The experimental results showed that the indoor space model proposed in this paper can effectively support the expression of functional subspace under the multi-type segmentation based on indoor elements, especially from the aspects of semantics, geometry, relationship, and attribute. This study enriches the granularity of existing indoor models and provides support for refined indoor navigation and evacuation applications.
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O'Keefe, John, Neil Burgess, James G. Donnett, Kathryn J. Jeffery, and Eleanor A. Maguire. "Place cells, navigational accuracy, and the human hippocampus." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1373 (August 29, 1998): 1333–40. http://dx.doi.org/10.1098/rstb.1998.0287.
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The hippocampal formation in both rats and humans is involved in spatial navigation. In the rat, cells coding for places, directions, and speed of movement have been recorded from the hippocampus proper and/or the neighbouring subicular complex. Place fields of a group of the hippocampal pyramidal cells cover the surface of an environment but do not appear to do so in any systematic fashion. That is, there is no topographical relation between the anatomical location of the cells within the hippocampus and the place fields of these cells in an environment. Recent work shows that place cells are responding to the summation of two or more Gaussian curves, each of which is fixed at a given distance to two or more walls in the environment. The walls themselves are probably identified by their allocentric direction relative to the rat and this information may be provided by the head direction cells. The right human hippocampus retains its role in spatial mapping as demonstrated by its activation during accurate navigation in imagined and virtual reality environments. In addition, it may have taken on wider memory functions, perhaps by the incorporation of a linear time tag which allows for the storage of the times of visits to particular locations. This extended system would serve as the basis for a spatio–temporal event or episodic memory system.
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Liu, Xiaoxu, Weihua Bai, Junming Xia, Feixiong Huang, Cong Yin, Yueqiang Sun, Qifei Du, et al. "FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval." Remote Sensing 13, no. 23 (November 27, 2021): 4820. http://dx.doi.org/10.3390/rs13234820.
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Based on deep learning, this paper proposes a new hybrid neural network model, a recurrent deep neural network using a feature attention mechanism (FA-RDN) for GNSS-R global sea surface wind speed retrieval. FA-RDN can process data from the Cyclone Global Navigation Satellite System (CYGNSS) satellite mission, including characteristics of the signal, spatio-temporal, geometry, and instrument. FA-RDN can receive data extended in temporal dimension and mine the temporal correlation information of features through the long-short term memory (LSTM) neural network layer. A feature attention mechanism is also added to improve the model’s computational efficiency. To evaluate the model performance, we designed comparison and validation experiments for the retrieval accuracy, enhancement effect, and stability of FA-RDN by comparing the evaluation criteria results. The results show that the wind speed retrieval root mean square error (RMSE) of the FA-RDN model can reach 1.45 m/s, 10.38%, 6.58%, 13.28%, 17.89%, 20.26%, and 23.14% higher than that of Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), respectively, confirming the feasibility and effectiveness of the designed method. At the same time, the designed model has better stability and applicability, serving as a new research idea of data mining and feature selection, as well as a reference model for GNSS-R-based sea surface wind speed retrieval.
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Radu, Violeta-Monica, Petra Ionescu, Gyorgy Deak, Elena Diacu, Irina-Elena Ciobotaru, Ecaterina Marcu, and Mariana Pipirigeanu. "Statistical Distribution and Spatio-Temporal Variation of Nutrients in Lower Danube River Waters Between km 375 - km 175 in Relation to Hydrological Regime." Revista de Chimie 71, no. 4 (May 5, 2020): 71–80. http://dx.doi.org/10.37358/rc.20.4.8044.
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Nutrient pollution, a growing problem for most European river basins, is a priority challenge in the Danube River Basin, a river that interconnects freshwater with the marine environment. The nutrient content of the Lower Danube waters in a river area where hydro-technical works to improve navigation conditions were carried out, respectively between km 375 and km 175, was evaluated in conection with other water quality parameters and with the hydrological regime. This paper is based on the data obtained during the period 2011-2017 on water samples taken from 10 sampling sections, and the following parameters were investigated: pH, ammonia nitrogen - NH4-N, nitrites - NO2-N, nitrates - NO3-N, total nitrogen - TN, orthophosphates - PO4-P, total phosphorus - TP, Chlorophyll a - Chl �a�, all of which are correlated with water flow - Q. The results showed a significant correlation between the nutrients content, Chl �a�, and water flow, taking into account both the anthropic and climatic pressures on the aquatic ecosystem and the impact of the water body loads on the Black Sea coastal zone.
