{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T20:08:16Z","timestamp":1785701296611,"version":"3.56.0"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Engineering Research at Seoul National University","award":["no number"],"award-info":[{"award-number":["no number"]}]},{"name":"BK21 PLUS research program of the National Research Foundation of Korea","award":["no number"],"award-info":[{"award-number":["no number"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under the same conditions to construct data labels is difficult. Indeed, small datasets limit the effective learning of deep-learning networks. To address this problem, we propose a combined U-net model that is trained using a combined weighted loss function and can handle heterogeneous datasets. The network consists of encoder and decoder blocks. The convolutional layers that form the encoder blocks are shared with the heterogeneous datasets, and the decoder blocks are assigned separate training weights. Herein, the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Cityscape datasets are used as the RS and natural-image datasets, respectively. When the layers are shared, only visible bands of the ISPRS Potsdam data are used. Experimental results show that when same-sized heterogeneous datasets are used, the semantic segmentation accuracy of the Potsdam data obtained using our proposed method is lower than that obtained using only the Potsdam data (four bands) with other methods, such as SegNet, DeepLab-V3+, and the simplified version of U-net. However, the segmentation accuracy of the Potsdam images is improved when the larger Cityscape dataset is used. The combined U-net model can effectively train heterogeneous datasets and overcome the insufficient training data problem in the context of RS-image datasets. Furthermore, it is expected that the proposed method can not only be applied to segmentation tasks of aerial images but also to tasks with various purposes of using big heterogeneous datasets.<\/jats:p>","DOI":"10.3390\/ijgi9100601","type":"journal-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T10:18:00Z","timestamp":1602497880000},"page":"601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9190-2848","authenticated-orcid":false,"given":"Ahram","family":"Song","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongil","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"ref_1","unstructured":"Li, H., Cai, J., Nguyen, T.N.A., and Zheng, J. (2013, January 15\u201319). A benchmark for semantic image segmentation. Proceedings of the IEEE International Conference on Multimedia and Expo, San Jose, CA, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.neucom.2018.03.037","article-title":"Methods and datasets on semantic segmentation: A review","volume":"304","author":"Yu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e1264","DOI":"10.1002\/widm.1264","article-title":"Deep learning for remote sensing image classification: A survey","volume":"8","author":"Li","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2014","journal-title":"Int. J. Comput. Vis."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/LGRS.2018.2795531","article-title":"Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM","volume":"15","author":"Sun","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kemker, R., Salvaggio, C., and Kanan, C.W. (2018). Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning. ISPRS J. Photogramm. Remote Sens., 60\u201377.","DOI":"10.1016\/j.isprsjprs.2018.04.014"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rahman, M.T. (2016). Detection of land use\/land cover changes and urban sprawl in Al-Khobar, Saudi Arabia: An analysis of multi-temporal remote sensing data. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5020015"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.isprsjprs.2019.02.006","article-title":"Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks","volume":"150","author":"Wurm","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hu, J., Li, L., Lin, Y., Wu, F., and Zhao, J. (2019, January 20\u201322). A comparison and strategy of semantic segmentation on remote sensing images. Proceedings of the International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, Kunming, China.","DOI":"10.1007\/978-3-030-32456-8_3"},{"key":"ref_13","unstructured":"Shelhamer, E., Long, J., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5585","DOI":"10.1109\/TGRS.2017.2710079","article-title":"Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification","volume":"55","author":"Jiao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fu, G., Liu, C., Zhou, R., Sun, T., and Zhang, Q. (2017). Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sens., 9.","DOI":"10.3390\/rs9050498"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer Assisted Interventions, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/LGRS.2018.2879492","article-title":"Water body extraction from very high-resolution remote sensing imagery using deep u-net and a super pixel -based conditional random field model","volume":"16","author":"Feng","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ren, Q., Geng, J., Ding, M., and Li, J. (2018). Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images. Sensors, 18.","DOI":"10.3390\/s18103232"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/LGRS.2015.2499239","article-title":"Deep learning earth observation classification using ImageNet pretrained networks","volume":"13","author":"Marmanis","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Marmanis, D., Wegner, J.D., Galliani, S., Schindler, K., Datcu, M., and Stilla, U. (2016, January 12\u201319). Semantic segmentation of aerial images with an ensemble of CNSS. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic.","DOI":"10.5194\/isprsannals-III-3-473-2016"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Audebert, N., Le Saux, B., and Lef\u00e8vre, S. (2016, January 20\u201324). Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks. Proceedings of the Computer Vision\u2014ACCV, Taipei, Taiwan.","DOI":"10.1007\/978-3-319-54181-5_12"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"293","DOI":"10.5194\/isprsannals-I-3-293-2012","article-title":"The ISPRS benchmark on urban object classification and 3D building reconstruction","volume":"1","author":"Rottensteiner","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Volpi, M., and Ferrari, V. (2015, January 8\u201310). Semantic segmentation of urban scenes by learning local class interactions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301377"},{"key":"ref_25","unstructured":"(2018, December 01). Dstl Satellite Imagery Feature Detection. Available online: https:\/\/www.kaggle.com\/c\/dstl-satellite-imagery-feature-detection\/overview\/description."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Meletis, P., and Dubbelman, G. (2018, January 26\u201330). Training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation. Proceedings of the IEEE Intelligent Vehicles Symposium, Changshu, Suzhou, China.","DOI":"10.1109\/IVS.2018.8500398"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., and Igel, C. (2013, January 4\u20139). Detection of traffic signs in real- world images: The german traffic sign detection benchmark. Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA.","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6517","DOI":"10.1109\/TGRS.2019.2906689","article-title":"Learning and adapting robust features for satellite image segmentation on heterogeneous datasets","volume":"57","author":"Ghassemi","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","first-page":"1","article-title":"Transfer learning for high resolution aerial image classification","volume":"10","author":"Liang","year":"2016","journal-title":"Proc. IEEE Appl. Imag. Pattern Recognit. Workshop"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lee, H., Eum, S., and Kwon, H. (2018, January 22\u201327). Cross-domain CNN for hyperspectral image classification. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519419"},{"key":"ref_31","unstructured":"ISPRS WG III\/4 (2020, January 16). ISPRS 2D Semantic Labeling Contest. Available online: http:\/\/www2.isprs.org\/commissions\/comm3\/wg4\/semantic-labeling.html."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"61677","DOI":"10.1109\/ACCESS.2018.2874767","article-title":"Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications","volume":"6","author":"Carneiro","year":"2018","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, Z., Pan, Z., and Lei, B. (2017). Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data. Remote Sens., 9.","DOI":"10.3390\/rs9090907"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/10\/601\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:19:35Z","timestamp":1760177975000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/10\/601"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":33,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["ijgi9100601"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9100601","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,12]]}}}