2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)最新文献

{"title":"Spatiotemporal vegetation dynamic patterns in a subtropical humid region of China","authors":"Bingwen Qiu, Min Feng, Ming Zhong, Zhenghong Tang, Chongcheng Chen","doi":"10.1109/ICSDM.2015.7298032","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298032","url":null,"abstract":"This paper aims to improve our knowledge of the complex vegetation-climate relationship in subtropical humid region in the context of global warming, by taking into considerations of spatio-temporal variation of both vegetation and climate change. A multi-resolution analysis (MRA) based on the wavelet transform (WT) is applied to examine the vegetation growth and its relationship with climate factors based on 250m 16-day composites MODIS vegetation EVI dataset in subtropical humid region of China over the period 2001-2010. A general greening up (68%) was observed over the period 2001-2010, as well as rather local negative trends. A trend toward global warming was also observed for the whole study region, whereas no obvious trend of precipitation is examined in most areas. Temperature generally has a positive influence on vegetation; with only very few negative EVI-temperature coefficients observed on the south portion principally due to changes in land use, land degradation, and cloud noise. However, nearly equally positive and negative EVI-rainfall relationship is observed on the inter-annual level, with negative coefficients principally observed in the northwest portion with abundant precipitation. Very strong positive relationship is observed between both EVI-temperature and EVI-precipitation at seasonal level. It is revealed that spatio-temporal variation of both vegetation and climate should be taken into considerations when analyzing the long-term effects of global climate change. Interactions between vegetation dynamics and climate variability must be studied through spatially and temporally explicit multi-scale analysis to investigate the influence of long-term climate change on the vegetation growth.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125962567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research of spatial co-location pattern mining based on segmentation threshold weight for big dataset","authors":"Minwei Tang, Zhanquan Wang","doi":"10.1109/ICSDM.2015.7298024","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298024","url":null,"abstract":"Discovering spatial co-location patterns is an important field in many scenarios such as identifying plant distribution, detecting crime, etc. In practical applications, all attributes on a geographic surface are related to each other, but closer values are more strongly related than the distant ones. The distance scale between two instances of different types and the big dataset aren't considered in the previous co-location pattern mining algorithms. A new co-location pattern mining approach is proposed based on segmentation threshold weight which is meant to measure the relation of two instances according to their Euclidean distance. This approach defines a new weight interest measurement. The new pruning technique is used to reduce the amount of candidates and the algorithm is realized in the MapReduce framework and run on the distributed platform Hadoop according to the proposed guiding strategy. The correctness, completeness and efficiency of the proposed methods are analysed and the experiments in real and synthetic dataset have been performed to prove that the algorithm is effective and feasible.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115654736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geovisualization and correlation analysis between geotagged Twitter and JMA rainfall data: Case of heavy rain disaster in Hiroshima","authors":"K. Lwin, K. Zettsu, K. Sugiura","doi":"10.1109/ICSDM.2015.7298028","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298028","url":null,"abstract":"With the development of wireless communication technology along with emergence of location-enabled mobile devices and cyber-physical social sensor networks, nowadays we can collect and store a large amount of geospatial data such as weather phenomena, human mobility, and social networking activities along with time of occurrence. Information or knowledge extraction from this so-called Big Data is a challenge to many geospatial information users due to the nature of data complexity and large data volume. In this article, we discuss handling of Big Data with GIS by utilizing 10-minute intervals of Japan Metrological Agency (JMA) rainfall data synchronized with Twitter messages by specific keywords like “rain” or/and “landslide” to analyse the relationship between environmental phenomena and social responses in heavy rain conditions in the Hiroshima region. The results were analysed and visualized through a geovisualization technique to evaluate the possible use of inputs from social media websites to the government decision-making process, especially for disaster and emergency preparedness in the near future.