{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T22:57:23Z","timestamp":1773269843173,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Melbourne"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The evolving FCNs suffer from the inadequate use of multi-scale feature maps in their backbone of convolutional neural networks (CNNs). Furthermore, the DL methods are not robust in cross-domain settings due to domain-shift problems. Two scale-robust novel networks, namely MSA-UNET and MSA-ResUNET, are developed in this study by aggregating the multi-scale feature maps in U-Net and ResUNET with partial concepts of the feature pyramid network (FPN). Furthermore, supervised domain adaptation is investigated to minimise the effects of domain-shift between the two datasets. The datasets include the benchmark WHU Building dataset and a developed dataset with 5\u00d7 fewer samples, 4\u00d7 lower spatial resolution and complex high-rise buildings and skyscrapers. The newly developed networks are compared to six state-of-the-art FCNs using five metrics: pixel accuracy, adjusted accuracy, F1 score, intersection over union (IoU), and the Matthews Correlation Coefficient (MCC). The proposed networks outperform the FCNs in the majority of the accuracy measures in both datasets. Compared to the larger dataset, the network trained on the smaller one shows significantly higher robustness in terms of adjusted accuracy (by 18%), F1 score (by 31%), IoU (by 27%), and MCC (by 29%) during the cross-domain validation of MSA-UNET. MSA-ResUNET shows similar improvements, concluding that the proposed networks when trained using domain adaptation increase the robustness and minimise the domain-shift between the datasets of different complexity.<\/jats:p>","DOI":"10.3390\/rs15020488","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T04:06:47Z","timestamp":1673842007000},"page":"488","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Multi-Scale Feature Map Aggregation and Supervised Domain Adaptation of Fully Convolutional Networks for Urban Building Footprint Extraction"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2127","authenticated-orcid":false,"given":"Jagannath","family":"Aryal","sequence":"first","affiliation":[{"name":"Department of Infrastructure Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5331-9897","authenticated-orcid":false,"given":"Bipul","family":"Neupane","sequence":"additional","affiliation":[{"name":"Department of Infrastructure Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne, VIC 3010, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Neupane, B., Horanont, T., and Aryal, J. (2021). Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040808"},{"key":"ref_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1109\/TGRS.2019.2954461","article-title":"Toward automatic building footprint delineation from aerial images using CNN and regularization","volume":"58","author":"Wei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","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 Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set","volume":"57","author":"Ji","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1109\/TGRS.2005.843569","article-title":"Use of the Bradley-Terry model to quantify association in remotely sensed images","volume":"43","author":"Stein","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shadman Roodposhti, M., Aryal, J., Lucieer, A., and Bryan, B.A. (2019). Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest. Entropy, 21.","DOI":"10.3390\/e21010078"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Neupane, B., Horanont, T., Duy, H.N., Suebvong, S., and Mahattanawutakorn, T. (2019, January 7\u201311). An Open-Source UAV Image Processing Web Service for Crop Health Monitoring. Proceedings of the 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI), Toyama, Japan.","DOI":"10.1109\/IIAI-AAI.2019.00014"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Neupane, B., Horanont, T., and Hung, N.D. (2019). Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV). PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0223906"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Neupane, B., Horanont, T., and Aryal, J. (2022). Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network. Sensors, 22.","DOI":"10.3390\/s22103813"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1080\/13658816.2019.1624761","article-title":"A locally-constrained yolo framework for detecting small and densely-distributed building footprints","volume":"34","author":"Xie","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Aryal, J., and Dutta, R. (2015, January 13\u201317). Smart city and geospatiality: Hobart deeply learned. Proceedings of the 2015 31st IEEE International Conference on Data Engineering Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICDEW.2015.7129557"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_18","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_19","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_21","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (\u2013, 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_22","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mnih, V., and Hinton, G.E. (2010, January 5\u201311). Learning to detect roads in high-resolution aerial images. Proceedings of the European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"ref_25","unstructured":"Mnih, V. (2013). Machine Learning for Aerial Image Labeling. [Ph.D. Thesis, University of Toronto]."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Saito, S., and Aoki, Y. (2015, January 8\u201312). Building and road detection from large aerial imagery. Proceedings of the Image Processing: Machine Vision Applications VIII. International Society for Optics and Photonics, San Francisco, CA, USA.","DOI":"10.1117\/12.2083273"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2352\/ISSN.2470-1173.2016.10.ROBVIS-392","article-title":"Multiple object extraction from aerial imagery with convolutional neural networks","volume":"2016","author":"Saito","year":"2016","journal-title":"Electron. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vakalopoulou, M., Karantzalos, K., Komodakis, N., and Paragios, N. (2015, January 26\u201331). Building detection in very high resolution multispectral data with deep learning features. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326158"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Cui, W., and Jiang, H. (2016, January 10\u201315). Fully convolutional networks for building and road extraction: Preliminary results. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729406"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","unstructured":"Marcu, A.E., and Leordeanu, M. (2017, January 4\u20139). Object contra context: Dual local-global semantic segmentation in aerial images. Proceedings of the Workshops at the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhao, K., Kang, J., Jung, J., and Sohn, G. (2018, January 18\u201322). Building extraction from satellite images using mask R-CNN with building boundary regularization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00045"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2600","DOI":"10.1109\/JSTARS.2018.2835377","article-title":"Building extraction at scale using convolutional neural network: Mapping of the united states","volume":"11","author":"Yang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2019.05.013","article-title":"Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours","volume":"154","author":"Griffiths","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, M., Gao, F., Dong, J., and Qi, L. (2022, January 17\u201322). Multi-Scale Feature Fusion for Hyperspectral and Lidar Data Joint Classification. Proceedings of the IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884168"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"881021","DOI":"10.3389\/fnbot.2022.881021","article-title":"Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection","volume":"16","author":"Huang","year":"2022","journal-title":"Front. Neurorobot."