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Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote sensing.
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Ronneberger, O., Fischer, P., Brox, T.: ”U-Net: Convolutional Networks for Biomedical Image Segmentation”. In: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234–241 (2015)
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Yang, L., Tian, S., Yu, L., Ye, F., Qian, J., Qian, Y.: Deep Learning for Extracting Water Body From Landsat Imagery. International Journal of Innovative Computing, Information and Control 11
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234–241 (2015)
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Gong, M., Zhao, J., Liu, J., Miao, Q., Jiao, L.: Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks. IEEE Transactions on Neural Networks and Learning Systems 27
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Claverie, M., Masek, J.G., Ju, J., Dungan, J.L.: Harmonized Landsat-8 Sentinel-2 (HLS) Product User’s Guide. National Aeronautics and Space Administration (NASA): Washington, DC, USA (2017)
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Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-Image Translation With Conditional Adversarial Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
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Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L.: DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3974–3983 (2018)
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Claverie, M., Ju, J., Masek, J.G., Dungan, J.L., Vermote, E.F., Roger, J.-C., Skakun, S.V., Justice, C.: The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set. Remote Sensing of Environment 219
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Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A.: Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review. ISPRS Journal of Photogrammetry and Remote Sensing 152
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Du, Z., Yang, J., Ou, C., Zhang, T.: Smallholder Crop Area Mapped With a Semantic Segmentation Deep Learning Method. Remote Sensing 11
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Zhou, Q., Rover, J., Brown, J., Worstell, B., Howard, D., Wu, Z., Gallant, A.L., Rundquist, B., Burke, M.: Monitoring Landscape Dynamics in Central Us Grasslands With Harmonized Landsat-8 and Sentinel-2 Time Series Data. Remote Sensing 11
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Alemohammad, H., Maskey, M., Estes, L., Gentine, P., Lunga, D., Fang, Z.: Advancing Application of Machine Learning Tools for NASA’s Earth Observation Data (2020). https://www.earthdata.nasa.gov/s3fs-public/imported/NASA_ML_Workshop_Report.pdf
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Bar, S., Parida, B.R., Pandey, A.C.: Landsat-8 and Sentinel-2 Based Forest Fire Burn Area Mapping Using Machine Learning Algorithms on GEE Cloud Platform Over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment 18
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Brovkina, O., Stojanović, M., Milanović, S., Latypov, I., Marković, N., Cienciala, E.: Monitoring of Post-Fire Forest Scars in Serbia Based on Satellite Sentinel-2 Data. Geomatics, Natural Hazards and Risk 11
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Dimitris, S., Thomas, K., Chara, M., Ioannis, Z.G.: Automated Burned Scar Mapping Using Sentinel-2 Imagery. Journal of Geographic Information System 12
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Griffiths, P., Nendel, C., Pickert, J., Hostert, P.: Towards National-Scale Characterization of Grassland Use Intensity From Integrated Sentinel-2 and Landsat Time Series. Remote Sensing of Environment 238
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Bonafilia, D., Tellman, B., Anderson, T., Issenberg, E.: Sen1Floods11: A Georeferenced Dataset to Train and Test Deep Learning Flood Algorithms for Sentinel-1. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 210–211 (2020)
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Hu, X., Ban, Y., Nascetti, A.: Uni-Temporal Multispectral Imagery for Burned Area Mapping With Deep Learning. Remote Sensing 13
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Zhang, P., Ban, Y., Nascetti, A.: Learning U-Net Without Forgetting for Near Real-Time Wildfire Monitoring by the Fusion of SAR and Optical Time Series. Remote Sensing of Environment 261
2021
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Kearney, S.P., Porensky, L.M., Augustine, D.J., Gaffney, R., Derner, J.D.: Monitoring Standing Herbaceous Biomass and Thresholds in Semiarid Rangelands From Harmonized Landsat 8 and Sentinel-2 Imagery to Support Within-Season Adaptive Management. Remote Sensing of Environment 271
2022
