{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:16:22Z","timestamp":1766067382019,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2017YFB0502700"],"award-info":[{"award-number":["2017YFB0502700"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61501098"],"award-info":[{"award-number":["61501098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the High-Resolution Earth Observation Youth Foundation","award":["GFZX04061502"],"award-info":[{"award-number":["GFZX04061502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Instance segmentation of high-resolution aerial images is challenging when compared to object detection and semantic segmentation in remote sensing applications. It adopts boundary-aware mask predictions, instead of traditional bounding boxes, to locate the objects-of-interest in pixel-wise. Meanwhile, instance segmentation can distinguish the densely distributed objects within a certain category by a different color, which is unavailable in semantic segmentation. Despite the distinct advantages, there are rare methods which are dedicated to the high-quality instance segmentation for high-resolution aerial images. In this paper, a novel instance segmentation method, termed consistent proposals of instance segmentation network (CPISNet), for high-resolution aerial images is proposed. Following top-down instance segmentation formula, it adopts the adaptive feature extraction network (AFEN) to extract the multi-level bottom-up augmented feature maps in design space level. Then, elaborated RoI extractor (ERoIE) is designed to extract the mask RoIs via the refined bounding boxes from proposal consistent cascaded (PCC) architecture and multi-level features from AFEN. Finally, the convolution block with shortcut connection is responsible for generating the binary mask for instance segmentation. Experimental conclusions can be drawn on the iSAID and NWPU VHR-10 instance segmentation dataset: (1) Each individual module in CPISNet acts on the whole instance segmentation utility; (2) CPISNet* exceeds vanilla Mask R-CNN 3.4%\/3.8% AP on iSAID validation\/test set and 9.2% AP on NWPU VHR-10 instance segmentation dataset; (3) The aliasing masks, missing segmentations, false alarms, and poorly segmented masks can be avoided to some extent for CPISNet; (4) CPISNet receives high precision of instance segmentation for aerial images and interprets the objects with fitting boundary.<\/jats:p>","DOI":"10.3390\/rs13142788","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T09:32:07Z","timestamp":1626341527000},"page":"2788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["CPISNet: Delving into Consistent Proposals of Instance Segmentation Network for High-Resolution Aerial Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1564-8494","authenticated-orcid":false,"given":"Xiangfeng","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8091-9540","authenticated-orcid":false,"given":"Shunjun","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinshan","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zichen","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7676-8380","authenticated-orcid":false,"given":"Jun","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2343-3055","authenticated-orcid":false,"given":"Xiaoling","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Science and Technology on Communication Security Laboratory, Institute of Southwestern Communication, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TIP.2018.2867198","article-title":"Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection","volume":"28","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.isprsjprs.2020.05.016","article-title":"Hyperli-net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery","volume":"167","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Su, H., Wei, S., Ming, J., Wang, C., Yan, M., Kumar, D., Shi, J., and Zhang, X. (2020). Precise and robust ship detection for high-resolution sar imagery based on hr-sdnet. Remote Sens., 12.","DOI":"10.3390\/rs12010167"},{"key":"ref_5","unstructured":"Liu, L., Pan, Z., and Lei, B. (2017). Learning a rotation invariant detector with rotatable bounding box. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8333","DOI":"10.1109\/TGRS.2019.2920534","article-title":"Drbox-v2: An improved detector with rotatable boxes for target detection in sar images","volume":"57","author":"An","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","unstructured":"Mou, L., and Zhu, X.X. (2018). Rifcn: Recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"Resunet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1109\/TGRS.2020.2994150","article-title":"Lanet: Local attention embedding to improve the semantic segmentation of remote sensing images","volume":"59","author":"Ding","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classification with an edge: Improving semantic image segmentation with boundary detection","volume":"135","author":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Su, H., Wei, S., Liu, S., Liang, J., Wang, C., Shi, J., and Zhang, X. (2020). Hq-isnet: High-quality instance segmentation for remote sensing imagery. Remote Sens., 12.","DOI":"10.3390\/rs12060989"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","article-title":"Hrsid: A high-resolution sar images dataset for ship detection and instance segmentation","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Su, H., Wei, S., Yan, M., Wang, C., Shi, J., and Zhang, X. (August, January 28). Object detection and instance segmentation in remote sensing imagery based on precise mask r-cnn. