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I am a Professor in Computer Science and Research Chair of the French Naval Academy. I was previously head of the GIS research group at the Naval Academy Research Institute, Senior Lecturer in Computing at the Nottingham Trent University, Associate Lecturer at the Open University in the UK, Senior Researcher at the Swiss Federal Institute of Technology, and consultant for several GIS companies and international GIS programs. My research is oriented towards theoretical and pluri-disciplinary aspects of geographical information science (spatio-temporal models and theories, computational models of space, GIS applications to urban, environmental and urban spaces). I am an associate editor of the International Journal of Geographical and Information Science (IJGIS) and regularly serve on the editorial boards of several GIS journals (CEUS, JOSIS, JLBS, IJAIS, GIS, IJAEIS) and conferences/ workshops (COSIT, SeCoGIS, ACMGIS, SC, SDH, WEBIST, STAMI and ISA).
This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. While the integration of time within GIS has long been a domain of major interest, alternative modelling and data manipulation approaches derived from graph and knowledge-based principles provide many opportunities for many application domains. We first survey graph principles and how they have been applied to GIS and a few representative domains to date. A comprehensive analysis of the principles behind K-graphs, respective data representation and manipulation capabilities is discussed. The perspectives offered by a close integration of ST-graphs and K-graphs are explored. The whole approach is illustrated and discussed in the context of maritime transportation.
Géraldine Del Mondo; Peng Peng; Jérôme Gensel; Christophe Claramunt; Feng Lu. Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation. ISPRS International Journal of Geo-Information 2021, 10, 541 .
AMA StyleGéraldine Del Mondo, Peng Peng, Jérôme Gensel, Christophe Claramunt, Feng Lu. Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation. ISPRS International Journal of Geo-Information. 2021; 10 (8):541.
Chicago/Turabian StyleGéraldine Del Mondo; Peng Peng; Jérôme Gensel; Christophe Claramunt; Feng Lu. 2021. "Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation." ISPRS International Journal of Geo-Information 10, no. 8: 541.
An urban landscape can be considered as a background environment that influences humans’ movements at various scales in the city. This research is oriented to the study of the interactions between urban forest patches and their degree of influence and attractions on humans’ behaviors and interactions. The objective is to evaluate the relations between individuals’ movements and the city space nearby natural landscapes, and also to question spatial practices in the city. Forest patches are modelled according to a structural approach at the city level, while Space syntax principles have been applied and compared to in situ movements as experimentally observed. A statistical analysis complements the configurational analysis by highlighting correlations between structural properties and human movements. The whole approach is applied to the Bir El Bey Forest of the Tunisian city of Hammam Chatt in order to explore the interaction between the built and natural landscapes at different levels of scale. The findings exhibit the respective effects of the urban network and natural landscape on the urban space, and how such spaces are appropriated by Hammam Chatt inhabitants and users. Finally, the results propose a generic framework analysis for the study of the relations between humans and urban structure and landscape preferences and that offers novel perspectives for urban planning.
Asma Rejeb Bouzgarrou; Yasmine Attia Ben Cherifa; Christophe Claramunt; Hichem Rejeb. Urban Connectivity: Elements for an Identification of Bir El Bey’s Preferential Landscapes. Urban Science 2021, 5, 55 .
AMA StyleAsma Rejeb Bouzgarrou, Yasmine Attia Ben Cherifa, Christophe Claramunt, Hichem Rejeb. Urban Connectivity: Elements for an Identification of Bir El Bey’s Preferential Landscapes. Urban Science. 2021; 5 (3):55.
Chicago/Turabian StyleAsma Rejeb Bouzgarrou; Yasmine Attia Ben Cherifa; Christophe Claramunt; Hichem Rejeb. 2021. "Urban Connectivity: Elements for an Identification of Bir El Bey’s Preferential Landscapes." Urban Science 5, no. 3: 55.
