Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network

Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network

Agricultural production in greenhouses shows a rapid growth in many parts of the world. This form of intensive farming requires a large amount of water and fertilizers, and can have a severe impact on the environment. The number of greenhouses and their location is important for applications like sp...

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Bibliographic Details
Main Authors: Jakab Balázs, van Leeuwen Boudewijn, Tobak Zalán
Format: Article
Language:English
Published: Sciendo 2021-04-01
Series:Journal of Environmental Geography
Subjects:
plastic greenhouse
deep learning
convolutional neural network
satellite image
google earth
Online Access:https://doi.org/10.2478/jengeo-2021-0004
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spelling doaj-4edb088cf7694869877423ae8845d42b2021-09-06T19:41:36ZengSciendoJournal of Environmental Geography2060-467X2021-04-01141-2384610.2478/jengeo-2021-0004Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural NetworkJakab Balázs0van Leeuwen Boudewijn1Tobak Zalán2Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem u. 2-6, 6722 Szeged, HungaryDepartment of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem u. 2-6, 6722 Szeged, HungaryDepartment of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem u. 2-6, 6722 Szeged, HungaryAgricultural production in greenhouses shows a rapid growth in many parts of the world. This form of intensive farming requires a large amount of water and fertilizers, and can have a severe impact on the environment. The number of greenhouses and their location is important for applications like spatial planning, environmental protection, agricultural statistics and taxation. Therefore, with this study we aim to develop a methodology to detect plastic greenhouses in remote sensing data using machine learning algorithms.https://doi.org/10.2478/jengeo-2021-0004plastic greenhousedeep learningconvolutional neural networksatellite imagegoogle earth
collection DOAJ
language English
format Article
sources DOAJ
author Jakab Balázs
van Leeuwen Boudewijn
Tobak Zalán
spellingShingle Jakab Balázs
van Leeuwen Boudewijn
Tobak Zalán
Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network
Journal of Environmental Geography
plastic greenhouse
deep learning
convolutional neural network
satellite image
google earth
author_facet Jakab Balázs
van Leeuwen Boudewijn
Tobak Zalán
author_sort Jakab Balázs
title Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network
title_short Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network
title_full Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network
title_fullStr Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network
title_full_unstemmed Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network
title_sort detection of plastic greenhouses using high resolution rgb remote sensing data and convolutional neural network
publisher Sciendo
series Journal of Environmental Geography
issn 2060-467X
publishDate 2021-04-01
description Agricultural production in greenhouses shows a rapid growth in many parts of the world. This form of intensive farming requires a large amount of water and fertilizers, and can have a severe impact on the environment. The number of greenhouses and their location is important for applications like spatial planning, environmental protection, agricultural statistics and taxation. Therefore, with this study we aim to develop a methodology to detect plastic greenhouses in remote sensing data using machine learning algorithms.
topic plastic greenhouse
deep learning
convolutional neural network
satellite image
google earth
url https://doi.org/10.2478/jengeo-2021-0004
work_keys_str_mv AT jakabbalazs detectionofplasticgreenhousesusinghighresolutionrgbremotesensingdataandconvolutionalneuralnetwork
AT vanleeuwenboudewijn detectionofplasticgreenhousesusinghighresolutionrgbremotesensingdataandconvolutionalneuralnetwork
AT tobakzalan detectionofplasticgreenhousesusinghighresolutionrgbremotesensingdataandconvolutionalneuralnetwork
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