УДК 004.8, 004.62

М.С. ЛАВРЕНЮК,
Інститут космічних досліджень НАН України та ДКА України, Київ, Україна,
 nick_93@ukr.net

Л.Л. ШУМІЛО,
Університет Меріленду, Коледж Парк, США,  shumilo.leonid@gmail.com

Б.Я. ЯЙЛИМОВ,
Інститут космічних досліджень НАН України та ДКА України, Київ, Україна,
 yailymov@gmail.com

Н.М. КУССУЛЬ,
Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського», Київ, Україна,  nataliia.kussul@gmail.com

ОГЛЯД МЕТОДІВ ГЛИБИННОГО НАВЧАННЯ У ПРИКЛАДНИХ ЗАДАЧАХ
ЕКОНОМІЧНОГО МОНІТОРИНГУ НА ОСНОВІ ГЕОПРОСТОРОВИХ ДАНИХ

СПИСОК ЛІТЕРАТУРИ

1. Sohnesen T.P., Fisker P., Malmgren-Hansen D. Using satellite data to guide urban poverty reduction. Review of Income and Wealth. 2021. doi.org/10.1111/roiw.12552.


2. Michael Y., Lensky I.M., Brenner S., Tchetchik A., Tessler N., Helman D. Economic assessment of fire damage to urban forest in the wildland-urban interface using planet satellites constellation images. Remote Sensing. 2018. Vol. 10, Iss. 9. 1479. doi.org/10.3390/rs10091479.


3. Bilokonska Y., Yailymova H., Yailymov B., Shelestov A., Shumilo L., Lavreniuk M. Losses assessment for winter crops based on satellite data and fuzzy logic. Proc. 2020 IEEE 5th International Symposium on Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS) (17–18 September 2020, Dortmund, Germany). Dortmund, 2020. P. 1–5.


4. Merz B., Kreibich H., Schwarze R., Thieken A. Assessment of economic flood damage. Natural Hazards and Earth System Sciences (NHESS). 2010. Vol. 10, Iss. 8. P. 1697–1724. doi.org/10.5194/nhess-10-1697-2010.


5. Lelkes M., Csornai G. & Wirnhardt C. Natural disaster monitoring by remote sensing in Hungary: waterlogging and floods in the 1998-2001 period. In: Observing Our Environment from Space. Begni G. (Ed.). London: CRC Press, 2002. P. 259–264.


6. Tralli D.M., Blom R.G., Zlotnicki V., Donnellan A., Evans D.L. Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS Journal of Photogrammetry and Remote Sensing. 2005. Vol. 59, Iss. 4. P. 185–198. doi.org/10.1016/j.isprsjprs.2005.02.002.


7. Martinez L.R. How much should we trust the dictator’s GDP growth estimates? SSRN Electronic Journal. 2019. doi.org/10.2139/ssrn.3093296.


8. Kussul N., Deininger K., Shumilo L., Lavreniuk M., Ali D.A., Nivievskyi O. Biophysical impact of sunflower crop rotation on agricultural fields. Sustainability. 2022. Vol. 14, N 7. 3965. doi.org/10.3390/su14073965.


9. Shumilo L., Lavreniuk M., Skakun S., Kussul N. Is soil bonitet an adequate indicator for agricultural land appraisal in Ukraine? Sustainability. 2021. Vol. 13, N 21. 12096. doi.org/10.3390/su132112096.


10. Skakun S., Justice C.J., Kussul N., Shelestov A., Lavreniuk M. Satellite data reveal cropland losses in South-Eastern Ukraine under military conflict. Frontiers in Earth Science. 2019. Vol. 7. doi.org/10.3389/feart.2019.00305.


11. Efremova N. et al. Prediction of soil moisture content based on satellite data and sequenceto-sequence networks. arXiv preprint arXiv:1907.03697. 2019. doi.org/10.48550/arXiv.1907.03697 .


12. Reichstein M., Camps-Valls G., Stevens B., Jung M., Denzler J., Carvalhais N., Prabhat. Deep learning and process understanding for data-driven Earth system science. Nature. 2019. Vol. 566 (7743). P. 195–204. doi.org/10.1038/s41586-019-0912-1.


