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Работы, на которые ссылается эта работа

Работ: 36

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

  1. Random Forests

    Leo Breiman

    Статья2001Цитирований: 67
    ABI

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

    Nozimjon Teshaev, Bunyod Mamadaliyev, Azamjon Ibragimov +1

    СтатьяEngineering and Agricultural InnovationsInterCarto InterGIS2020Цитирований: 50
    ABI

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

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

    СтатьяEngineering and Agricultural InnovationsE3S Web of Conferences2021Цитирований: 25
    ABI

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

    Bokhir Alikhanov, Sh Alikhanova, Rustam Oymatov +2

    СтатьяEngineering and Agricultural InnovationsIOP Conference Series Materials Science and Engineering2020Цитирований: 24
    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

    СтатьяRangeland Management and Livestock EcologyE3S Web of Conferences2023Цитирований: 15
    ABI

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

    Khusen Gafforov, Anming Bao, Shavkat Rakhimov +7

    СтатьяSoil erosion and sediment transportSustainability2020Цитирований: 12
    ABI

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

    Mariana Belgiu, Lucian Drăguţ

    Обзорная статья2016Цитирований: 10
    ABI

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

    Shavkat Sharipov, Alijon Khayitmurodov

    СтатьяUrban Heat Island MitigationBIO Web of Conferences2024Цитирований: 7
    ABI

  9. Status of land cover classification accuracy assessment

    Giles M. Foody

    Статья2002Цитирований: 6
    ABI

  10. Support-Vector Networks

    Corinna Cortes, Vladimir Vapnik

    Статья1995Цитирований: 5
    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

    СтатьяTransboundary Water Resource ManagementForum Geografi2024Цитирований: 4
    ABI

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

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

    Статья2018Цитирований: 4
    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

    СтатьяTransboundary Water Resource ManagementE3S Web of Conferences2024Цитирований: 3
    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

    Статья2017Цитирований: 3
    ABI

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

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

    Статья2002Цитирований: 3
    ABI

  16. Random Forests for land cover classification

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

    Статья2005Цитирований: 2
    ABI

  17. Random forest classifier for remote sensing classification

    Mahesh Pal

    Статья2005Цитирований: 2
    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

    Статья2020Цитирований: 2
    ABI

  19. Support Vector Machines and Support Vector Regression

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

    Глава2022Цитирований: 2
    ABI

  20. Support vector machines in remote sensing: A review

    Giorgos Mountrakis, Jungho Im, Caesar Ogole

    Обзорная статья2010Цитирований: 2
    ABI

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

    Elbek Erdanaev, Martin Kappas, Daniel Wyss

    Статья2022Цитирований: 2
    ABI

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

    Nizom Farmonov, Khilola Amankulova, Shahid Nawaz Khan +6

    СтатьяRemote Sensing in AgricultureHungarian Geographical Bulletin2024Цитирований: 1
    ABI

  23. Без названия

    ДругоеЦитирований: 1
    ABI

  24. Без названия

    ДругоеЦитирований: 1
    ABI

  25. Без названия

    ДругоеЦитирований: 1
    ABI