DOI

Currently, there is significant progress in the interpretation of remote images of the earth’s surface using various recognition systems. To train these systems, large labeled datasets are required. The creation of such datasets is carried out by experts and is quite a time-consuming task. This paper proposes a method for creating specialized datasets for their use in monitoring the state of agricultural lands undergoing degradation. The method allows you to automate the process of creating such datasets The datasets are generated from freely available data obtained by the Sentinel satellites as part of the European Copernicus space program. The method is based on the processing of the results of the preliminary classification of images of the earth’s cover, previously produced by the Sentinel Hub service.

Язык оригиналаанглийский
Название основной публикацииComputational Science and Its Applications – ICCSA 2021
Подзаголовок основной публикации21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII
РедакторыO Gervasi, B Murgante, S Misra, C Garau, Blecic, D Taniar, BO Apduhan, AMAC Rocha, E Tarantino, CM Torre
ИздательSpringer Nature
Страницы406-416
Число страниц11
ISBN (печатное издание)9783030870096
DOI
СостояниеОпубликовано - 2021
Событие21st International Conference on Computational Science and Its Applications, ICCSA 2021 - Virtual, Online, Италия
Продолжительность: 13 сен 202116 сен 2021

Серия публикаций

НазваниеLecture Notes in Computer Science
ИздательSPRINGER INTERNATIONAL PUBLISHING AG
Том12956
ISSN (печатное издание)0302-9743

конференция

конференция21st International Conference on Computational Science and Its Applications, ICCSA 2021
Сокращенное названиеICCSA 2021
Страна/TерриторияИталия
ГородVirtual, Online
Период13/09/2116/09/21

    Предметные области Scopus

  • Теоретические компьютерные науки
  • Компьютерные науки (все)

ID: 86276268