Deep learning for region detection in high-resolution aerial images

The goal of given investigation is to develop deep learning and convolutional neural network methods for automatically extracting the locations of objects such as water resource, forest and urban areas from given aerial images. We show how deep neural networks implemented on modern GPUs can be used to efficiently learn highly discriminative image features. For deep learning on supercomputer NVIDIA DGX-1 we used the marked image database UrbanAtlas, which contains images of 21 classes. Images obtained from the Landsat-8 satellites are used for estimation of automatic object detection quality. Object detection on aerial images has found application at urban planning, forest management, climate modelling, etc.

Авторы
Khryashchev V.V.1 , Priorov A.L.1 , Pavlov V.A.1 , Ostrovskaya A.A. 2
Редакторы
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Сборник материалов конференции
Издательство
Institute of Electrical and Electronics Engineers
Номер выпуска
-
Язык
Английский
Страницы
792-796
Статус
Опубликовано
Подразделение
-
Ссылка
-
Номер
-
Том
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Год
2018
Организации
  • 1 P.G. Demidov Yaroslavl State University
  • 2 Peoples Friendship University of Russia
Ключевые слова
remote sensing; forestry; convolutional neural networks; satellites; earth
Дата создания
07.11.2019
Дата изменения
07.11.2019
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/50700/