13. Chen H., He X., Qing L., Wu Y., Ren C., Sheriff R.E., Zhu C. Real-world single image super-resolution: A brief review. Information Fusion. 2022. Vol. 79. P. 124–145. doi.org/10.1016/j.inffus.2021.09.005 .


14. Bellavia F., Fanfani M., Colombo C., Piva A. Experiencing with electronic image stabilization and PRNU through scene content image registration. Pattern Recognition Letters. 2021. Vol. 145. P. 8–15. doi.org/10.1016/j.patrec.2021.01.014 .


15. Nadif M., Role F. Unsupervised and self-supervised deep learning approaches for biomedical text mining. Briefings in Bioinformatics. 2021. Vol. 22, Iss. 2. P. 1592–1603. doi.org/10.1093/bib/bbab016.


16. Chaiani M., Selouani S.A., Boudraa M., Yakoub M.S. Voice disorder classification using speech enhancement and deep learning models. Biocybernetics and Biomedical Engineering. 2022. Vol. 42, Iss. 2. P. 463–480. doi.org/10.1016/j.bbe.2022.03.002.


17. Lim, B., Son S., Kim H., Nah S., Lee K.M. Enhanced deep residual networks for single image super-resolution. Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (21–26 July 2017, Honolulu, HI, USA). Honolulu, 2017. P. 136–144. doi.org/10.1109/CVPRW.2017.151.


18. Ruwurm M., Krner M. Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satellite images. Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (21–26 July 2017, Honolulu, HI, USA). Honolulu, 2017. P. 11–19. doi.org/10.1109/ CVPRW.2017.193.


19. Jiang H., Lu N., Qin J., Tang W., Yao L. A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data. Renewable and Sustainable Energy Reviews. 2019. Vol. 114. 109327. doi.org/10.1016/j.rser.2019.109327 .


20. Li X., Song W., Lian L., Wei X. Forest fire smoke detection using back-propagation neural network based on MODIS data. Remote Sensing. 2015. Vol. 7, N 4. P. 4473–4498. doi.org/10.3390/rs70404473.


21. Jianchao Y., Huang T. Image super-resolution: Historical overview and future challenges. In: Super-resolution imaging. Milanfar P. (Ed.). Boca Ratorn: CRC Press, 2011. P. 20–34. doi.org/10.1201/9781439819319-1.


22. Zhang J., Wang Z., Zheng Y., Zhang G. Cascade convolutional neural network for image super-resolution. arXiv:2008.10329. 2020. P. 1–12. doi.org/10.48550/arXiv.2008.10329.


23. Yang J., Wright J., Huang T.S., Ma Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing. 2010. Vol. 19, Iss. 11. P. 2861–2873. doi.org/10.1109/TIP.2010.2050625.


24. Dong C., Loy C.C., He K., Tang, X. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2016. Vol. 38, Iss. 2. P. 295–307. doi.org/10.1109/TPAMI.2015.2439281.


25. Zhihao W., Chen J., Hoi S.C.H. Deep learning for image super-resolution: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021. Vol. 43, Iss. 10. P. 3365–3387. doi.org/10.1109/TPAMI.2020.2982166.


26. Kim J., Lee J.K., Lee K.M. Accurate image super-resolution using very deep convolutional networks. Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (27–30 June 2016, Las Vegas, NV, USA). Las Vegas, 2016. P. 1646–1654.


27. Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014. doi.org/10.48550/arXiv.1409.1556.


28. Dong C., Loy C.C., Tang X. Accelerating the super-resolution convolutional neural network. Proc. European conference on computer vision (ECCV 2016) (11–14 October 2016, Amsterdam, The Netherlands). Amsterdam, 2016. Lecture Notes in Computer Science. 2016. Vol. 9906. P. 391–407. doi.org/10.1007/978-3-319-46475-6_25 .