The successful implementation of Vessel Traffic Services (VTS) relies heavily on human decisions. With the increasing development of maritime traffic, there is an urgent need to provide a sound support for dynamic risk appraisals and decision support. This research introduces a cellular automata (CA) simulation-based modelling approach the objective of which is to analyze and evaluate real-time maritime traffic risks in port environments. The first component is the design of a CA model to monitor ships’ behavior and maritime fairway traffic. The second component is the refinement of the modelling approach by combining a cloud model with expert knowledge. The third component establishes a risk assessment model based on a fuzzy comprehensive evaluation. A typical scenario was experimentally implemented to validate the model’s efficiency and operationality.
Yongfeng Suo; Zhihong Sun; Christophe Claramunt; Shenhua Yang; Zhibing Zhang. A Dynamic Risk Appraisal Model and Its Application in VTS Based on a Cellular Automata Simulation Prediction. Sensors 2021, 21, 4741 .
AMA StyleYongfeng Suo, Zhihong Sun, Christophe Claramunt, Shenhua Yang, Zhibing Zhang. A Dynamic Risk Appraisal Model and Its Application in VTS Based on a Cellular Automata Simulation Prediction. Sensors. 2021; 21 (14):4741.
Chicago/Turabian StyleYongfeng Suo; Zhihong Sun; Christophe Claramunt; Shenhua Yang; Zhibing Zhang. 2021. "A Dynamic Risk Appraisal Model and Its Application in VTS Based on a Cellular Automata Simulation Prediction." Sensors 21, no. 14: 4741.
Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.
Gang Tang; Congqiang Tang; Hao Zhou; Christophe Claramunt; Shaoyang Men. R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition. Remote Sensing 2021, 13, 1525 .
AMA StyleGang Tang, Congqiang Tang, Hao Zhou, Christophe Claramunt, Shaoyang Men. R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition. Remote Sensing. 2021; 13 (8):1525.
Chicago/Turabian StyleGang Tang; Congqiang Tang; Hao Zhou; Christophe Claramunt; Shaoyang Men. 2021. "R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition." Remote Sensing 13, no. 8: 1525.
This paper introduces a simplex-based enriched graph data model integrating a discrete and place-based indoor spatial model with a spatial-social network. The proposed model incorporates similarity and relevance measures, exhibited from Q-analysis of simplicial complexes, facilitating data manipulation and revealing latent relations in a spatial-social network. It also uses an indoor-specific metric representing the ease of access to process spatial-social queries in indoor environments. The proposed model’s experimental implementation shows the quantitative advantage of using graph-based representation and the qualitative superiority of simplex-based enrichment in processing spatial-social queries in indoor environments.
Mahdi Rahimi; Mohammad Reza Malek; Christophe Claramunt; Thierry Le Pors. A topology-based graph data model for indoor spatial-social networking. International Journal of Geographical Information Science 2021, 1 -23.
AMA StyleMahdi Rahimi, Mohammad Reza Malek, Christophe Claramunt, Thierry Le Pors. A topology-based graph data model for indoor spatial-social networking. International Journal of Geographical Information Science. 2021; ():1-23.
Chicago/Turabian StyleMahdi Rahimi; Mohammad Reza Malek; Christophe Claramunt; Thierry Le Pors. 2021. "A topology-based graph data model for indoor spatial-social networking." International Journal of Geographical Information Science , no. : 1-23.
This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions P(x, y) and W(x, y) and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm.
Gang Tang; Congqiang Tang; Christophe Claramunt; Xiong Hu; Peipei Zhou. Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment. IEEE Access 2021, 9, 59196 -59210.
AMA StyleGang Tang, Congqiang Tang, Christophe Claramunt, Xiong Hu, Peipei Zhou. Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment. IEEE Access. 2021; 9 (99):59196-59210.
Chicago/Turabian StyleGang Tang; Congqiang Tang; Christophe Claramunt; Xiong Hu; Peipei Zhou. 2021. "Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment." IEEE Access 9, no. 99: 59196-59210.
