Geoinformatica最新文献

{"title":"Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs","authors":"R. Yazdan, M. Varshosaz, S. Pirasteh, F. Remondino","doi":"10.1139/GEOMAT-2020-0010","DOIUrl":"https://doi.org/10.1139/GEOMAT-2020-0010","url":null,"abstract":"Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"1 1","pages":"1-23"},"PeriodicalIF":2.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48350066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to the special issue on smart transportation","authors":"Bo Xu, Gautam S. Thakur","doi":"10.1007/s10707-021-00432-3","DOIUrl":"https://doi.org/10.1007/s10707-021-00432-3","url":null,"abstract":"","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"25 1","pages":"417 - 418"},"PeriodicalIF":2.0,"publicationDate":"2021-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10707-021-00432-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48718327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed mining of convoys in large scale datasets","authors":"F. Orakzai, T. Pedersen, T. Calders","doi":"10.1007/s10707-020-00431-w","DOIUrl":"https://doi.org/10.1007/s10707-020-00431-w","url":null,"abstract":"","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"25 1","pages":"353 - 396"},"PeriodicalIF":2.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10707-020-00431-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48839488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From reanalysis to satellite observations: gap-filling with imbalanced learning","authors":"Jingze Lu, Kaijun Ren, Xiaoyong Li, Yanlai Zhao, Zichen Xu, Xiaoli Ren","doi":"10.1007/s10707-020-00426-7","DOIUrl":"https://doi.org/10.1007/s10707-020-00426-7","url":null,"abstract":"","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"26 1","pages":"397-428"},"PeriodicalIF":2.0,"publicationDate":"2021-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10707-020-00426-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42681740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a semantic indoor trajectory model: application to museum visits.","authors":"Alexandros Kontarinis,&nbsp;Karine Zeitouni,&nbsp;Claudia Marinica,&nbsp;Dan Vodislav,&nbsp;Dimitris Kotzinos","doi":"10.1007/s10707-020-00430-x","DOIUrl":"https://doi.org/10.1007/s10707-020-00430-x","url":null,"abstract":"<p><p>In this paper we present a new conceptual model of trajectories, which accounts for semantic and indoor space information and supports the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we combine aspects of state-of-the-art semantic outdoor trajectory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC's IndoorGML standard. We drive the discussion on modeling issues that have been overlooked so far and illustrate them with a real-world case study concerning the Louvre Museum, in an effort to provide a pragmatic view of what the proposed model represents and how. We also present experimental results based on Louvre's visiting data showcasing how state-of-the-art mining algorithms can be applied on trajectory data represented according to the proposed model, and outline their advantages and limitations. Finally, we provide a formal outline of a new sequential pattern mining algorithm and how it can be used for extracting interesting trajectory patterns.</p>","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"25 2","pages":"311-352"},"PeriodicalIF":2.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10707-020-00430-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25451054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finding the most navigable path in road networks","authors":"R. Kaur, Vikram Goyal, Venkata M. V. Gunturi","doi":"10.1007/s10707-020-00428-5","DOIUrl":"https://doi.org/10.1007/s10707-020-00428-5","url":null,"abstract":"","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"25 1","pages":"207-240"},"PeriodicalIF":2.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10707-020-00428-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45897242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hidden Markov map matching based on trajectory segmentation with heading homogeneity","authors":"Ge Cui, Wentao Bian, Xin Wang","doi":"10.1007/s10707-020-00429-4","DOIUrl":"https://doi.org/10.1007/s10707-020-00429-4","url":null,"abstract":"","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"25 1","pages":"179-206"},"PeriodicalIF":2.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10707-020-00429-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43716540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal event sequence discovery without thresholds.","authors":"Berkay Aydin,&nbsp;Soukaina Filali Boubrahimi,&nbsp;Ahmet Kucuk,&nbsp;Bita Nezamdoust,&nbsp;Rafal A Angryk","doi":"10.1007/s10707-020-00427-6","DOIUrl":"https://doi.org/10.1007/s10707-020-00427-6","url":null,"abstract":"<p><p>Spatiotemporal event sequences (STESs) are the ordered series of event types whose instances frequently follow each other in time and are located close-by. An STES is a spatiotemporal frequent pattern type, which is discovered from moving region objects whose polygon-based locations continiously evolve over time. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. The quality of the discovered sequences is of great importance to the domain experts who use these algorithms. We introduce a novel algorithm to find the most relevant STESs without threshold values. We tested the relevance and performance of our threshold-free algorithm with a case study on solar event metadata, and compared the results with the previous STES mining algorithms.