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36 ta ish

Ish: Comparative analysis of Random Forest and Support Vector Machine for LULC classification in Tashkent Region using Landsat-8 imagery

  1. Random Forests

    Leo Breiman

    Maqola200167 iqtibos
    ABI

  2. The soil-adjusted vegetation index for soil salinity assessment in Uzbekistan

    Nozimjon Teshaev, Bunyod Mamadaliyev, Azamjon Ibragimov +1

    MaqolaEngineering and Agricultural InnovationsInterCarto InterGIS202050 iqtibos
    ABI

  3. Applying remote sensing techniques to monitor green areas in Tashkent Uzbekistan

    I. A. Aslanov, Uzbekkhon Mukhtorov, Rahimjon Mahsudov +4

    MaqolaEngineering and Agricultural InnovationsE3S Web of Conferences202125 iqtibos
    ABI

  4. Land cover change in Tashkent province during 1992 – 2018

    Bokhir Alikhanov, Sh Alikhanova, Rustam Oymatov +2

    MaqolaEngineering and Agricultural InnovationsIOP Conference Series Materials Science and Engineering202024 iqtibos
    ABI

  5. Analysis of desertification trends in Central Asia based on MODIS Data using Google Earth Engine

    I. A. Aslanov, Nozimjon Teshaev, Kholmurod Khayitov +3

    MaqolaRangeland Management and Livestock EcologyE3S Web of Conferences202315 iqtibos
    ABI

  6. The Assessment of Climate Change on Rainfall-Runoff Erosivity in the Chirchik–Akhangaran Basin, Uzbekistan

    Khusen Gafforov, Anming Bao, Shavkat Rakhimov +7

    MaqolaSoil erosion and sediment transportSustainability202012 iqtibos
    ABI

  7. Random forest in remote sensing: A review of applications and future directions

    Mariana Belgiu, Lucian Drăguţ

    Sharh maqola201610 iqtibos
    ABI

  8. The impacts of green spaces on mitigating the urban hot island effect in the city of Tashkent

    Shavkat Sharipov, Alijon Khayitmurodov

    MaqolaUrban Heat Island MitigationBIO Web of Conferences20247 iqtibos
    ABI

  9. Status of land cover classification accuracy assessment

    Giles M. Foody

    Maqola20026 iqtibos
    ABI

  10. Support-Vector Networks

    Corinna Cortes, Vladimir Vapnik

    Maqola19955 iqtibos
    ABI

  11. The Detection of Past and Future Land Use and Land Cover Change in Ugam Chatkal National Park, Uzbekistan, Using CA-Markov and Random Forest Machine Learning Algorithms

    Bokhir Alikhanov, Bakhtiyor Pulatov, Luqmon Samiev

    MaqolaTransboundary Water Resource ManagementForum Geografi20244 iqtibos
    ABI

  12. Implementation of machine-learning classification in remote sensing: an applied review

    Aaron E. Maxwell, Timothy A. Warner, Fang Fang

    Maqola20184 iqtibos
    ABI

  13. Characterizing land surface dynamics in Aral Sea basin of Uzbekistan using climatic and remote sensing data to project future conditions

    I. A. Aslanov, Nozimjon Teshaev, Zаfаrjon Jаbbarov +4

    MaqolaTransboundary Water Resource ManagementE3S Web of Conferences20243 iqtibos
    ABI

  14. Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index

    Lauri Korhonen, Hadi Hadi, Petteri Packalén +1

    Maqola20173 iqtibos
    ABI

  15. An assessment of support vector machines for land cover classification

    Chenn‐Jung Huang, Larr S. Davis, J. R. Townshend

    Maqola20023 iqtibos
    ABI

  16. Random Forests for land cover classification

    Pall Oskar Gislason, Jón Atli Benediktsson, Jóhannes R. Sveinsson

    Maqola20052 iqtibos
    ABI

  17. Random forest classifier for remote sensing classification

    Mahesh Pal

    Maqola20052 iqtibos
    ABI

  18. Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms

    Md. Qutub Uddin Sajib, Wang Tao

    Maqola20202 iqtibos
    ABI

  19. Support Vector Machines and Support Vector Regression

    Osval A. Montesinos‐López, Abelardo Montesinos‐López, José Crossa

    Bob20222 iqtibos
    ABI

  20. Support vector machines in remote sensing: A review

    Giorgos Mountrakis, Jungho Im, Caesar Ogole

    Sharh maqola20102 iqtibos
    ABI

  21. Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

    Elbek Erdanaev, Martin Kappas, Daniel Wyss

    Maqola20222 iqtibos
    ABI

  22. Effectiveness of machine learning and deep learning models at county-level soybean yield forecasting

    Nizom Farmonov, Khilola Amankulova, Shahid Nawaz Khan +6

    MaqolaRemote Sensing in AgricultureHungarian Geographical Bulletin20241 iqtibos
    ABI

  23. Sarlavhasiz

    Boshqa1 iqtibos
    ABI

  24. Sarlavhasiz

    Boshqa1 iqtibos
    ABI

  25. Sarlavhasiz

    Boshqa1 iqtibos
    ABI