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Zhu, Z., Ye, S.: AI satellite mapping can quickly pinpoint hurricane damage across an entire state to spot where people may be trapped. https://phys.org/news/2022-10-ai-satellite-quickly-hurricane-entire.html
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Freitag, M.O., Albrecht, C.M., Marianno, F.J., Lu, S., Hamann, H.F., Schmude, J.W.: Efficient Querying Using Overview Layers of Geospatial-Temporal Data in a Data Analytics Platform. Google Patents. US Patent 11,360,970 (2022)
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He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked Autoencoders Are Scalable Vision Learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009 (2022)
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Arruda, V.L.S., Piontekowski, V.J., Alencar, A., Pereira, R.S., Matricardi, E.A.T.: An Alternative Approach for Mapping Burn Scars Using Landsat Imagery, Google Earth Engine, and Deep Learning in the Brazilian Savanna. Remote Sensing Applications: Society and Environment 22
2021
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Tuyen, D.N., Tuan, T.M., Son, L.H., Ngan, T.T., Giang, N.L., Thong, P.H., Hieu, V.V., Gerogiannis, V.C., Tzimos, D., Kanavos, A.: A Novel Approach Combining Particle Swarm Optimization and Deep Learning for Flash Flood Detection From Satellite Images. Mathematics 9
2021
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Zhang, L., Xia, J.: Flood Detection Using Multiple Chinese Satellite Datasets During 2020 China Summer Floods. Remote Sensing 14
2021
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Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C.: HLS Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30 M v2.0. NASA EOSDIS Land Processes DAAC (2021)
2021
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Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C.: HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. NASA EOSDIS Land Processes DAAC (2021)
2021
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Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
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Yu, J., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., Wu, Y.: CoCa: Contrastive Captioners Are Image-Text Foundation Models. Transactions on Machine Learning Research (2022)
2022
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Wang, J., Chen, D., Wu, Z., Luo, C., Zhou, L., Zhao, Y., Xie, Y., Liu, C., Jiang, Y.-G., Yuan, L.: OmniVL: One Foundation Model for Image-Language and Video-Language Tasks. Advances in Neural Information Processing Systems 35
2022
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2022
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2022
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Feichtenhofer, C., fan, h., Li, Y., He, K.: Masked Autoencoders as Spatiotemporal Learners. In: Advances in Neural Information Processing Systems, vol. 35, pp. 35946–35958 (2022)
2022
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Baier, G., Deschemps, A., Schmitt, M., Yokoya, N.: Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data. IEEE Transactions on Geoscience and Remote Sensing 60
2022
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2023
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2023
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Sun, X., Wang, P., Lu, W., Zhu, Z., Lu, X., He, Q., Li, J., Rong, X., Yang, Z., Chang, H., He, Q., Yang, G., Wang, R., Lu, J., Fu, K.: RingMo: A Remote Sensing Foundation Model With Masked Image Modeling. IEEE Transactions on Geoscience and Remote Sensing 61
2023
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2023
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2023
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2023
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Aleissaee, A.A., Kumar, A., Anwer, R.M., Khan, S., Cholakkal, H., Xia, G.-S., Khan, F.S.: Transformers in Remote Sensing: A Survey. Remote Sensing 15
2023
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Jakubik, J., Muszynski, M., Vössing, M., Kühl, N., Brunschwiler, T.: Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation. In: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5638–5641 (2023)
2023
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2023
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Mohammadi, S., Belgiu, M., Stein, A.: Improvement in Crop Mapping From Satellite Image Time Series by Effectively Supervising Deep Neural Networks. ISPRS Journal of Photogrammetry and Remote Sensing 198
2023
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Khan, A.H., Zafar, Z., Shahzad, M., Berns, K., Fraz, M.M.: Crop Type Classification Using Multi-Temporal Sentinel-2 Satellite Imagery: A Deep Semantic Segmentation Approach. In: Proceedings of the International Conference on Robotics and Automation in Industry (ICRAI), pp. 1–6 (2023)
2023
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