{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:39:08Z","timestamp":1772822348246,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:00:00Z","timestamp":1728172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42471403"],"award-info":[{"award-number":["42471403"]}]},{"name":"National Natural Science Foundation of China","award":["42101435"],"award-info":[{"award-number":["42101435"]}]},{"name":"National Natural Science Foundation of China","award":["42101432"],"award-info":[{"award-number":["42101432"]}]},{"name":"National Natural Science Foundation of China","award":["62106276"],"award-info":[{"award-number":["62106276"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Online maps are of great importance in modern life, especially in commuting, traveling and urban planning. The accessibility of remote sensing (RS) images has contributed to the widespread practice of generating online maps based on RS images. The previous works leverage an idea of domain mapping to achieve end-to-end remote sensing image-to-map translation (RSMT). Although existing methods are effective and efficient for online map generation, generated online maps still suffer from ground features distortion and boundary inaccuracy to a certain extent. Recently, the emergence of diffusion models has signaled a significant advance in high-fidelity image synthesis. Based on rigorous mathematical theories, denoising diffusion models can offer controllable generation in sampling process, which are very suitable for end-to-end RSMT. Therefore, we design a novel end-to-end diffusion model to generate online maps directly from remote sensing images, called MapGen-Diff. We leverage a strategy inspired by Brownian motion to make a trade-off between the diversity and the accuracy of generation process. Meanwhile, an image compression module is proposed to map the raw images into the latent space for capturing more perception features. In order to enhance the geometric accuracy of ground features, a consistency regularization is designed, which allows the model to generate maps with clearer boundaries and colorization. Compared to several state-of-the-art methods, the proposed MapGen-Diff achieves outstanding performance, especially a 5% RMSE and 7% SSIM improvement on Los Angeles and Toronto datasets. The visualization results also demonstrate more accurate local details and higher quality.<\/jats:p>","DOI":"10.3390\/rs16193716","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T07:30:18Z","timestamp":1728286218000},"page":"3716","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MapGen-Diff: An End-to-End Remote Sensing Image to Map Generator via Denoising Diffusion Bridge Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5103-1669","authenticated-orcid":false,"given":"Jilong","family":"Tian","sequence":"first","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Jiangjiang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7880-3394","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7510-5638","authenticated-orcid":false,"given":"Mengyu","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"ref_1","unstructured":"Ablameyko, S.V., Beregov, B.S., and Kryuchkov, A.N. (1993, January 20\u201322). Computer-aided cartographical system for map digitizing. Proceedings of the 2nd International Conference on Document Analysis and Recognition (ICDAR\u201993), Tsukuba, Japan."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded recurrent neural networks for hyperspectral image classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., and Poole, B. (2020). Score-based generative modeling through stochastic differential equations. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1080\/13658816.2018.1542697","article-title":"Automated terrain feature identification from remote sensing imagery: A deep learning approach","volume":"34","author":"Li","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8819","DOI":"10.1109\/TGRS.2020.2991006","article-title":"Topology-enhanced urban road extraction via a geographic feature-enhanced network","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","first-page":"1","article-title":"TAL: Topography-aware multi-resolution fusion learning for enhanced building footprint extraction","volume":"19","author":"Wu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.isprsjprs.2021.12.007","article-title":"CMGFNet: A deep cross-modal gated fusion network for building extraction from very high-resolution remote sensing images","volume":"184","author":"Hosseinpour","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lin, S., Ding, L., and Bruzzone, L. (2020). Multi-scale context aggregation for semantic segmentation of remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12040701"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_10","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_13","unstructured":"Ganguli, S., Garzon, P., and Glaser, N. (2019). GeoGAN: A conditional GAN with reconstruction and style loss to generate standard layer of maps from satellite images. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Song, J., Li, J., Chen, H., and Wu, J. (2022). RSMT: A Remote Sensing Image-to-Map Translation Model via Adversarial Deep Transfer Learning. Remote Sens., 14.","DOI":"10.3390\/rs14040919"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhou, X., Han, C., Dong, B., and Li, H. (2023). SAM-GAN: Supervised learning-based aerial image-to-map translation via generative adversarial networks. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12040159"},{"key":"ref_16","unstructured":"Phatangare, S., Khalifa, M.M., Kharche, S., Khatib, A., and Kshirsagar, A. (2024). Satellite Image to Map Translation using GANs. Grenze Int. J. Eng. Technol. (GIJET), 10."},{"key":"ref_17","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/JSTARS.2021.3049905","article-title":"MapGen-GAN: A Fast Translator for Remote Sensing Image to Map Via Unsupervised Adversarial Learning","volume":"14","author":"Song","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","unstructured":"Xiao, Z., Kreis, K., and Vahdat, A. (2021). Tackling the generative learning trilemma with denoising diffusion gans. arXiv."},{"key":"ref_20","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, B., Xue, K., Liu, B., and Lai, Y. (2022, January 17\u201324). BBDM: Image-to-Image Translation with Brownian Bridge Diffusion Models. Proceedings of the 2023 IEEE\/CVF Confernece Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00194"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 18\u201324). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_23","unstructured":"Zhu, J.Y., Zhang, R., Pathak, D., Darrell, T., Efros, A.A., Wang, O., and Shechtman, E. (2017). Toward multimodal image-to-image translation. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhang, H., Tan, P., and Gong, M. (2017, January 22\u201329). DualGAN: Unsupervised Dual Learning for Image-to-Image Translation. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref_25","unstructured":"Kim, T., Cha, M., Kim, H., Lee, J.K., and Kim, J. (2017, January 6\u201311). Learning to discover cross-domain relations with generative adversarial networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4388","DOI":"10.1109\/TGRS.2020.3021819","article-title":"SMAPGAN: Generative Adversarial Network-Based Semisupervised Styled Map Tile Generation Method","volume":"59","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"1","article-title":"Semi-MapGen: Translation of Remote Sensing Image Into Map via Semisupervised Adversarial Learning","volume":"61","author":"Song","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., and Norouzi, M. (2022, January 7\u201311). Palette: Image-to-image diffusion models. Proceedings of the ACM SIGGRAPH 2022 Conference Proceedings, Vancouver, BC, Canada.","DOI":"10.1145\/3528233.3530757"},{"key":"ref_29","unstructured":"Wang, T., Zhang, T., Zhang, B., Ouyang, H., Chen, D., Chen, Q., and Wen, F. (2022). Pretraining is all you need for image-to-image translation. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., and Agrawala, M. (2023, January 1\u20136). Adding conditional control to text-to-image diffusion models. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3554729","article-title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","volume":"56","author":"Yang","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_32","unstructured":"Nichol, A.Q., and Dhariwal, P. (2021, January 18\u201324). Improved denoising diffusion probabilistic models. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_33","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015, January 6\u201311). Deep unsupervised learning using nonequilibrium thermodynamics. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_34","unstructured":"Song, Y., and Ermon, S. (2019). Generative modeling by estimating gradients of the data distribution. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_35","first-page":"12438","article-title":"Improved techniques for training score-based generative models","volume":"33","author":"Song","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","first-page":"1415","article-title":"Maximum likelihood training of score-based diffusion models","volume":"34","author":"Song","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Doob, J.L., and Doob, J. (1984). Classical Potential Theory and Its Probabilistic Counterpart, Springer.","DOI":"10.1007\/978-1-4612-5208-5"},{"key":"ref_38","unstructured":"Liu, X., Wu, L., Ye, M., and Liu, Q. (2022). Let us build bridges: Understanding and extending diffusion generative models. arXiv."},{"key":"ref_39","unstructured":"Zhou, L., Lou, A., Khanna, S., and Ermon, S. (2023). Denoising Diffusion Bridge Models. arXiv."},{"key":"ref_40","first-page":"17695","article-title":"Diffusion schr\u00f6dinger bridge with applications to score-based generative modeling","volume":"34","author":"Thornton","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_41","first-page":"4713","article-title":"Image super-resolution via iterative refinement","volume":"45","author":"Saharia","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","unstructured":"Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M. (2021). Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Parmar, G., Singh, K.K., Zhang, R., Li, Y., Lu, J., and Zhu, J.Y. (2023, January 6\u201310). Zero-shot Image-to-Image Translation. Proceedings of the ACM SIGGRAPH 2023 Conference Proceedings, Los Angeles, CA, USA.","DOI":"10.1145\/3588432.3591513"},{"key":"ref_44","unstructured":"Sasaki, H., Willcocks, C.G., and Breckon, T.P. (2021). Unit-ddpm: Unpaired image translation with denoising diffusion probabilistic models. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S. (2021, January 10\u201317). Ilvr: Conditioning method for denoising diffusion probabilistic models. Proceedings of the CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01410"},{"key":"ref_46","unstructured":"Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., and Ermon, S. (2021). Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv."},{"key":"ref_47","first-page":"3609","article-title":"Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations","volume":"35","author":"Zhao","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wu, C.H., and De la Torre, F. (2023, January 2\u20136). A latent space of stochastic diffusion models for zero-shot image editing and guidance. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00678"},{"key":"ref_49","unstructured":"Sun, S., Wei, L., Xing, J., Jia, J., and Tian, Q. (2023, January 23\u201329). SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation. Proceedings of the 40th International Conference on Machine Learning, ICML 2023, Honolulu, HI, USA. PMLR, 2023."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., and Ommer, B. (2021, January 20\u201325). Taming transformers for high-resolution image synthesis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Mustafa, A., and Mantiuk, R.K. (2020, January 23\u201328). Transformation consistency regularization\u2013a semi-supervised paradigm for image-to-image translation. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XVIII 16.","DOI":"10.1007\/978-3-030-58523-5_35"},{"key":"ref_52","unstructured":"Laine, S., and Aila, T. (2016). Temporal ensembling for semi-supervised learning. arXiv."},{"key":"ref_53","unstructured":"Park, T., Efros, A.A., Zhang, R., and Zhu, J.Y. (2020, January 23\u201328). Contrastive learning for unpaired image-to-image translation. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part IX 16."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_55","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)?\u2013Arguments against avoiding RMSE in the literature","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci. Model Dev."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Fu, H., Gong, M., Wang, C., Batmanghelich, K., Zhang, K., and Tao, D. (2019, January 15\u201320). Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00253"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Solano-Carrillo, E., Rodriguez, A.B., Carrillo-Perez, B., Steiniger, Y., and Stoppe, J. (2023, January 17\u201324). Look ATME: The Discriminator Mean Entropy Needs Attention. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00086"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Jones, C.B. (2014). Geographical Information Systems and Computer Cartography, Routledge.","DOI":"10.4324\/9781315846231"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3716\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:11:36Z","timestamp":1760112696000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3716"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,6]]},"references-count":60,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193716"],"URL":"https:\/\/doi.org\/10.3390\/rs16193716","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,6]]}}}