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  • Cartography and geographic base data  (9)
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  • Cartography and geographic base data  (9)
  • 1
    Online Resource
    Online Resource
    Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 2 ( 2020-02-20), p. 116-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 2 ( 2020-02-20), p. 116-
    Abstract: Accurate and timely location prediction of moving objects is crucial for intelligent transportation systems and traffic management. In recent years, ubiquitous location acquisition technologies have provided the opportunity for mining knowledge from trajectories, making location prediction and real-time decisions more feasible. Previous location prediction methods have mostly developed on the basis of shallow models whereas shallow models are not competent for some tricky challenges such as multi-time-step location coordinates prediction. Motivated by the current study status, we are dedicated to a deep-learning-based approach to predict the coordinates of several future locations of moving objects based on recent trajectory records. The method of this work consists of three successive parts: trajectory preprocessing, prediction model construction, and post-processing. In this work, a prediction model named the bidirectional recurrent mixture density network (BiRMDN) was constructed by integrating the long short-term memory (LSTM) and mixture density network (MDN) together. This model has the ability to learn long-term contextual information from recent trajectory and model real-valued location coordinates. We employed a vessel trajectory dataset for the implementation of this approach and determined the optimal model configuration after several parameter analysis experiments. Experimental results involving a performance comparison with other widely used methods demonstrate the superiority of the BiRMDN model.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9020116
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 2
    Online Resource
    Online Resource
    Mobility Modes Awareness from Trajectories Based on Clustering and a Convolutional Neural Network
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 5 ( 2019-05-07), p. 208-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 5 ( 2019-05-07), p. 208-
    Abstract: Massive trajectory data generated by ubiquitous position acquisition technology are valuable for knowledge discovery. The study of trajectory mining that converts knowledge into decision support becomes appealing. Mobility modes awareness is one of the most important aspects of trajectory mining. It contributes to land use planning, intelligent transportation, anomaly events prevention, etc. To achieve better comprehension of mobility modes, we propose a method to integrate the issues of mobility modes discovery and mobility modes identification together. Firstly, route patterns of trajectories were mined based on unsupervised origin and destination (OD) points clustering. After the combination of route patterns and travel activity information, different mobility modes existing in history trajectories were discovered. Then a convolutional neural network (CNN)-based method was proposed to identify the mobility modes of newly emerging trajectories. The labeled history trajectory data were utilized to train the identification model. Moreover, in this approach, we introduced a mobility-based trajectory structure as the input of the identification model. This method was evaluated with a real-world maritime trajectory dataset. The experiment results indicated the excellence of this method. The mobility modes discovered by our method were clearly distinguishable from each other and the identification accuracy was higher compared with other techniques.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8050208
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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  • 3
    Online Resource
    Online Resource
    A Formal Framework for Integrated Environment Modeling Systems
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 2 ( 2017-02-17), p. 47-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 2 ( 2017-02-17), p. 47-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi6020047
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
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  • 4
    Online Resource
    Online Resource
    Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 1 ( 2017-01-20), p. 26-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 1 ( 2017-01-20), p. 26-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi6010026
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
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  • 5
    Online Resource
    Online Resource
    The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 11 ( 2019-11-07), p. 502-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 11 ( 2019-11-07), p. 502-
    Abstract: Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain high spatial and temporal resolution remote sensing data for the target sensor with the help of the spatiotemporal fusion method. In this study, we employed three different sensor datasets to obtain one normalized difference vegetation index (NDVI) time series dataset with a 5.8-m spatial resolution using a spatial and temporal adaptive reflectance fusion model (STARFM). We studied the effectiveness of using multisource remote sensing data to extract crop classifications and analyzed whether the increase in the NDVI time series density could significantly improve the accuracy of the crop classification. The results indicated that multisource sensor data could be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform. With the increase in the number of NDVI phases, the classification accuracy of the support vector machine (SVM) and the random forest (RF) classifier gradually improved. If the added NDVI phases were not in the optimal time period for wheat recognition, the classification accuracy was not greatly improved. Under the same conditions, the classification accuracy of the RF classifier was higher than that of the SVM. In addition, this study can serve as a good reference for the selection of the optimal time range for base image pairs in the spatiotemporal fusion method for high accuracy mapping of crops, and help avoid excessive data collection and processing.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8110502
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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  • 6
    Online Resource
    Online Resource
    GeoGraphVis: A Knowledge Graph and Geovisualization Empowered Cyberinfrastructure to Support Disaster Response and Humanitarian Aid
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 3 ( 2023-03-07), p. 112-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 3 ( 2023-03-07), p. 112-
    Abstract: The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely disaster response and humanitarian aid to these events is a critical topic for decision makers and relief experts in order to mitigate impacts and save lives. When a disaster occurs, it is important to acquire first-hand, real-time information about the potentially affected area, its infrastructure, and its people in order to develop situational awareness and plan a response to address the health needs of the affected population. This requires rapid assembly of multi-source geospatial data that need to be organized and visualized in a way to support disaster-relief efforts. In this paper, we introduce a new cyberinfrastructure solution—GeoGraphVis—that is empowered by knowledge graph technology and advanced visualization to enable intelligent decision making and problem solving. There are three innovative features of this solution. First, a location-aware knowledge graph is created to link and integrate cross-domain data to make the graph analytics-ready. Second, expert-driven disaster response workflows are analyzed and modeled as machine-understandable decision paths to guide knowledge exploration via the graph. Third, a scene-based visualization strategy is developed to enable interactive and heuristic visual analytics to better comprehend disaster impact situations and develop action plans for humanitarian aid.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12030112
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
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  • 7
    Online Resource
    Online Resource
    A Matching Model for Door-to-Door Multimodal Transit by Integrating Taxi-Sharing and Subways
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 7 ( 2021-07-08), p. 469-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 7 ( 2021-07-08), p. 469-
    Abstract: We present a sustainable multimodal transit system that integrates taxi-sharing with subways to alleviate traffic congestion and restore the cooperative relationship between taxis and subways. This study proposes a two-phase matching model based on optimization theory, in which pick-up/drop-off sequences for participants, as well as their motivation to shift to a TSS service, were considered. For the transportation system, achieving a reduction in vehicle miles is considered to be the matching objective. We tested the matching model using empirical taxi global positioning system (GPS) data for a typical morning rush hour in Beijing. The optimization model performs well for large-scale data and the optimal solution can be calculated quickly, which is ideal in a dynamic system. Furthermore, several sensitive analysis experiments were conducted to evaluate the performance of the TSS system. We found that approximately 23.13% of taxi users can be served by TSS transit, total taxi mileage can be reduced by 20.17%, and carbon dioxide emissions may be reduced by 15.16%. The proposed model and findings demonstrate that the TSS service considered here is a feasible multimodal transit mode, with the advantages of flexibility and sustainability, and has great potential for improving social benefits.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10070469
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 8
    Online Resource
    Online Resource
    A Novel Absolute Orientation Method Using Local Similarities Representation
    MDPI AG ; 2016
    In:  ISPRS International Journal of Geo-Information Vol. 5, No. 8 ( 2016-08-09), p. 135-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 5, No. 8 ( 2016-08-09), p. 135-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi5080135
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
    detail.hit.zdb_id: 2655790-3
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  • 9
    Online Resource
    Online Resource
    Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 3 ( 2018-03-14), p. 110-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 3 ( 2018-03-14), p. 110-
    Abstract: Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi7030110
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655790-3
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