Flexible style transfer from remote sensing images to maps
Style transfer has emerged as a prominent technique for transferring stylistic elements between images (e.g., a reference image and a map). However, current methods face two challenges when applied to create image maps, especially when the reference image and map are not spatially aligned (e.g., cov...
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002134 |
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| author | Yanjie Sun Mingguang Wu |
| author_facet | Yanjie Sun Mingguang Wu |
| author_sort | Yanjie Sun |
| collection | DOAJ |
| description | Style transfer has emerged as a prominent technique for transferring stylistic elements between images (e.g., a reference image and a map). However, current methods face two challenges when applied to create image maps, especially when the reference image and map are not spatially aligned (e.g., covering different regions). These challenges include aligning the semantic elements between maps and remote sensing images, and then balancing the photorealistic textures with cartographic symbolism to maintain cartographic quality. To address these challenges, we propose a flexible style transfer method from remote sensing images to maps, relaxing the requirement of strict spatial alignment between remote sensing images and maps. Our approach enables the generation of image maps with adjustable stylistic results, offering a balance between photorealism and symbolization. First, we analyze the semantic of the input map and the reference imagery including semantic classes and semantic relationships encoded by colors. Then we implement hierarchical control and parameter interpolation to enable style matching. We also compare the transfer results of our method to those of the baseline image style transfer methods across four aspects including visual similarity, graphic discriminability, semantic consistency, and overall readability. The evaluations show that our approach significantly enhances cartographic quality by flexibly balancing photorealism and symbolization, while offering the flexibility to generate image maps with varying preferences. |
| format | Article |
| id | doaj-art-2a43af820a034c73a86d64cec8172b4b |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-2a43af820a034c73a86d64cec8172b4b2025-08-20T02:58:30ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910456610.1016/j.jag.2025.104566Flexible style transfer from remote sensing images to mapsYanjie Sun0Mingguang Wu1Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing, Jiangsu, China; Nanjing Research Institute of Electronic Engineering, Nanjing, Jiangsu, ChinaKey Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing, Jiangsu, China; College of Geographic Sciences, Nanjing Normal University, Nanjing, Jiangsu, China; State Key Laboratory Cultivation Base of Geographic Environment Evolution (Jiangsu Province), Nanjing, Jiangsu, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu, China; Corresponding author at: Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing, Jiangsu, China.Style transfer has emerged as a prominent technique for transferring stylistic elements between images (e.g., a reference image and a map). However, current methods face two challenges when applied to create image maps, especially when the reference image and map are not spatially aligned (e.g., covering different regions). These challenges include aligning the semantic elements between maps and remote sensing images, and then balancing the photorealistic textures with cartographic symbolism to maintain cartographic quality. To address these challenges, we propose a flexible style transfer method from remote sensing images to maps, relaxing the requirement of strict spatial alignment between remote sensing images and maps. Our approach enables the generation of image maps with adjustable stylistic results, offering a balance between photorealism and symbolization. First, we analyze the semantic of the input map and the reference imagery including semantic classes and semantic relationships encoded by colors. Then we implement hierarchical control and parameter interpolation to enable style matching. We also compare the transfer results of our method to those of the baseline image style transfer methods across four aspects including visual similarity, graphic discriminability, semantic consistency, and overall readability. The evaluations show that our approach significantly enhances cartographic quality by flexibly balancing photorealism and symbolization, while offering the flexibility to generate image maps with varying preferences.http://www.sciencedirect.com/science/article/pii/S1569843225002134Image mapStyle transferDiffusion modelCartographic continuumSemantic alignment |
| spellingShingle | Yanjie Sun Mingguang Wu Flexible style transfer from remote sensing images to maps International Journal of Applied Earth Observations and Geoinformation Image map Style transfer Diffusion model Cartographic continuum Semantic alignment |
| title | Flexible style transfer from remote sensing images to maps |
| title_full | Flexible style transfer from remote sensing images to maps |
| title_fullStr | Flexible style transfer from remote sensing images to maps |
| title_full_unstemmed | Flexible style transfer from remote sensing images to maps |
| title_short | Flexible style transfer from remote sensing images to maps |
| title_sort | flexible style transfer from remote sensing images to maps |
| topic | Image map Style transfer Diffusion model Cartographic continuum Semantic alignment |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225002134 |
| work_keys_str_mv | AT yanjiesun flexiblestyletransferfromremotesensingimagestomaps AT mingguangwu flexiblestyletransferfromremotesensingimagestomaps |