The continuous development of positioning technologies and computing solutions for the integration of large trajectory data sets offers many novel research opportunities. Among various research domains, the extraction of users' movement patterns is an important issue that is yet to be addressed. While many previous studies have analyzed human and animal movements from a predominantly geometrical point of view, additional semantics are still required to provide a better understanding of the patterns that emerge. User activity data provide important information resources to analyze and predict movement patterns in urban environments. This study introduces a computational framework that combines the geometric and activity‐based dimensions of human trajectories. First, the geometrical dimension considers a series of parameters (i.e., turning points, curvature, and self‐intersection) that are extracted by a convex‐hull algorithm and characterizes a given trajectory. Second, user activity transitions are modeled and then denote some recurrent patterns. Finally, geometric and activity patterns are integrated into a unified trajectory modeling framework. This favors the analysis of human movement patterns by taking into account the geometric and activity dimensions. The entire approach and framework have experimented with the LifeMap Korean trajectory data set commonly considered as a reference benchmark. The experiments showed how the integration of geometrical and activity‐based dimensions could provide a better understanding of the patterns and trends that emerge from a large trajectory data set.
Amin Hosseinpoor Milaghardan; Rahim Ali Abbaspour; Christophe Claramunt; Alireza Chehreghan. An activity‐based framework for detecting human movement patterns in an urban environment. Transactions in GIS 2021, 1 .
AMA StyleAmin Hosseinpoor Milaghardan, Rahim Ali Abbaspour, Christophe Claramunt, Alireza Chehreghan. An activity‐based framework for detecting human movement patterns in an urban environment. Transactions in GIS. 2021; ():1.
Chicago/Turabian StyleAmin Hosseinpoor Milaghardan; Rahim Ali Abbaspour; Christophe Claramunt; Alireza Chehreghan. 2021. "An activity‐based framework for detecting human movement patterns in an urban environment." Transactions in GIS , no. : 1.
High-resolution images provided by synthetic aperture radar (SAR) play an increasingly important role in the field of ship detection. Numerous algorithms have been so far proposed and relative competitive results have been achieved in detecting different targets. However, ship detection using SAR images is still challenging because these images are still affected by different degrees of noise while inshore ships are affected by shore image contrasts. To solve these problems, this paper introduces a ship detection method called N-YOLO, which based on You Only Look Once (YOLO). The N-YOLO includes a noise level classifier (NLC), a SAR target potential area extraction module (STPAE) and a YOLOv5-based detection module. First, NLC derives and classifies the noise level of SAR images. Secondly, the STPAE module is composed by a CA-CFAR and expansion operation, which is used to extract the complete region of potential targets. Thirdly, the YOLOv5-based detection module combines the potential target area with the original image to get a new image. To evaluate the effectiveness of the N-YOLO, experiments are conducted using a reference GaoFen-3 dataset. The detection results show that competitive performance has been achieved by N-YOLO in comparison with several CNN-based algorithms.
Gang Tang; Yichao Zhuge; Christophe Claramunt; Shaoyang Men. N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction. Remote Sensing 2021, 13, 871 .
AMA StyleGang Tang, Yichao Zhuge, Christophe Claramunt, Shaoyang Men. N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction. Remote Sensing. 2021; 13 (5):871.
Chicago/Turabian StyleGang Tang; Yichao Zhuge; Christophe Claramunt; Shaoyang Men. 2021. "N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction." Remote Sensing 13, no. 5: 871.
Nowadays, gathering information about commercial products can be performed either online or offline. While online searches can be virtually undertaken through online shopping websites, offline searches should be done physically at stores. However, there is a specific emerging trend where users can check some product opportunities online before getting to the stores and then possibly buying some items whose properties have already been evaluated over the Web. Product properties can be studied online while on site evaluation provides a direct contact with these goods at the stores. The objective of the approach developed in this paper is to discover user preferences when searching and exploring online shopping websites and then recommend the stores that better match their interests. First, users’ internet behaviours are extracted from an online shopping website. Secondly, a Voronoi high-dimensional structure supports the derivation of similarities between the users and stores. Third, a distance matrix between the user and the selected stores is generated. Finally, a ranked list of the most appropriate stores is provided to the users based on their product interest and their locations. The whole approach has been successfully tested by a panel of 30 volunteers in the 6th District of the city of Tehran.