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898573"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Feng, Y., Diao, W., Zhang, Y., Li, H., Chang, Z., Yan, M., Sun, X., and Gao, X. (August, January 28). Ship instance segmentation from remote sensing images using sequence local context module. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8897948"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6699","DOI":"10.1109\/TGRS.2018.2841808","article-title":"Vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network","volume":"56","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","unstructured":"Zamir, S.W., Arora, A., Gupta, A., Khan, S., Sun, G., Khan, F.S., Zhu, F., Shao, L., Xia, G.-S., and Bai, X. (2019, January 16\u201320). isaid: A large-scale dataset for instance segmentation in aerial images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.isprsjprs.2020.07.011","article-title":"A novel deep learning instance segmentation model for automated marine oil spill detection","volume":"167","author":"Yekeen","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). Yolo9000: Better, faster, stronger. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Hononlulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_21","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_22","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_25","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_26","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (November, January 27). Centernet: Keypoint triplets for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_27","unstructured":"Yang, X., Liu, Q., Yan, J., Li, A., Zhang, Z., and Yu, G. (2019). R3det: Refined single-stage detector with feature refinement for rotating object. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Hononlulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 13\u201316). Fast r-cnn. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_31","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Y., Qi, H., Dai, J., Ji, X., and Wei, Y. (2017, January 21\u201326). Fully convolutional instance-aware semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Hononlulu, HI, USA.","DOI":"10.1109\/CVPR.2017.472"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path aggregation network for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., and Wang, X. (2019, January 16\u201320). Mask scoring r-cnn. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00657"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., and Ouyang, W. (2019, January 16\u201320). Hybrid task cascade for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00511"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., and Luo, P. (2020, January 13\u201319). Polarmask: Single shot instance segmentation with polar representation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01221"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., and Li, L. (2020, January 23\u201328). Solo: Segmenting objects by locations. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"ref_39","unstructured":"Wang, X., Zhang, R., Kong, T., Li, L., and Shen, C. (2020). Solov2: Dynamic and fast instance segmentation. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., and Yan, Y. (2020, January 13\u201319). Blendmask: Top-down meets bottom-up for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., and Doll\u00e1r, P. (2020, January 13\u201319). Designing network design spaces. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Hononlulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_43","unstructured":"Radosavovic, I., Johnson, J., Xie, S., Lo, W.-Y., and Doll\u00e1r, P. (November, January 27). On network design spaces for visual recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 16\u201320). Deformable convnets v2: More deformable, better results. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Vu, T., Kang, H., and Yoo, C.D. (2020). Scnet: Training inference sample consistency for instance segmentation. arXiv.","DOI":"10.1609\/aaai.v35i3.16374"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2014.10.002","article-title":"Multi-class geospatial object detection and geographic image classification based on collection of part detectors","volume":"98","author":"Cheng","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., and Davis, L.S. (2017, January 22\u201329). Soft-nms\u2013improving object detection with one line of code. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.593"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 16\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_52","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Rossi, L., Karimi, A., and Prati, A. (2021, January 10\u201315). A novel region of interest extraction layer for instance segmentation. Proceedings of the 2020 25th IEEE International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412258"},{"key":"ref_54","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). Mmdetection: Open mmlab detection toolbox and benchmark. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2788\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:31:10Z","timestamp":1760164270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2788"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,15]]},"references-count":54,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142788"],"URL":"https:\/\/doi.org\/10.3390\/rs13142788","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,7,15]]}}}