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.W., and Wu, J. (2020, January 4\u20138). Unet 3+: A full-scale connected unet for medical image segmentation. Proceedings of the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, W., He, C., Fang, J., Zheng, J., Fu, H., and Yu, L. (2019). Semantic segmentation-based building footprint extraction using very high-resolution satellite images and multi-source GIS data. Remote Sens., 11.","DOI":"10.3390\/rs11040403"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yi, Y., Zhang, Z., Zhang, W., Zhang, C., Li, W., and Zhao, T. (2019). Semantic segmentation of urban buildings from vhr remote sensing imagery using a deep convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11151774"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bischke, B., Helber, P., Folz, J., Borth, D., and Dengel, A. (2019, January 22\u201325). Multi-task learning for segmentation of building footprints with deep neural networks. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803050"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Qin, Y., Wu, Y., Li, B., Gao, S., Liu, M., and Zhan, Y. (2019). Semantic segmentation of building roof in dense urban environment with deep convolutional neural network: A case study using GF2 VHR imagery in China. Sensors, 19.","DOI":"10.3390\/s19051164"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., and Alamri, A.M. (2020). An Ensemble Architecture of Deep Convolutional Segnet and Unet Networks for Building Semantic Segmentation from High-resolution Aerial Images. Geocarto Int., 1\u201313.","DOI":"10.1080\/10106049.2020.1856199"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pan, Z., Xu, J., Guo, Y., Hu, Y., and Wang, G. (2020). Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net. Remote Sens., 12.","DOI":"10.3390\/rs12101574"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"138","DOI":"10.26833\/ijeg.645426","article-title":"Feature Extraction from Satellite Images Using Segnet and Fully Convolutional Networks (FCN)","volume":"5","author":"Sariturk","year":"2020","journal-title":"Int. J. Eng. Geosci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ayala, C., Sesma, R., Aranda, C., and Galar, M. (2021). A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13163135"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"114219","DOI":"10.1016\/j.eswa.2020.114219","article-title":"Visual saliency detection by integrating spatial position prior of object with background cues","volume":"168","author":"Jian","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yang, D., Liu, G., Ren, M., Xu, B., and Wang, J. (2020). A multi-scale feature fusion method based on U-Net for retinal vessel segmentation. Entropy, 22.","DOI":"10.3390\/e22080811"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"639930","DOI":"10.3389\/fgene.2021.639930","article-title":"MSU-net: Multi-scale U-net for 2D medical image segmentation","volume":"12","author":"Su","year":"2021","journal-title":"Front. Genet."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"11968","DOI":"10.1038\/s41598-022-16329-6","article-title":"Multi-scale feature progressive fusion network for remote sensing image change detection","volume":"12","author":"Lu","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yang, H., Wu, P., Yao, X., Wu, Y., Wang, B., and Xu, Y. (2018). Building extraction in very high resolution imagery by dense-attention networks. Remote Sens., 10.","DOI":"10.3390\/rs10111768"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Wu, G., Shao, X., Guo, Z., Chen, Q., Yuan, W., Shi, X., Xu, Y., and Shibasaki, R. (2018). Automatic building segmentation of aerial imagery using multi-constraint fully convolutional networks. Remote Sens., 10.","DOI":"10.3390\/rs10030407"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, L., Wu, Y., Wu, G., Guo, Z., and Waslander, S.L. (2018). Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings. arXiv.","DOI":"10.1016\/j.isprsjprs.2018.11.011"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3308","DOI":"10.1080\/01431161.2018.1528024","article-title":"A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery","volume":"40","author":"Ji","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","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_58","first-page":"121","article-title":"The influence of pattern similarity and transfer learning upon training of a base perceptron B2","volume":"Volume 3","author":"Bozinovski","year":"1976","journal-title":"Proceedings of the Symposium Informatica"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Saha, A., Rai, P., Daum\u00e9, H., Venkatasubramanian, S., and DuVall, S.L. (2011, January 5\u20139). Active supervised domain adaptation. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Athens, Greece.","DOI":"10.1007\/978-3-642-23808-6_7"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Motiian, S., Piccirilli, M., Adjeroh, D.A., and Doretto, G. (2017, January 22\u201329). Unified deep supervised domain adaptation and generalization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.609"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Panboonyuen, T., Jitkajornwanich, K., Lawawirojwong, S., Srestasathiern, P., and Vateekul, P. (2019). Semantic segmentation on remotely sensed images using an enhanced global convolutional network with channel attention and domain specific transfer learning. Remote Sens., 11.","DOI":"10.20944\/preprints201812.0090.v3"},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_65","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 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Liu, W., Su, F., Jin, X., Li, H., and Qin, R. (2020). Bispace Domain Adaptation Network for Remotely Sensed Semantic Segmentation. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2020.3035561"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"173","DOI":"10.5194\/isprs-annals-X-4-W3-2022-173-2022","article-title":"Building Footprint Segmentation using Transfer Learning: A case study of the City of Melbourne","volume":"10","author":"Neupane","year":"2022","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018, January 4\u20137). A survey on deep transfer learning. Proceedings of the International Conference on Artificial Neural Networks, Rhodes, Greece.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Taormina, V., Cascio, D., Abbene, L., and Raso, G. (2020). Performance of fine-tuning convolutional neural networks for HEP-2 image classification. Appl. Sci., 10.","DOI":"10.3390\/app10196940"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M. (2017). Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/488\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:33Z","timestamp":1760119533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/488"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,13]]},"references-count":71,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020488"],"URL":"https:\/\/doi.org\/10.3390\/rs15020488","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,13]]}}}