29. Shi W., Caballero J., Huszїr F., Totz J., Aitken A.P., Bishop R., Rueckert D., Wang Z. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (27–30 June 2016, Las Vegas, NV, USA). Las Vegas, 2016. P. 1874–1883.


30. Dai J., Li Y., He K., Sun, J. R-fcn: Object detection via region-based fully convolutional networks. Proc. 30th Annual Conference on Neural Information Processing Systems 2016 (NIPS 2016) (5–10 December 2016, Barcelona, Spain). Barcelona, 2016. Vol. 1. P. 379–387.


31. Ahn N., Kang B., Sohn K.A. Fast, accurate, and lightweight super-resolution with cascading residual network. Proc. European Conference on Computer Vision (ECCV 2018) (8–14 September 2018, Munich, Germany). Munich, 2018. Part XV. P. 252–268.


32. Tai Y., Yang J., Liu X. Image super-resolution via deep recursive residual network. Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (21–26 July 2017, Honolulu, HI, USA). Honolulu, 2017. P. 3147–3155.


33. Kim J., Kwon Lee J., Mu Lee K. Deeply-recursive convolutional network for image super-resolution. Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (27–30 June 2016, Las Vegas, NV, USA). Las Vegas, 2016. P. 1637–1645.


34. Lai W.S., Huang J.B., Ahuja N., Yang M.H. Deep Laplacian pyramid networks for fast and accurate super-resolution. Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (21–26 July 2017, Honolulu, HI, USA. Honolulu, 2017. P. 624–632.


35. Hu J., Shen L., Sun G. Squeeze-and-excitation networks. Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (18–23 June 2018, Salt Lake City, UT, USA). Salt Lake City, 2018. P. 7132–7141.


36. Zhang Y., Li K., Li K., Wang L., Zhong B., Fu Y. Image super-resolution using very deep residual channel attention networks. Proc. European Conference on Computer Vision (ECCV 2018) (8–14 September 2018, Munich, Germany). Munich, 2018. P. 286–301.


37. Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Bengio Y. Generative adversarial nets. Proc. 27th International Conference on Neural Information Processing Systems (NIPS 2-14) (8–13 December 2014, Montreal, Canada). Montreal, 2014. P. 2672–2680.


38. Ledig C., Theis L., Huszїr F., Caballero J., Cunningham A., Acosta A., Shi W. Photo-realistic single image super-resolution using a generative adversarial network. Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (21–26 July 2017, Honolulu, HI, USA. Honolulu, 2017. P. 4681–4690.


39. Johnson J., Alahi A., Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution. Proc. European Conference on Computer Vision (ECCV 2016) (11–14 October 2016, Amsterdam, The Netherlands). Lecture Notes in Computer Science. Vol. 9906. 2016. P. 694–711.


40. Yuan Y., Liu S., Zhang J., Zhang Y., Dong C., Lin L. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (18–22 June 2018, Salt Lake City, UT, USA). Salt Lake City, 2018. P. 814–81409.


41. Zhu J., Park T., Isola P., Efros A. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proc. 2017 IEEE International Conference on Computer Vision (ICCV) (22–29 October 2017, Venice, Italy). Venice, 2017. P. 2242–2251.


42. Mao X., Li Q., Xie H., Lau R.Y., Wang Z., Paul Smolley S. Least squares generative adversarial networks. Proc. 2017 IEEE International Conference on Computer Vision (ICCV) (22–29 October 2017, Venice, Italy). Venice, 2017. P. 2813–2831.


43. Wang X., Yu K., Wu S., Gu J., Liu Y., Dong C., Qiao Y., Loy C.C. ESRGAN: Enhanced super-resolution generative adversarial networks. Proc. European Conference on Computer Vision (ECCV 2018) Workshops (8–14 September 2018, Munich, Germany). Munich, 2018. Part V. P. 63–79.


44. Demiray B.Z., Sit M., Demir I. D-SRGAN: DEM super-resolution with generative adversarial network. arXiv preprint arXiv:2004.04788. 2020. doi.org/10.48550/arXiv.2004.04788 .