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116881026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating quality indices of a digital depth model for navigational safety","authors":"Lihua Zhang, Shuaidong Jia, Tao Wang, Hongbo Cao","doi":"10.1109/ICSDM.2015.7298039","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298039","url":null,"abstract":"To ensure safe ship navigation, true water depths should be deep enough with respect to the corresponding depths on a chart. Therefore, a set of basic quality indices, including an index of the probability of adequate depth, were developed to evaluate the quality of a digital depth model (DDM) with respect to navigational safety. In addition, these methods are proposed for assessing the probability of adequate depth and the percentage of acceptable probabilities, the deep-bias displacement. Theoretical analysis and experimental comparisons presented in this paper demonstrate that the defined indices can be used to evaluate quantitatively the quality of a DDM in terms of the navigational safety.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130641042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An agent-based negotiation approach for knowledge exchange in cloud service platforms","authors":"Sameh Abdalla, B. Huang, Mohand Tahar Kechadi","doi":"10.1109/ICSDM.2015.7298019","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298019","url":null,"abstract":"A number of cloud service platforms are targeting experts involved in vital decision-making activities that can lead, for example, governments to take precautions or healthcare professionals to perform specific operations. In order for these cloud platforms to operate there exist an underlying knowledge discovery engine that is employing a number of advanced data analytics techniques to derive insightful conclusions. The technical challenges facing designers and developers of such cloud architectures are increasing relatively with the sensitivity of the data in subject and the ranking of the addressees. In this paper, we investigate these challenges and examine the design approaches of these specific cloud service architectures. We propose an agent-based negotiation model for knowledge exchange among service architectures similar to those in subject.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131998205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classifying forest stands based on multi-scale structure features using Quickbird image","authors":"Wanli Huang, Hu Li, Guangfa Lin","doi":"10.1109/ICSDM.2015.7298054","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298054","url":null,"abstract":"Forest stands classification is the precondition of stand inventory and forest management. It is also the primary task and main content of the inventory and dynamic monitoring of forest resources. With the improvements of the spatial resolution of remotely sensed image and the development of GEOgraphic Object-Based Image Analysis (GEOBIA) technologies, the precision of stand division had increased. However, the current methods can only classify forest stand coarsely, and they are difficult to classify the stands with different spatial structures. In this study, three indices describing multi-scale structure features, trees crown coverage (TCC), trees cluster index (TCI) and ratio of shadows and trees (RST), were derived from high spatial resolution image by GEOBIA technology. The indices can well indicate the diversities of stand density, structure, size and differences in the spatial distribution. With Quickbird image, we tested the method in Tekesi forest farm of Xinjiang province, P. R. China. The result showed that the stands with the difference spatial distribution could be classified according to these indices. Using the method to extract stands, can improve efficiency, reduce costs, and keep consistency of the results, which are essential for dynamic monitoring and changing detection of forest resources. The methods of indices extraction and classification are also suitable for similar applications that need the structural features accounting for the density and aggregation of the objects and their shadows, such as the urban area division.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125477519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive simulation of virtual trees spray","authors":"Hui Song, Ying Xu, Zhijie Li, Ding Lin","doi":"10.1109/ICSDM.2015.7298046","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298046","url":null,"abstract":"Variable-rate tree spraying is one of the most important technique of precision agriculture. This paper presented an interactive virtual tree spraying method based on particle system, which gain fine control flexible through parameters such as the emitter position and pitch angle, atomizing particle, atomizing anger and spray penetration depth. We proposed a 2-hierarchy collision detection algorithm to identify the leaves with dropping particles, which speed up the simulation for about 90%. Then, we implemented the prototype system using VC++ to carry out the precision of the spray as needed.