Open Access
Issue
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
Article Number 09011
Number of page(s) 7
Section Material Engineering
DOI https://doi.org/10.1051/e3sconf/202459109011
Published online 14 November 2024
  1. Meisam Amani et al., “Canadian Wetland Inventory using Google Earth Engine The first map and preliminary results”, volume 11, Remote Sensing, Apr. 2019. [Google Scholar]
  2. N. Gorelick et al “Google earth engine: Planetary-scale geospatial analysis for everyone”, volume 202, Remote Sensing, Dec. 2017. [Google Scholar]
  3. L. Kumar et al., “Google Earth Engine applications since inception: Usage, trends, and potential”, volume 10, Remote Sensing, Sep. 2018. [Google Scholar]
  4. O. Mutanga et al., “Google Earth Engine applications”, volume 11, Remote Sensing, Mar.2019. [Google Scholar]
  5. H. Tamiminia, et al., “Google Earth Engine for geo-big data applications: A meta-analysis and systematic review”, volume 164, ISPRS J. Photogrammetry Remote Sensing, Jun. 2020. [Google Scholar]
  6. M. Amani et al., “Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review”, volume 13, IEEE journal of selected topics in applied earth observations and remote sensing, 2020. [Google Scholar]
  7. Meinan Zhang et al., “Mapping bamboo with regional phenological characteristics derived from dense Landsat time series using Google Earth Engine”, volume 40, IJRS, December 2019. [Google Scholar]
  8. Zunyi Xie et al., “Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands –A first step towards identifying degraded lands for conservation”, Remote Sensing Environment, Oct. 2019. [Google Scholar]
  9. E Durate et al., “Monitoring Approach for Tropical Coniferous Forest Degradation using Remote Sensing and Field Data”, volume 232, Remote Sensing, volume12, Aug 2020. [Google Scholar]
  10. K. Shimizu et al., “Detecting forest changes using dense Landsat 8 and Sentinel-1 time series data in tropical seasonal forests”, volume 11, Remote Sensing, Aug. 2019. [Google Scholar]
  11. J. W. McArthur et al., “Agriculture, aid, and economic growth in Africa”, volume 33, World Bank Economic Review, Feb. 2019. [CrossRef] [Google Scholar]
  12. Pengpeng Han et al., “Monitoring rubber plantation distribution on Hainan Island using Landsat OLI imagery”,volume 39, IJRS, Apr. 2018. [Google Scholar]
  13. L. Liang et al., “Automated mapping of rice fields using multiyear training sample normalization”, volume 40, IJRS, Sep. 2019. [Google Scholar]
  14. S. Wang et al., “Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques”, volume 222, Remote Sensing Environment, March 2019. [Google Scholar]
  15. Felix Rembold et al., “ASAP: A new global early warning system to detect anomaly hot spots of agricultural production for food security analysis”, volume 168, Agricultural Systems, Jan. 2019. [Google Scholar]
  16. Zhenong Jin et al., “Smallholder maize area and yield mapping at national scales with Google Earth Engine”, volume 228, Remote Sensing Environment, Jul. 2019. [Google Scholar]
  17. D. B. Lobell et al., “A scalable satellite-based crop yield mapper”, volume 164, Remote Sensing Environment, Jul. 2015. [Google Scholar]
  18. Rudiyanto et al., “Automated near-real-time mapping and monitoring of rice extent, cropping patterns, and growth stages in Southeast Asia using Sentinel-1 time series on a Google Earth Engine platform”, volume 11, Remote Sensing, Jul. 2019. [Google Scholar]
  19. S. Mahdavi et al., “Remote sensing for wetland classification: A comprehensive review”, volume 55, GIScience & Remote Sensing, Sep. 2018. [Google Scholar]
  20. Q. Wu et al., “Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine,” Remote Sensing Environment, volume 228, Jul. 2019. [Google Scholar]
  21. J. Miettinen et al., “Towards automated 10–30 m resolution land cover mapping in insular South-East Asia”, volume 34, Geocarto International, Mar. 2019. [Google Scholar]
  22. A. Ghorbanian et al., “Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples”, volume 167, ISPRS Journal of Photogrammetry Remote Sensing, Sep. 2020. [Google Scholar]
  23. Tengfei Long et al., “30 m resolution global annual burned area mapping based on Landsat Images and Google Earth Engine”, volume 11, Remote Sensing, Feb. 2019. [Google Scholar]
  24. Haz-Mapper: A global open-source natural hazard mapping application in Google Earth Engine [Google Scholar]
  25. Pragathi, B., and P. Ramu. “Authentication Technique for Safeguarding Privacy in Smart Grid Settings.” E3S Web of Conferences. Vol. 540. EDP Sciences, 2024. [Google Scholar]
  26. Pragathi, Bellamkonda, Deepak Kumar Nayak, and Ramesh Chandra Poonia. “Lorentzian adaptive filter for controlling shunt compensator to mitigate power quality problems of solar PV interconnected with grid.” International Journal of Intelligent Information and Database Systems 13.2-4 (2020): 491-506. [CrossRef] [Google Scholar]
  27. Pragathi, Bellamkonda, et al. “Evaluation and analysis of soft computing techniques for grid connected photo voltaic system to enhance power quality issues.” Journal of Electrical Engineering & Technology 16 (2021): 1833-1840. [CrossRef] [Google Scholar]

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