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Most current methods mainly take images as input and sometimes cannot achieve satisfactory results due to limited information. Inspired by the inherent relations between land cover and land use, we investigate land use segmentation using additional land cover data. The topological relations among land cover objects are beneficial for bridging the semantic gap between land cover and land use. Specifically, these relations are usually depicted by a geo-object-based graph structure. Deep convolutional neural networks (CNNs) are capable of extracting local patterns but fail to efficiently explore topological relations. In contrast, contextual relations among objects can be easily captured by graph convolutional networks (GCNs). In this study, we integrated CNNs and GCNs and proposed the CNN-enhanced HEterogeneous Graph Convolutional Network (CHeGCN) to incorporate local spectral-spatial features and long-range dependencies. We represent topological relations by heterogeneous graphs which are constructed with images and land cover data. Afterwards, we employed GCNs to build topological relations by graph reasoning. Finally, we fused CNN and GCN features to accomplish the inference from land cover to land use. Compared with other homogeneous graph-based models, the land cover data provide more sufficient information for graph reasoning. The proposed method can achieve the transformation from land cover to land use. Extensive experiments showed the competitive performance of CHeGCN and demonstrated the positive effects of land cover data. On the IoU metric over two datasets, CHeGCN outperforms CNNs and GCNs by nearly 3.5% and 5%, respectively. In contrast to homogeneous graphs, heterogeneous graphs have an IoU improvement of approximately 2.5% in the ablation experiments. Furthermore, the generated visualizations help explore the underlying mechanism of CHeGCN. It is worth noting that CHeGCN can be easily degenerated to scenarios where no land cover information is available and achieves satisfactory performance.<\/jats:p>","DOI":"10.3390\/rs14195027","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"5027","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8185-4945","authenticated-orcid":false,"given":"Zhi-Qiang","family":"Liu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Tang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3730-9223","authenticated-orcid":false,"given":"Weixiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4549-3502","authenticated-orcid":false,"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2012.06.003","article-title":"A review of regional science applications of satellite remote sensing in urban settings","volume":"37","author":"Patino","year":"2013","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1126\/science.277.5325.494","article-title":"Human domination of Earth\u2019s ecosystems","volume":"277","author":"Vitousek","year":"1997","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.rse.2019.04.020","article-title":"Understanding an urbanizing planet: Strategic directions for remote sensing","volume":"228","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"274","article-title":"Community detection in spatial networks: Inferring land use from a planar graph of land cover objects","volume":"18","author":"Comber","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, M., and Stein, A. (2020). Mapping land use from high resolution satellite images by exploiting the spatial arrangement of land cover objects. Remote Sens., 12.","DOI":"10.3390\/rs12244158"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1080\/00330124.2001.9628460","article-title":"From land cover to land use: A methodology for efficient land use mapping over large areas","volume":"53","author":"Cihlar","year":"2001","journal-title":"Prof. Geogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1080\/13658816.2013.865189","article-title":"From land cover-graphs to urban structure types","volume":"28","author":"Walde","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/S0198-9715(97)10001-1","article-title":"Distinguishing urban land-use categories in fine spatial resolution land-cover data using a graph-based, structural pattern recognition system","volume":"21","author":"Barnsley","year":"1997","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., Corpetti, T., and Zhao, L. (2021). WTS: A Weakly towards strongly supervised learning framework for remote sensing land cover classification using segmentation models. Remote Sens., 13.","DOI":"10.3390\/rs13030394"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1007\/s11430-018-9255-3","article-title":"GlobeLand30: Operational global land cover mapping and big-data analysis","volume":"61","author":"Chen","year":"2018","journal-title":"Sci. China Earth Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_14","unstructured":"Liu, W., Rabinovich, A., and Berg, A.C. (2015). Parsenet: Looking wider to see better. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_16","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid attention network for semantic segmentation. arXiv."},{"key":"ref_17","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., 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.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_22","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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_23","first-page":"9225","article-title":"Beyond grids: Learning graph representations for visual recognition","volume":"31","author":"Li","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, Y., Rohrbach, M., Yan, Z., Shuicheng, Y., Feng, J., and Kalantidis, Y. (2019, January 16\u201320). Graph-based global reasoning networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00052"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.isprsjprs.2007.03.003","article-title":"Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping","volume":"62","author":"Lucas","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","first-page":"1","article-title":"Graph representation learning","volume":"14","author":"Hamilton","year":"2020","journal-title":"Synth. Lect. Artifical Intell. Mach. Learn."},{"key":"ref_27","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","first-page":"6533","article-title":"Protein interface prediction using graph convolutional networks","volume":"30","author":"Fout","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rhee, S., Seo, S., and Kim, S. (2017). Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification. arXiv.","DOI":"10.24963\/ijcai.2018\/490"},{"key":"ref_30","first-page":"1222","article-title":"Beyond categories: The visual memex model for reasoning about object relationships","volume":"22","author":"Malisiewicz","year":"2009","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8657","DOI":"10.1109\/TGRS.2020.3037361","article-title":"CNN-enhanced graph convolutional network with pixel-and superpixel-level feature fusion for hyperspectral image classification","volume":"59","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/TGRS.2020.2994205","article-title":"Hyperspectral image classification with context-aware dynamic graph convolutional network","volume":"59","author":"Wan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1109\/TGRS.2019.2949180","article-title":"Multiscale dynamic graph convolutional network for hyperspectral image classification","volume":"58","author":"Wan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph convolutional networks for hyperspectral image classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cui, W., Yao, M., Hao, Y., Wang, Z., He, X., Wu, W., Li, J., Zhao, H., Xia, C., and Wang, J. (2021). Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation. Sensors, 21.","DOI":"10.3390\/s21113848"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hu, H., Ji, D., Gan, W., Bai, S., Wu, W., and Yan, J. (2020, January 23\u201328). Class-wise dynamic graph convolution for semantic segmentation. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58520-4_1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., and Yu, P.S. (2019, January 13\u201317). Heterogeneous graph attention network. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313562"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ouyang, S., and Li, Y. (2020). Combining deep semantic segmentation network and graph convolutional neural network for semantic segmentation of remote sensing imagery. Remote Sens., 13.","DOI":"10.3390\/rs13010119"},{"key":"ref_40","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":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_42","first-page":"84","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8246","DOI":"10.1109\/TGRS.2020.2973363","article-title":"Nonlocal graph convolutional networks for hyperspectral image classification","volume":"58","author":"Mou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","first-page":"20","article-title":"Graph attention networks","volume":"1050","author":"Velickovic","year":"2017","journal-title":"Stat"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","article-title":"Geometric deep learning: Going beyond euclidean data","volume":"34","author":"Bronstein","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_47","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the ICML, Haifa, Israel."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1109\/TGRS.2007.895416","article-title":"Semi-supervised graph-based hyperspectral image classification","volume":"45","author":"Marsheva","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_50","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_51","unstructured":"Loshchilov, I., and Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1111\/1467-9868.00196","article-title":"Probabilistic principal component analysis","volume":"61","author":"Tipping","year":"1999","journal-title":"J. R. Stat. Soc. Ser. (Stat. Methodol.)"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201322). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5027\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:48:36Z","timestamp":1760143716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5027"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,9]]},"references-count":53,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14195027"],"URL":"https:\/\/doi.org\/10.3390\/rs14195027","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,9]]}}}