Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data
Artificial intelligence (AI) began to make its way into geoinformation science several decades ago and since then has constantly brought forth new cutting-edge approaches for diverse geographic use cases. AI and deep learning methods have become essential approaches for land use and land cover (LULC...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2364460 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846139015426736128 |
|---|---|
| author | Torben Dedring Andreas Rienow |
| author_facet | Torben Dedring Andreas Rienow |
| author_sort | Torben Dedring |
| collection | DOAJ |
| description | Artificial intelligence (AI) began to make its way into geoinformation science several decades ago and since then has constantly brought forth new cutting-edge approaches for diverse geographic use cases. AI and deep learning methods have become essential approaches for land use and land cover (LULC) classifications, which are important in urban planning and regional management. While the extraction of LULC information from multispectral satellite images has been a well-studied part of past and present research, only a few studies emerged about the recovery of spectral properties from LULC information. Estimates of the spectral characteristics of LULC categories could enrich LULC forecasting models by providing necessary information to delineate vegetation indices or microclimatic parameters. We train two identical Conditional Generative Adversarial Networks (CGAN) to synthesize a multispectral Sentinel-2 image based on different combinations of open-source LULC data sets. Large-scale synthetic multispectral satellite images of the administrative region of Bonn and Rhein-Sieg in Germany are generated with a Euclidean distance-based patch-fusion method. The approach generated a realistic-looking satellite image without noticeable seams between patch borders. Based on several metrics, such as difference calculations, the spectral information divergence (SID), and the Fréchet inception distance (FID), we evaluate the resulting images. The models reach mean SIDs as low as 0.026 for urban fabrics and forests and FIDs below 90 for bands B2 and B5 showing that the CGAN is capable of synthesizing distinct synthetic features matching with features typical for respective LULC categories and manages to mimic multispectral signatures. The method used in this paper to generate large-scale synthetic multispectral satellite images can be used as an approach to support scenario-oriented sustainable urban planning. |
| format | Article |
| id | doaj-art-ff0234124c9b457eb3a83a477a5c7174 |
| institution | Kabale University |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-ff0234124c9b457eb3a83a477a5c71742024-12-06T13:51:50ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262024-12-0161110.1080/15481603.2024.2364460Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 dataTorben Dedring0Andreas Rienow1Interdisciplinary Geographic Information Sciences, Geomatics Research Group, Institute of Geography, Faculty of Geosciences, Ruhr University Bochum, Bochum, GermanyInterdisciplinary Geographic Information Sciences, Geomatics Research Group, Institute of Geography, Faculty of Geosciences, Ruhr University Bochum, Bochum, GermanyArtificial intelligence (AI) began to make its way into geoinformation science several decades ago and since then has constantly brought forth new cutting-edge approaches for diverse geographic use cases. AI and deep learning methods have become essential approaches for land use and land cover (LULC) classifications, which are important in urban planning and regional management. While the extraction of LULC information from multispectral satellite images has been a well-studied part of past and present research, only a few studies emerged about the recovery of spectral properties from LULC information. Estimates of the spectral characteristics of LULC categories could enrich LULC forecasting models by providing necessary information to delineate vegetation indices or microclimatic parameters. We train two identical Conditional Generative Adversarial Networks (CGAN) to synthesize a multispectral Sentinel-2 image based on different combinations of open-source LULC data sets. Large-scale synthetic multispectral satellite images of the administrative region of Bonn and Rhein-Sieg in Germany are generated with a Euclidean distance-based patch-fusion method. The approach generated a realistic-looking satellite image without noticeable seams between patch borders. Based on several metrics, such as difference calculations, the spectral information divergence (SID), and the Fréchet inception distance (FID), we evaluate the resulting images. The models reach mean SIDs as low as 0.026 for urban fabrics and forests and FIDs below 90 for bands B2 and B5 showing that the CGAN is capable of synthesizing distinct synthetic features matching with features typical for respective LULC categories and manages to mimic multispectral signatures. The method used in this paper to generate large-scale synthetic multispectral satellite images can be used as an approach to support scenario-oriented sustainable urban planning.https://www.tandfonline.com/doi/10.1080/15481603.2024.2364460Synthetic satellite imagesGenerative Adversarial NetworkSentinel-2patch fusionDeep Fake Geography |
| spellingShingle | Torben Dedring Andreas Rienow Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data GIScience & Remote Sensing Synthetic satellite images Generative Adversarial Network Sentinel-2 patch fusion Deep Fake Geography |
| title | Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data |
| title_full | Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data |
| title_fullStr | Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data |
| title_full_unstemmed | Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data |
| title_short | Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data |
| title_sort | synthesis and evaluation of seamless large scale multispectral satellite images using generative adversarial networks on land use and land cover and sentinel 2 data |
| topic | Synthetic satellite images Generative Adversarial Network Sentinel-2 patch fusion Deep Fake Geography |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2024.2364460 |
| work_keys_str_mv | AT torbendedring synthesisandevaluationofseamlesslargescalemultispectralsatelliteimagesusinggenerativeadversarialnetworksonlanduseandlandcoverandsentinel2data AT andreasrienow synthesisandevaluationofseamlesslargescalemultispectralsatelliteimagesusinggenerativeadversarialnetworksonlanduseandlandcoverandsentinel2data |