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121690065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Making full use of the Landsat7 SLC-Off ETM+ data for urban thermal environment monitoring","authors":"F. Chen, Song Yang, K. Yin, Xiaofeng Zhao, P. Chan","doi":"10.1109/ICSDM.2015.7298052","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298052","url":null,"abstract":"Landsat program has provided the well calibrated and relatively high spatial resolution data of the Earth's surface, since the launch of its first satellite (Landsat1) in 1972. The continuity of this program is ensured thanks to the operation of Landsat8 (launched on 11 February 2013). According to the data open policy announced in 2008, all new and archived Landsat data held by the U.S. Geological Survey are freely accessible through the internet to general users worldwide. Unfortunately, the scan-line corrector (SLC) for the Landsat7 ETM+ sensor failed permanently on 31 May 2003. The SLC-off problem limits the quantitative application of ETM+ data, and results in a period of data gap consequently. Therefore, resolving the problem of Landsat7 SLC-off ETM+ imagery is a valuable issue. At present, several approaches have been proposed. However, a few researches have been done to recover the thermal imagery of Landsat7 SLC-off ETM+, while relatively much attention has been paid to the recovering of multispectral bands. Considering the merits of thermal imagery for monitoring urban thermal environment, we evaluated the approaches to recover the thermal band of Landsat7 SLC-off ETM+. Methods in this paper are mainly two current approaches, including the adaptive window linear histogram match (AWLHM) and the spectral and temporal based method (named STM) which is a simple but effective method. Case study shows that with different band or band combination as explanatory variable(s) the modified AWLHM performs well, according to error indicators. In this paper, three error measurements are calculated, which specifically are the universal image quality index (UIQI), the relative mean absolute error (MAEr), and the relative root mean square error (RMSEr). Meanwhile, the STM method presents slight advantages over the modified AWLHM methods. Accordingly, most parts of SLC-off affected pixels are able to be recovered properly, confirming that the recovered thermal band can be applied in urban thermal environment issues. We assume that more acquisitions can provide relatively more comprehensive details about urban thermal environment. The importance of the multiple recovered SLC-off thermal imageries is obvious. Accordingly, the significance of the open policy of Landsat series is demonstrated in this paper, in view of monitoring urban thermal environment. Finally, case study indicates that the improvement over the areas with surface heterogeneity is a difficult but worthy task.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128456414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring time-dependent traffic congestion patterns from taxi trajectory data","authors":"Chengkun Liu, K. Qin, Chaogui Kang","doi":"10.1109/ICSDM.2015.7298022","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298022","url":null,"abstract":"Due to public travel choice, city function zoning and road network structure, urban traffic congestion tends to have strong spatiotemporal correlations. Unveiling the spatiotemporal patterns of urban traffic congestions will provide useful information for urban planning, traffic control, and location based service (LBS). This paper proposes an approach to identify traffic congestion regions and their spatiotemporal distribution from taxi trajectory data. Firstly, slow trajectory sequences are extracted from raw taxi trajectory data. Together with taxi engine states, these sequences are then transferred into congestion events that define the congestion duration and the average speed. Thereafter, highly congestion-prone areas are identified by clustering these congestion events using the DBSCAN clustering method. From the perspective of spatial homogeneity, global aggregation degrees of those identified congestion-prone areas are defined by the Ripley K function. Finally, considering congestions of nearby areas can influence each other and worsen the local traffic condition, the theory of data field is imposed to reveal the interactions between neighbouring congestion events. It also enables the visualization of the congestion intensity distribution from the trajectory potential of trajectory data field. The proposed method is validated by a case study of taxi trajectory data analysis in Wuhan City, China.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127114549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D-PatchMatch: An optimization algorithm for point cloud completion","authors":"Zhipeng Cai, Cheng Wang, Chenglu Wen, Jonathan Li","doi":"10.1109/ICSDM.2015.7298044","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298044","url":null,"abstract":"Point cloud completion is an indispensible process for handling the occlusion problem occurring during the data acquisition. For completion, 3D point clouds generate a much larger searching space than 2D images while searching for the best match for boundary patches. To handle the searching speed bottleneck, this paper proposed a novel optimization algorithm which is called the 3D-PatchMatch algorithm for 3D point cloud completion. Inspired by the nature coherence of the point cloud data, the proposed algorithm use a random initialization process and a propagation process to reduce the convergence time. Furthermore, a random guess process is proposed to make sure the proposed algorithm has the ability of escaping from the local minima. Experimental results show significant time reduction rate for this method compared to brute-force search. Furthermore, the completion results with comparisons indicate that the proposed algorithm is rarely trapped in the local minima.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121956826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}