Goshtasb Shahriari-Mehr; Mahmoud Reza Delavar; Christophe Claramunt; Babak Nadjar Araabi; Mohammad-Reza A. Dehaqani. A store location-based recommender system using user’s position and web searches. Journal of Location Based Services 2021, 15, 118 -141.
AMA StyleGoshtasb Shahriari-Mehr, Mahmoud Reza Delavar, Christophe Claramunt, Babak Nadjar Araabi, Mohammad-Reza A. Dehaqani. A store location-based recommender system using user’s position and web searches. Journal of Location Based Services. 2021; 15 (2):118-141.
Chicago/Turabian StyleGoshtasb Shahriari-Mehr; Mahmoud Reza Delavar; Christophe Claramunt; Babak Nadjar Araabi; Mohammad-Reza A. Dehaqani. 2021. "A store location-based recommender system using user’s position and web searches." Journal of Location Based Services 15, no. 2: 118-141.
Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a region of interest from input images. In order to efficiently match ship candidates, the principle of our approach is to distinguish suspected areas from the images based on hue, saturation, value (HSV) differences between ships and the background. The whole approach is the basis of an experiment with a large ship dataset, consisting of Google Earth images and HRSC2016 datasets. The experiment shows that the H-YOLO network, which uses the same weight training from a set of remote sensing images, has a 19.01% higher recognition rate and a 16.19% higher accuracy than applying the you only look once (YOLO) network alone. After image preprocessing, the value of the intersection over union (IoU) is also greatly improved.
Gang Tang; Shibo Liu; Iwao Fujino; Christophe Claramunt; Yide Wang; Shaoyang Men. H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network. Remote Sensing 2020, 12, 4192 .
AMA StyleGang Tang, Shibo Liu, Iwao Fujino, Christophe Claramunt, Yide Wang, Shaoyang Men. H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network. Remote Sensing. 2020; 12 (24):4192.
Chicago/Turabian StyleGang Tang; Shibo Liu; Iwao Fujino; Christophe Claramunt; Yide Wang; Shaoyang Men. 2020. "H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network." Remote Sensing 12, no. 24: 4192.
Deep learning provides appropriate mechanisms to predict vessel trajectories for safer and efficient shipping, but still existing models are mainly oriented to longer-term prediction trends and do not fully support real time navigation needs. While most recent works have been largely exploiting Automatic Identification System (AIS), the complete semantics of these data haven’t so far fully exploited. The research presented in this paper introduced an extended sequence-to-sequence model using AIS data. A Gated Recurrent Unit (GRU) network encodes historical spatio-temporal sequences as a context vector, which not only preserves the sequential relationships among trajectory locations, but also alleviates the gradient descent problem. The GRU network acts as a decoder, outputting target trajectory location sequences. Real AIS data from the Chongqing and Wuhan sections of the Yangzi River were selected as typical experimental areas for evaluation purposes. The proposed ST-Seq2Seq model has been tested against the LSTM-RNN and GRU-RNN baseline models for short term trajectory prediction experiments. A 10-minute historical trajectory sequence was used to predict the trajectory sequence for the next five minutes. Overall, the findings show that LSTM and GRU networks, while applying a recursive method to predict a sequence of continuous trajectory points, when the number of predicted trajectory points increases accuracy decreases. Conversely, the extended sequence-to-sequence model shows satisfactory stability on different ship channels.
Lan You; Siyu Xiao; Qingxi Peng; Christophe Claramunt; Xuewei Han; Zhengyi Guan; Jiahe Zhang. ST-Seq2Seq: A Spatio-Temporal Feature-Optimized Seq2Seq Model for Short-Term Vessel Trajectory Prediction. IEEE Access 2020, 8, 218565 -218574.
AMA StyleLan You, Siyu Xiao, Qingxi Peng, Christophe Claramunt, Xuewei Han, Zhengyi Guan, Jiahe Zhang. ST-Seq2Seq: A Spatio-Temporal Feature-Optimized Seq2Seq Model for Short-Term Vessel Trajectory Prediction. IEEE Access. 2020; 8 (99):218565-218574.