</p>","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"25 1","pages":"149-177"},"PeriodicalIF":2.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10707-020-00427-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38606797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Query the trajectory based on the precise track: a Bloom filter-based approach.","authors":"Zengjie Wang,&nbsp;Wen Luo,&nbsp;Linwang Yuan,&nbsp;Hong Gao,&nbsp;Fan Wu,&nbsp;Xu Hu,&nbsp;Zhaoyuan Yu","doi":"10.1007/s10707-021-00433-2","DOIUrl":"https://doi.org/10.1007/s10707-021-00433-2","url":null,"abstract":"<p><p>Fast and precise querying in a given set of trajectory points is an important issue of trajectory query. Typically, there are massive trajectory data in the database, yet the query sets only have a few points, which is a challenge for the superior performance of trajectory querying. The current trajectory query methods commonly use the tree-based index structure and the signature-based method to classify, simplify, and filter the trajectory to improve the performance. However, the unstructured essence and the spatiotemporal heterogeneity of the trajectory-sequence lead these methods to a high degree of spatial overlap, frequent I/O, and high memory occupation. Thus, they are not suitable for the time-critical tasks of trajectory big data. In this paper, a query method of trajectory is developed on the Bloom Filter. Based on the gridded space and geocoding, the spatial trajectory sequences (tracks) query is transformed into the query of the text string. The geospace was regularly divided by the geographic grid, and each cell was assigned an independent geocode, converting the high-dimensional irregular space trajectory query into a one-dimensional string query. The point in each cell is regarded as a signature, which forms a mapping to the bit-array of the Bloom Filter. This conversion effectively eliminates the high degree of overlap and instability of query performance. Meanwhile, the independent coding ensures the uniqueness of the whole tracks. In this method, there is no need for additional I/O on the raw trajectory data when the track is queried. Compared to the original data, the memory occupied by this method is negligible. Based on Beijing Taxi and Shenzhen bus trajectory data, an experiment using this method was constructed, and random queries under a variety of conditions boundaries were constructed. The results verified that the performance and stability of our method, compared to R*tree index, have been improved by 2000 to 4000 times, based on one million to tens of millions of trajectory data. And the Bloom Filter-based query method is hardly affected by grid size, original data size, and length of tracks. With such a time advantage, our method is suitable for time-critical spatial computation tasks, such as anti-terrorism, public safety, epidemic prevention, and control, etc.</p>","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"25 2","pages":"397-416"},"PeriodicalIF":2.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10707-021-00433-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25500531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Susceptibilité aux glissements de terrain dans la ville d’Al Hoceima et sa périphérie : application de la méthode de la théorie de l’évidence","authors":"Taoufik Byou, K. Obda, A. Taous, Ilias Obda","doi":"10.1139/geomat-2019-0025","DOIUrl":"https://doi.org/10.1139/geomat-2019-0025","url":null,"abstract":"Le Rif Marocain en général et la ville d’Al Hoceima et sa périphérie urbaine, plus particulièrement, connaissent fréquemment des aléas géomorphologiques, notamment les glissements de terrain qui entravent la gestion urbaine. Ce type d’aléa naturel est de grande actualité, aussi bien sur le plan scientifique que sur le plan médiatique, à cause de l’augmentation de la vulnérabilité, en raison de circonstances de changements globaux (réchauffements climatiques) et à la forte urbanisation, souvent irrationnelle. L’objectif de cet article est la mise en place d’une approche objective visant l’évaluation de la susceptibilité aux glissements de terrain dans la ville d’Al Hoceima et sa périphérie. La théorie de l’évidence, qui est une méthode probabiliste bivariée, est fondée sur les règles de Bayes qui consistent à calculer la probabilité d’occurrence spatiale de glissements de terrain, en se basant sur la notion de probabilité à priori et de probabilité à posteriori, tout en considérant les glissements de terrain comme variable à modéliser et chaque facteur causatif comme variable prédictive. Le but de ce travail est de procéder à un zonage d’aléa glissement de terrain tout en assurant une bonne prédiction de ce phénomène avec une bonne résolution spatiale. Les résultats de la courbe de ROC (receiver operating characteristic) montre que la confrontation de la carte de susceptibilité, des glissements de terrain à la carte d’inventaire, permet une capacité de prédiction considérable (area under curve, AUC = 0,889). Ceci pousse au constat selon lequel, plus de deux tiers des glissements de terrain inventoriés s’inscrivent dans des classes de susceptibilité élevée et très élevée. Ce produit cartographique peut constituer un puissant outil d’aide permettant la formulation de suggestions, dans le but d’optimiser l’évaluation du risque de glissements de terrain dans les zones exposées à ce phénomène.","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47362809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}