45. Chen H., Zhang X., Liu Y., Zeng Q. Generative adversarial networks capabilities for super-resolution reconstruction of weather radar echo images. Atmosphere. 2019. Vol. 10, N 9. 555. doi.org/10.3390/atmos10090555 .


46. Salgueiro Romero L., Marcello J., Vilaplana V. Super-resolution of Sentinel-2 imagery using generative adversarial networks. Remote Sensing. 2020. Vol. 12, N 15. doi.org/10.3390/rs12152424.


47. Pashaei M., Starek M.J., Kamangir H., Berryhill J. Deep learning-based single image super-resolution: an investigation for dense scene reconstruction with UAS photogrammetry. Remote Sensing. 2020. Vol. 12, N 11. 1757. doi.org/10.3390/rs12111757.


48. Jiang K., Wang Z., Yi P., Wang G., Lu T., Jiang J. Edge-enhanced GAN for remote sensing image superresolution. IEEE Transactions on Geoscience and Remote Sensing. 2019. Vol. 57, Iss. 8. P. 5799–5812. doi.org/10.1109/TGRS.2019.2902431 .


49. Dou X., Li C., Shi Q., Liu M. Super-resolution for hyperspectral remote sensing images based on the 3D attention-SRGAN network. Remote Sensing. 2020. Vol. 12, N 7. 1204. doi.org/10.3390/rs12071204.


50. Tsagkatakis G., Aidini A., Fotiadou K., Giannopoulos M., Pentari A., Tsakalides P. Survey of deep-learning approaches for remote sensing observation enhancement. Sensors. 2019. Vol. 19, N 18. 3929. doi.org/10.3390/s19183929 .


51. Pan S.J., Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering. 2009. Vol. 22, N 10. P. 1345–1359. doi.org/10.1109/TKDE.2009.191.


52. Xie M., Jean N., Burke M., Lobell D., Ermon S. Transfer learning from deep features for remote sensing and poverty mapping. arXiv preprint arXiv:1510.00098. 2015. doi.org/10.48550/arXiv.1510.00098.


53. Wang A.X., Tran C., Desai N., Lobell D., Ermon S. Deep transfer learning for crop yield prediction with remote sensing data. Proc. 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ’18) (20–22 June 2018, Menlo Park and San Jose, CA, USA). Menlo Park and San Jose, 2018. 50. P. 1–5. doi.org/10.1145/ 3209811.3212707 .


54. Hao P., Di L., Zhang C., Guo L. Transfer learning for crop classification with Cropland Data Layer data (CDL) as training samples. Science of The Total Environment. 2020. Vol. 733. 138869. doi.org/10.1016/j.scitotenv.2020.138869.


55. Crop Data Layer. URL: https://nassgeodata.gmu.edu .


56. Brewer E., Lin J., Kemper P., Hennin J., Runfola D. Predicting road quality using high resolution satellite imagery: A transfer learning approach. PLoS ONE. 2021. Vol. 16, N 7. doi.org/10.1371/journal.pone.0253370 .


57. Wang H., Zhao X., Zhang X., Wu D., Du X. Long time series land cover classification in China from 1982 to 2015 based on Bi-LSTM deep learning. Remote Sensing. 2019. Vol. 11, N 14. 1639. doi.org/10.3390/rs11141639.


58. Sun Z., Di L., Fang H. Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series. International Journal of Remote Sensing. 2019. Vol. 40, Iss. 2. P. 593–614.


59. Sun J., Lai Z., Di L., Sun Z., Tao J., Shen Y. Multilevel deep learning network for county-level corn yield estimation in the U.S. corn belt. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020. Vol. 13. P. 5048-5060. https://doi.org/ 10.1109/JSTARS.2020.3019046.


60. Cho W., Kim S., Na M., Na I. Forecasting of tomato yields using attention-based LSTM network and ARMA model. Electronics. 2021. Vol. 10, N 13. 1576. doi.org/10.3390/electronics10131576.


61. Gavahi K., Abbaszadeh P., Moradkhani H. DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting. Expert Systems with Applications. 2021. Vol. 184. 115511. https://doi.org/10.1016/j.eswa.2021.115511.