Chicago/Turabian StyleLan You; Siyu Xiao; Qingxi Peng; Christophe Claramunt; Xuewei Han; Zhengyi Guan; Jiahe Zhang. 2020. "ST-Seq2Seq: A Spatio-Temporal Feature-Optimized Seq2Seq Model for Short-Term Vessel Trajectory Prediction." IEEE Access 8, no. 99: 218565-218574.
Land change models are amongst the most widely developed tools for spatial decision support. Despite this progress, only a few models have been created thus far that simulate urban growth that incorporate two important aspects of uncertainty inherent to land use dynamics: fuzziness and roughness. Combining fuzziness and roughness into models will enhance the use of these tools for decision support. This study applied and evaluated a fuzzy-based approach to the feature selection effects on the accuracy of a land change model. Fuzzy rough set theory (FRST) was employed here as feature selection method and was integrated with a support vector regression (SVR) algorithm to simulate urban growth of Tabriz mega city in northwest Iran. In order to apply feature selection to a FRST algorithm, incoming data has been first fuzzified by an adaptive neural fuzzy inference system (ANFIS). To evaluate the application of FRST, SVR was used with and without FRST (SVR and SVR-FRST), while for performance evaluation logistic regression (LR) and kernelled LR (KLR) models were integrated with and without FRST (LR, LR-FRST, KLR, and KLR-FRST). The accuracy of the simulated maps of all models were evaluated by calculating the overall accuracy (OA), true positive rate (TPR), true negative rate (TNR), total operating characteristic (TOC) and their area under curve (AUC). The results showed that integrating FRST with the above-mentioned models enhanced the overall performances based on the above criteria. Among the above mentioned models, SVM-FRST and KLR-FRST yielded the best goodness of fit measures. Moreover, SVM-FRST with 83.6% OA, 41.6% TPR, and 90.4% TNR performs better than KLR-FRST with 82.4% OA, 37.4% TPR, and 89.8% TNR. However, KLR-FRST has more AUC, less green area destruction, more barren to urban areas conversion, and fast tuning process related to SVR-FRST. Finally, we suggest that KLR-FRST and SVR-FRST are, among those evaluated, the most appropriate models for urban growth modelling of the Tabriz mega city of Iran when considering uncertainty.
D. Parvinnezhad; M. R. Delavar; B. C. Pijanowski; C. Claramunt. Integration of adaptive neural fuzzy inference system and fuzzy rough set theory with support vector regression to urban growth modelling. Earth Science Informatics 2020, 14, 17 -36.
AMA StyleD. Parvinnezhad, M. R. Delavar, B. C. Pijanowski, C. Claramunt. Integration of adaptive neural fuzzy inference system and fuzzy rough set theory with support vector regression to urban growth modelling. Earth Science Informatics. 2020; 14 (1):17-36.
Chicago/Turabian StyleD. Parvinnezhad; M. R. Delavar; B. C. Pijanowski; C. Claramunt. 2020. "Integration of adaptive neural fuzzy inference system and fuzzy rough set theory with support vector regression to urban growth modelling." Earth Science Informatics 14, no. 1: 17-36.
This research introduces a spatio-temporal planning framework whose objective is to simulate a sailing yacht match race. The race is a duel in which strategy and tactics play a major role as sailors continuously have to take decisions according to wind variations and opponent’s locations and actions. We introduce a decision-aid framework based on a stochastic game approach grounded on an action-oriented model that replicates yachts’ behaviors. The objective is to replicate as closely as possible the respective behaviors and navigation decisions taken by yachts competitors. The proposed formalism has been implemented and is illustrated by a sample race example .
Lamia Belaouer; Mathieu Boussard; Patrick Bot; Christophe Claramunt. A Non-cooperative Game Approach for the Dynamic Modeling of a Sailing Match Race. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 197 -213.
AMA StyleLamia Belaouer, Mathieu Boussard, Patrick Bot, Christophe Claramunt. A Non-cooperative Game Approach for the Dynamic Modeling of a Sailing Match Race. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():197-213.
