{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:46:20Z","timestamp":1760240780679,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,9,17]],"date-time":"2019-09-17T00:00:00Z","timestamp":1568678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2018YFC0810600, 2018YFC0810605"],"award-info":[{"award-number":["2018YFC0810600, 2018YFC0810605"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/ijgi8090417","type":"journal-article","created":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T04:50:21Z","timestamp":1569214221000},"page":"417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6857-3417","authenticated-orcid":false,"given":"Wei","family":"Cui","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyou","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"He","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjie","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijie","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqi","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2595-4946","authenticated-orcid":false,"given":"Jiejun","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A Computer Movie Simulating Urban Growth in the Detroit Region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_2","first-page":"69","article-title":"The First Law of Geography and Spatial-Temporal Proximity","volume":"29","author":"Li","year":"2007","journal-title":"Chin. J. Nat."},{"key":"ref_3","first-page":"1749","article-title":"The enlightenment of geographical theories construction from the First Law of Geography and its debate","volume":"31","author":"Sun","year":"2012","journal-title":"Geogr. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cui, W., Wang, F., He, X., Zhang, D., Xu, X., Yao, M., Wang, Z., and Huang, J. (2019). Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model. Remote Sens., 11.","DOI":"10.3390\/rs11091044"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Qu, B., Li, X., Tao, D., and Lu, X. (2016, January 6\u20138). Deep semantic understanding of high resolution remote sensing image. Proceedings of the 2016 International Conference on Computer, Information and Telecommunication Systems (CITS 2016), Kunming, China.","DOI":"10.1109\/CITS.2016.7546397"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3623","DOI":"10.1109\/TGRS.2017.2677464","article-title":"Can a Machine Generate Humanlike Language Descriptions for a Remote Sensing Image?","volume":"55","author":"Shi","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1109\/TGRS.2017.2776321","article-title":"Exploring Models and Data for Remote Sensing Image Caption Generation","volume":"56","author":"Lu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1109\/LGRS.2019.2893772","article-title":"Semantic Descriptions of High-Resolution Remote Sensing Images","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, X., Tang, X., Zhou, H., and Li, C. (2019). Description Generation for Remote Sensing Images Using Attribute Attention Mechanism. Remote Sens., 11.","DOI":"10.3390\/rs11060612"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TGRS.1986.289598","article-title":"Segmentation of a Thematic Mapper Image Using the Fuzzy c-Means Clusterng Algorthm","volume":"GE-24","author":"Cannon","year":"1986","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1109\/36.158859","article-title":"Classification with spatio-temporal interpixel class dependency contexts","volume":"30","author":"Jeon","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","first-page":"12","article-title":"An optimization approach for high quality multi-scale image segmentation","volume":"12","author":"Baatz","year":"2000","journal-title":"Angew. Geogr. Inf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4238","DOI":"10.1109\/TGRS.2015.2393857","article-title":"Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images","volume":"53","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1109\/TGRS.2014.2374218","article-title":"Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning","volume":"53","author":"Han","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1080\/01431161.2016.1266059","article-title":"Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery","volume":"38","author":"Han","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"025006","DOI":"10.1117\/1.JRS.10.025006","article-title":"Large patch convolutional neural networks for the scene classification of high spatial resolution imagery","volume":"10","author":"Zhong","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017). Mask R-CNN. arXiv.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2015, January 7\u201312). Show and tell: A neural image caption generator. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111338","DOI":"10.1016\/j.rse.2019.111338","article-title":"Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation","volume":"232","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4062","DOI":"10.1109\/TGRS.2018.2889677","article-title":"Cloud Detection in Remote Sensing Image on Multiscale Features-Convolution Neural Network","volume":"57","author":"Shao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tao, Y., Xu, M., Lu, Z., and Zhong, Y. (2018). DenseNet-Based Depth-Width Double Reinforced Deep Learning Neural Network for High-Resolution Remote Sensing Image Per-Pixel Classification. Remote Sens., 10.","DOI":"10.3390\/rs10050779"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., and Zhang, S. (2018). Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery. Sensors, 18.","DOI":"10.3390\/s18113717"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhuang, S., Wang, P., Jiang, B., Wang, G., and Wang, C. (2019). A Single Shot Framework with Multi-Scale Feature Fusion for Geospatial Object Detection. Remote Sens., 11.","DOI":"10.3390\/rs11050594"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liang, B., Ding, M., and Li, J. (2018). Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11010020"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, P., Liu, X., Liu, M., Shi, Q., Yang, J., Xu, X., and Zhang, Y. (2019). Building Footprint Extraction from High-Resolution Images via Spatial Residual Inception Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11070830"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fu, K., Li, Y., Sun, H., Yang, X., Xu, G., Li, Y., and Sun, X. (2018). A Ship Rotation Detection Model in Remote Sensing Images Based on Feature Fusion Pyramid Network and Deep Reinforcement Learning. Remote Sens., 10.","DOI":"10.3390\/rs10121922"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, S., Zhu, X., and Bao, J. (2019). Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification. Sensors, 19.","DOI":"10.3390\/s19071714"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lv, X., Ming, D., Lu, T., Zhou, K., Wang, M., and Bao, H. (2018). A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification. Remote Sens., 10.","DOI":"10.3390\/rs10121946"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, Z., Tan, B., Pei, H., and Jiang, W. (2018). Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data. Sensors, 18.","DOI":"10.3390\/s18103347"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Feng, J., Wang, L., Yu, H., Jiao, L., and Zhang, X. (2019). Divide-and-Conquer Dual-Architecture Convolutional Neural Network for Classification of Hyperspectral Images. Remote Sens., 11.","DOI":"10.3390\/rs11050484"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep Recurrent Neural Networks for Hyperspectral Image Classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, H., and Prasad, S. (2017). Convolutional Recurrent Neural Networks forHyperspectral Data Classification. Remote Sens., 9.","DOI":"10.3390\/rs9030298"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1080\/2150704X.2018.1511933","article-title":"Spectral-spatial classification of hyperspectral imagery based on recurrent neural networks","volume":"9","author":"Liu","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhou, F., Hang, R., and Yuan, X. (2017). Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification. Remote Sens., 9.","DOI":"10.3390\/rs9121330"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Seydgar, M., Alizadeh Naeini, A., Zhang, M., Li, W., and Satari, M. (2019). 3-D Convolution-Recurrent Networks for Spectral-Spatial Classification of Hyperspectral Images. Remote Sens., 11.","DOI":"10.3390\/rs11070883"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1109\/TGRS.2017.2777868","article-title":"SAR Image Classification via Deep Recurrent Encoding Neural Networks","volume":"56","author":"Geng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., and Hossard, L. (2018). Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.1117\/12.2325160"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ma, A., Filippi, A., Wang, Z., and Yin, Z. (2019). Hyperspectral Image Classification Using Similarity Measurements-Based Deep Recurrent Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11020194"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TPAMI.2016.2598339","article-title":"Deep Visual-Semantic Alignments for Generating Image Descriptions","volume":"39","author":"Karpathy","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","unstructured":"Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., and Bengio, Y. (2015). Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lu, J., Xiong, C., Parikh, D., and Socher, R. (2016). Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning. arXiv.","DOI":"10.1109\/CVPR.2017.345"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6482","DOI":"10.1080\/01431161.2019.1594439","article-title":"Geospatial relation captioning for high-spatial-resolution images by using an attention-based neural network","volume":"40","author":"Chen","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","unstructured":"Zhang, X., Wang, Q., and Li, X. (August, January 28). Multi-Scale Cropping Mechanism for Remote Sensing Image Captioning. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Yokohama, Japan."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, Y., Lin, Z., Shen, X., Cohen, S., and Cottrell, G.W. (2017). Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition. arXiv.","DOI":"10.1109\/CVPR.2017.780"},{"key":"ref_51","first-page":"131","article-title":"Judgement Characteristics and Quantitative Index of Suitable Block Scale","volume":"40","author":"Huang","year":"2012","journal-title":"J. South China Univ. Technol. (Nat. Sci. Ed.)"},{"key":"ref_52","first-page":"81","article-title":"Taking history as a Lesson: Research on the evoiution of block Sizes from the perspective of typomorphoiogy","volume":"10","author":"Wang","year":"2018","journal-title":"Plan. Des."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., and Chua, T.S. (2016). SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning. arXiv.","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TNN.2006.875977","article-title":"Universal approximation using incremental constructive feedforward networks with random hidden nodes","volume":"17","author":"Huang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_55","unstructured":"Huang, G.B., Zhu, Q.Y., and Siew, C.K. (2004, January 25\u201329). Extreme learning machine: A new learning scheme of feedforward neural networks. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), Budapest, Hungary."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","article-title":"Trends in extreme learning machines: A review","volume":"61","author":"Huang","year":"2015","journal-title":"Neural Netw."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2588","DOI":"10.1016\/j.patcog.2011.03.013","article-title":"Human face recognition based on multidimensional PCA and extreme learning machine","volume":"44","author":"Mohammed","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/TCYB.2014.2307349","article-title":"Semi-Supervised and Unsupervised Extreme Learning Machines","volume":"44","author":"Huang","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5795","DOI":"10.3390\/rs6065795","article-title":"Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine","volume":"6","author":"Chen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2018.2829166","article-title":"Deep Kernel Extreme-Learning Machine for the Spectral\u2013Spatial Classification of Hyperspectral Imagery","volume":"10","author":"Li","year":"2018","journal-title":"Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Salerno, V.M., and Rabbeni, G. (2018). An Extreme Learning Machine Approach to Effective Energy Disaggregation. Electronics, 7.","DOI":"10.20944\/preprints201808.0551.v1"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/9\/417\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:20:58Z","timestamp":1760188858000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/9\/417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,17]]},"references-count":61,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["ijgi8090417"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8090417","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2019,9,17]]}}}