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Stoll, Stefan, and Peter Beeck. "Larval fish in troubled waters — is the behavioural response of larval fish to hydrodynamic impacts active or passive?" Canadian Journal of Fisheries and Aquatic Sciences 69, no. 10 (October 2012): 1576–84. http://dx.doi.org/10.1139/f2012-086.
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In a mesocosm experiment, we tested whether larval fish show an active behavioural response to hydrodynamic impacts. Exposing 1- to 3-week-old allis shad ( Alosa alosa ) larvae to a pulsed wave regime, we found that already 1-week-old larvae immediately adapt their microhabitat use and activity patterns at the onset of the wave pulses. The fish larvae instantaneously increased their activity level and moved downwards, concentrating in the calmer lower third of the water column. Within 4 min after the end of the wave pulse, the fish returned to their former distribution. Two- and 3-week-old fish larvae foraged close to the bottom substratum during calm periods but avoided this zone during the wave pulses. Thus, larval fish show an active response to hydrodynamic impacts. With the ability to adjust microhabitat use and activity level, already fish larvae are able to trade costs and benefits associated with spatio-temporal hydrodynamic heterogeneity. In particular, fish larvae should be able to minimize some of the harmful effects of navigation-induced waves where calmer evasion habitats are available.
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Lagasio, Martina, Antonio Parodi, Luca Pulvirenti, Agostino Meroni, Giorgio Boni, Nazzareno Pierdicca, Frank Marzano, et al. "A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast." Remote Sensing 11, no. 20 (October 15, 2019): 2387. http://dx.doi.org/10.3390/rs11202387.
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The Mediterranean region is frequently struck by severe rainfall events causing numerous casualties and several million euros of damages every year. Thus, improving the forecast accuracy is a fundamental goal to limit social and economic damages. Numerical Weather Prediction (NWP) models are currently able to produce forecasts at the km scale grid spacing but unreliable surface information and a poor knowledge of the initial state of the atmosphere may produce inaccurate simulations of weather phenomena. The STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims to investigate whether Sentinel satellites constellation weather observation data, in combination with Global Navigation Satellite System (GNSS) observations, can be used to better understand and predict with a higher spatio-temporal resolution the atmospheric phenomena resulting in severe weather events. Two heavy rainfall events that occurred in Italy in the autumn of 2017 are studied—a localized and short-lived event and a long-lived one. By assimilating a wide range of Sentinel and GNSS observations in a state-of-the-art NWP model, it is found that the forecasts benefit the most when the model is provided with information on the wind field and/or the water vapor content.
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Jahn, Markus Wilhelm, and Patrick Erik Bradley. "A Robustness Study for the Extraction of Watertight Volumetric Models from Boundary Representation Data." ISPRS International Journal of Geo-Information 11, no. 4 (March 26, 2022): 224. http://dx.doi.org/10.3390/ijgi11040224.
Full textAbstract:
Geometrically induced topology plays a major role in applications such as simulations, navigation, spatial or spatio-temporal analysis and many more. This article computes geometrically induced topology useful for such applications and extends previous results by presenting the unpublished used algorithms to find inner disjoint (d+1)-dimensional simplicial complexes from a set of intersecting d-dimensional simplicial complexes which partly shape their B-Reps (Boundary Representations). CityGML has been chosen as the input data format for evaluation purposes. In this case, the input data consist of planar segment complexes whose triangulated polygons serve as the set of input triangle complexes for the computation of the tetrahedral model. The creation of the volumetric model and the computation of its geometrically induced topology is partly parallelized by decomposing the input data into smaller pices. A robustness analysis of the implementations is given by varying the angular precision and the positional precision of the epsilon heuristic inaccuracy model. The results are analysed spatially and topologically, summarised and presented. It turns out that one can extract most, but not all, volumes and that the numerical issues of computational geometry produce failures as well as a variety of outcomes.