Chicago/Turabian StyleLamia Belaouer; Mathieu Boussard; Patrick Bot; Christophe Claramunt. 2020. "A Non-cooperative Game Approach for the Dynamic Modeling of a Sailing Match Race." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 197-213.
The migrant caravan that recently came out from Central towards North America generated polarized opinions in online social networks. The objective of this paper is to explore the social spatial-temporal trends that emerge from this migrant caravan phenomenon, and based on a combination of social media and newspaper opinions and reports, together with additional socio-economic data. The framework combines text data mining, text clustering, sentiment analysis and spatiotemporal data exploration. The study reveals significant ethnic polarization and ideological patterns but noticeable regional differences in rural and urban areas. The experimental study shows that our approach provides a valuable experimental framework to study emerging regional phenomena as they appear from social media.
Roberto Zagal-Flores; Miguel Felix Mata; Christophe Claramunt. A Social-Spatial Data Approach for Analyzing the Migrant Caravan Phenomenon. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 156 -165.
AMA StyleRoberto Zagal-Flores, Miguel Felix Mata, Christophe Claramunt. A Social-Spatial Data Approach for Analyzing the Migrant Caravan Phenomenon. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():156-165.
Chicago/Turabian StyleRoberto Zagal-Flores; Miguel Felix Mata; Christophe Claramunt. 2020. "A Social-Spatial Data Approach for Analyzing the Migrant Caravan Phenomenon." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 156-165.
Younes Hamdani; Rémy Thibaud; Christophe Claramunt. A hybrid data model for dynamic GIS: application to marine geomorphological dynamics. International Journal of Geographical Information Science 2020, 1 -25.
AMA StyleYounes Hamdani, Rémy Thibaud, Christophe Claramunt. A hybrid data model for dynamic GIS: application to marine geomorphological dynamics. International Journal of Geographical Information Science. 2020; ():1-25.
Chicago/Turabian StyleYounes Hamdani; Rémy Thibaud; Christophe Claramunt. 2020. "A hybrid data model for dynamic GIS: application to marine geomorphological dynamics." International Journal of Geographical Information Science , no. : 1-25.
Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship’s trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.
Yongfeng Suo; Wenke Chen; Christophe Claramunt; Shenhua Yang. A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network. Sensors 2020, 20, 5133 .
AMA StyleYongfeng Suo, Wenke Chen, Christophe Claramunt, Shenhua Yang. A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network. Sensors. 2020; 20 (18):5133.
Chicago/Turabian StyleYongfeng Suo; Wenke Chen; Christophe Claramunt; Shenhua Yang. 2020. "A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network." Sensors 20, no. 18: 5133.
In many situations, the trajectory of an unmanned aerial vehicle (UAV) is very likely to deviate from the initial path generated by a path planning algorithm. This is in fact due to the existence of dynamic constraints of the UAV. In order to reduce the degree of such a deviation, this research introduces a trajectory planning algorithm, the objective of which is to minimize distance while maintaining security. The algorithm first develops preprocess trajectory points by constructing isosceles triangles then, on the basis of a minimum snap trajectory method, it applies a corridor constraint to an optimization objective function, while the deviation evaluation function is established to quantitatively evaluate the deviation distance. A series of experiments were carried out in a simulation environment with a simplified quay crane model. The results show that the proposed method not only optimizes the time and length of the generated trajectory, but also reduces the average deviation distance by 88.7%. Moreover, the generated trajectory can be well tracked by the UAV through qualitative and quantitative analysis. Overall, the experiments show that the proposed method can generate a higher UAV trajectory quality.
Gang Tang; Zhipeng Hou; Christophe Claramunt; Xiong Hu. UAV Trajectory Planning in a Port Environment. Journal of Marine Science and Engineering 2020, 8, 592 .
AMA StyleGang Tang, Zhipeng Hou, Christophe Claramunt, Xiong Hu. UAV Trajectory Planning in a Port Environment. Journal of Marine Science and Engineering. 2020; 8 (8):592.
Chicago/Turabian StyleGang Tang; Zhipeng Hou; Christophe Claramunt; Xiong Hu. 2020. "UAV Trajectory Planning in a Port Environment." Journal of Marine Science and Engineering 8, no. 8: 592.
