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  • Cartography and geographic base data  (13)
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  • Cartography and geographic base data  (13)
  • 1
    Online Resource
    Online Resource
    Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 5 ( 2021-04-29), p. 284-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 5 ( 2021-04-29), p. 284-
    Abstract: A holistic strategy is established for automated UAV-LiDAR strip adjustment for plantation forests, based on hierarchical density-based clustering analysis of the canopy cover. The method involves three key stages: keypoint extraction, feature similarity and correspondence, and rigid transformation estimation. Initially, the HDBSCAN algorithm is used to cluster the scanned canopy cover, and the keypoints are marked using topological persistence analysis of the individual clusters. Afterward, the feature similarity is calculated by considering the linear and angular relationships between each point and the pointset centroid. The one-to-one feature correspondence is retrieved by solving the assignment problem on the similarity score function using the Kuhn–Munkres algorithm, generating a set of matching pairs. Finally, 3D rigid transformation parameters are determined by permutations over all conceivable pair combinations within the correspondences, whereas the best pair combination is that which yields the maximum count of matched points achieving distance residuals within the specified tolerance. Experimental data covering eighteen subtropical forest plots acquired from the GreenValley and Riegl UAV-LiDAR platforms in two scan modes are used to validate the method. The results are extremely promising for redwood and poplar tree species from both the Velodyne and Riegl UAV-LiDAR datasets. The minimal mean distance residuals of 31 cm and 36 cm are achieved for the coniferous and deciduous plots of the Velodyne data, respectively, whereas their corresponding values are 32 cm and 38 cm for the Riegl plots. Moreover, the method achieves both higher matching percentages and lower mean distance residuals by up to 28% and 14 cm, respectively, compared to the baseline method, except in the case of plots with extremely low tree height. Nevertheless, the mean planimetric distance residual achieved by the proposed method is lower by 13 cm.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10050284
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    A New Geographical Cluster View on Passenger Vehicle Purchasing in Chinese Cities
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 1 ( 2018-01-01), p. 9-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 1 ( 2018-01-01), p. 9-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi7010009
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Multi-temporal remote sensing data applied in automatic land cover update using iterative training sample selection and Markov Random Field model
    Informa UK Limited ; 2015
    In:  Geocarto International Vol. 30, No. 8 ( 2015-09-14), p. 882-893
    Details
    In: Geocarto International, Informa UK Limited, Vol. 30, No. 8 ( 2015-09-14), p. 882-893
    Type of Medium: Online Resource
    ISSN: 1010-6049 , 1752-0762
    URL: Article
    DOI: 10.1080/10106049.2014.997310
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2015
    detail.hit.zdb_id: 2109550-4
    SSG: 14
    SSG: 14,1
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    Online Resource
  • 4
    Online Resource
    Online Resource
    For-backward LSTM-based missing data reconstruction for time-series Landsat images
    Informa UK Limited ; 2022
    In:  GIScience & Remote Sensing Vol. 59, No. 1 ( 2022-12-31), p. 410-430
    Details
    In: GIScience & Remote Sensing, Informa UK Limited, Vol. 59, No. 1 ( 2022-12-31), p. 410-430
    Type of Medium: Online Resource
    ISSN: 1548-1603 , 1943-7226
    URL: Article
    DOI: 10.1080/15481603.2022.2031549
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2209042-3
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    Online Resource
  • 5
    Online Resource
    Online Resource
    Size-Adaptive Texture Atlas Generation and Remapping for 3D Urban Building Models
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 12 ( 2021-11-29), p. 798-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 12 ( 2021-11-29), p. 798-
    Abstract: A high-fidelity 3D urban building model requires large quantities of detailed textures, which can be non-tiled or tiled ones. The fast loading and rendering of these models remain challenges in web-based large-scale 3D city visualization. The traditional texture atlas methods compress all the textures of a model into one atlas, which needs extra blank space, and the size of the atlas is uncontrollable. This paper introduces a size-adaptive texture atlas method that can pack all the textures of a model without losing accuracy and increasing extra storage space. Our method includes two major steps: texture atlas generation and texture atlas remapping. First, all the textures of a model are classified into non-tiled and tiled ones. The maximum supported size of the texture is acquired from the graphics hardware card, and all the textures are packed into one or more atlases. Then, the texture atlases are remapped onto the geometric meshes. For the triangle with the original non-tiled texture, new texture coordinates in the texture atlases can be calculated directly. However, as for the triangle with the original tiled texture, it is clipped into many unit triangles to apply texture mapping. Although the method increases the mesh vertex number, the increased geometric vertices have much less impact on the rendering efficiency compared with the method of increasing the texture space. The experiment results show that our method can significantly improve building model rendering efficiency for large-scale 3D city visualization.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10120798
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 6
    Online Resource
    Online Resource
    Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 9 ( 2019-09-17), p. 417-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 9 ( 2019-09-17), p. 417-
    Abstract: Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will include too many remote sensing objects and their complex spatial relationships. This will increase the computational burden of the image captioning network and reduce its precision. If the patch size is too small, it often fails to provide enough environmental and contextual information, which makes the remote sensing object difficult to describe. To address this problem, we propose a multi-scale semantic long short-term memory network (MS-LSTM). The remote sensing images are paired into image patches with different spatial scales. First, the large-scale patches have larger sizes. We use a Visual Geometry Group (VGG) network to extract the features from the large-scale patches and input them into the improved MS-LSTM network as the semantic information, which provides a larger receptive field and more contextual semantic information for small-scale image caption so as to play the role of global perspective, thereby enabling the accurate identification of small-scale samples with the same features. Second, a small-scale patch is used to highlight remote sensing objects and simplify their spatial relations. In addition, the multi-receptive field provides perspectives from local to global. The experimental results demonstrated that compared with the original long short-term memory network (LSTM), the MS-LSTM’s Bilingual Evaluation Understudy (BLEU) has been increased by 5.6% to 0.859, thereby reflecting that the MS-LSTM has a more comprehensive receptive field, which provides more abundant semantic information and enhances the remote sensing image captions.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8090417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 7
