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  • 标题:