A landscape generally consists of both natural and human-made elements of a land area. Human representations of landscapes can cover a wide range of expressions, from figurative paintings to photography and natural language expressions. However, the way humans perceive, describe, and value a given landscape is still a difficult task to address. The research developed in this paper introduces a volunteered geographic information (VGI) framework whose objective is to provide an interface for an interactive description of a given landscape. A series of qualitative and quantitative measures/metrics are developed to support such descriptions; they encompass several dimensions such as salience, topological and direction relations, solid angle, and distance. The framework is evaluated by a series of experiments that determine the respective roles of each metric aiming to describe a landscape in the VGI environment.
Samira Soleimani; Mohammad Reza Malek; Christophe Claramunt. Qualitative and quantitative metrics for the visual description of landscapes in a volunteered geographical information environment. Arabian Journal of Geosciences 2020, 13, 1 -12.
AMA StyleSamira Soleimani, Mohammad Reza Malek, Christophe Claramunt. Qualitative and quantitative metrics for the visual description of landscapes in a volunteered geographical information environment. Arabian Journal of Geosciences. 2020; 13 (14):1-12.
Chicago/Turabian StyleSamira Soleimani; Mohammad Reza Malek; Christophe Claramunt. 2020. "Qualitative and quantitative metrics for the visual description of landscapes in a volunteered geographical information environment." Arabian Journal of Geosciences 13, no. 14: 1-12.
The recent years have witnessed a greater demand for understanding how people move in urban environments. Due to the widespread usage of mobile phones, there have been several trajectory-based studies focusing on extracting the characteristics of human mobility from georeferenced mobile phone data. Mobile positioning data is generally generated as scattered points in CDRs (Call Detail Records). Even though CDR data can be regarded as an inexpensive scalable source of information on human mobility, mobility studies in urban settings based on such data sources still prove to be a research challenge due to the coarseness of CDR spatial granularity. Motivated by the need for transforming large-scale CDRs to movement trajectories, the present study offers a new solution which is made of two principal building blocks: (1) Developing a Bayesian-based induction method through adopting a GIS-based wave propagation model to solve the GSM-based localization problem when methods such as triangulation are not applicable due to the lack of measurements from more than one base station; (2) Reconstruction of movement trajectories from cellular location information using overlapping relations existing between observed cells as well as detection of ping-pong phenomena as auxiliary information. A case study employing CDR and GPS records obtained from an experimental survey on one of the central urban zones of Tehran was conducted, which showed the effectiveness of the proposed methodology in comparison to current approaches with respect to three perspectives, including movement path exploration, individual-oriented movement features extraction, and crowd-movement modelling.
Mohammad Forghani; Farid Karimipour; Christophe Claramunt. From cellular positioning data to trajectories: Steps towards a more accurate mobility exploration. Transportation Research Part C: Emerging Technologies 2020, 117, 102666 .
AMA StyleMohammad Forghani, Farid Karimipour, Christophe Claramunt. From cellular positioning data to trajectories: Steps towards a more accurate mobility exploration. Transportation Research Part C: Emerging Technologies. 2020; 117 ():102666.
Chicago/Turabian StyleMohammad Forghani; Farid Karimipour; Christophe Claramunt. 2020. "From cellular positioning data to trajectories: Steps towards a more accurate mobility exploration." Transportation Research Part C: Emerging Technologies 117, no. : 102666.
Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.
Zhihuan Wang; Mengyuan Yao; Chenguang Meng; Christophe Claramunt. Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data. ISPRS International Journal of Geo-Information 2020, 9, 351 .
AMA StyleZhihuan Wang, Mengyuan Yao, Chenguang Meng, Christophe Claramunt. Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data. ISPRS International Journal of Geo-Information. 2020; 9 (6):351.
Chicago/Turabian StyleZhihuan Wang; Mengyuan Yao; Chenguang Meng; Christophe Claramunt. 2020. "Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data." ISPRS International Journal of Geo-Information 9, no. 6: 351.