    Online Resource
    Online Resource
    Superpixel-based time-series reconstruction for optical images incorporating SAR data using autoencoder networks
    Informa UK Limited ; 2020
    In:  GIScience & Remote Sensing Vol. 57, No. 8 ( 2020-11-16), p. 1005-1025
    Details
    In: GIScience & Remote Sensing, Informa UK Limited, Vol. 57, No. 8 ( 2020-11-16), p. 1005-1025
    Type of Medium: Online Resource
    ISSN: 1548-1603 , 1943-7226
    URL: Article
    DOI: 10.1080/15481603.2020.1841459
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2209042-3
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    Online Resource
  • 8
    Online Resource
    Online Resource
    A Generalized Linear Mixed Model Approach to Assess Emerald Ash Borer Diffusion
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 7 ( 2020-06-27), p. 414-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 7 ( 2020-06-27), p. 414-
    Abstract: The Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis Fairmaire) can cause damage to all species of Ash trees (Fraxinus), and rampant, unchecked infestations of this insect can cause significant damage to forests. It is thus critical to assess and model the spread of the EAB in a manner that allows authorities to anticipate likely areas of future tree infestation. In this study, a generalized linear mixed model (GLMM), combining the features of the commonly used generalized linear model (GLM) and a random effects model, was developed to predict future EAB spread patterns in Southern Ontario, Canada. The GLMM was designed to deal with autocorrelation in the data. Two random effects were established based on the geographic information provided with the EAB data, and a method based on statistical inference was proposed to identify the most significant factors associated with the distribution of the EAB. The results of the model showed that 95% of the testing data were correctly classified. The predictive performance of the GLMM was substantially enhanced in comparison with that obtained by the GLM. The influence of climatic factors, such as wind speed and anthropogenic activities, had the most significant influence on the spread of the EAB.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9070414
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 9
    Online Resource
    Online Resource
    Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 4 ( 2020-03-26), p. 194-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 4 ( 2020-03-26), p. 194-
    Abstract: When a landslide happens, it is important to recognize the hazard-affected bodies surrounding the landslide for the risk assessment and emergency rescue. In order to realize the recognition, the spatial relationship between landslides and other geographic objects such as residence, roads and schools needs to be defined. Comparing with semantic segmentation and instance segmentation that can only recognize the geographic objects separately, image captioning can provide richer semantic information including the spatial relationship among these objects. However, the traditional image captioning methods based on RNNs have two main shortcomings: the errors in the prediction process are often accumulated and the location of attention is not always accurate which would lead to misjudgment of risk. To handle these problems, a landslide image interpretation network based on a semantic gate and a bi-temporal long-short term memory network (SG-BiTLSTM) is proposed in this paper. In the SG-BiTLSTM architecture, a U-Net is employed as an encoder to extract features of the images and generate the mask maps of the landslides and other geographic objects. The decoder of this structure consists of two interactive long-short term memory networks (LSTMs) to describe the spatial relationship among these geographic objects so that to further determine the role of the classified geographic objects for identifying the hazard-affected bodies. The purpose of this research is to judge the hazard-affected bodies of the landslide (i.e., buildings and roads) through the SG-BiTLSTM network to provide geographic information support for emergency service. The remote sensing data was taken by Worldview satellite after the Wenchuan earthquake happened in 2008. The experimental results demonstrate that SG-BiTLSTM network shows remarkable improvements on the recognition of landslide and hazard-affected bodies, compared with the traditional LSTM (the Baseline Model), the BLEU1 of the SG-BiTLSTM is improved by 5.89%, the matching rate between the mask maps and the focus matrix of the attention is improved by 42.81%. In conclusion, the SG-BiTLSTM network can recognize landslides and the hazard-affected bodies simultaneously to provide basic geographic information service for emergency decision-making.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9040194
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 10
    Online Resource
    Online Resource
    SASTGCN: A Self-Adaptive Spatio-Temporal Graph Convolutional Network for Traffic Prediction
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 8 ( 2023-08-18), p. 346-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 8 ( 2023-08-18), p. 346-
    Abstract: Traffic prediction plays a significant part in creating intelligent cities such as traffic management, urban computing, and public safety. Nevertheless, the complex spatio-temporal linkages and dynamically shifting patterns make it somewhat challenging. Existing mainstream traffic prediction approaches heavily rely on graph convolutional networks and sequence prediction methods to extract complicated spatio-temporal patterns statically. However, they neglect to account for dynamic underlying correlations and thus fail to produce satisfactory prediction results. Therefore, we propose a novel Self-Adaptive Spatio-Temporal Graph Convolutional Network (SASTGCN) for traffic prediction. A self-adaptive calibrator, a spatio-temporal feature extractor, and a predictor comprise the bulk of the framework. To extract the distribution bias of the input in the self-adaptive calibrator, we employ a self-supervisor made of an encoder–decoder structure. The concatenation of the bias and the original characteristics are provided as input to the spatio-temporal feature extractor, which leverages a transformer and graph convolution structures to learn the spatio-temporal pattern, and then applies a predictor to produce the final prediction. Extensive trials on two public traffic prediction datasets (METR-LA and PEMS-BAY) demonstrate that SASTGCN surpasses the most recent techniques in several metrics.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12080346
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
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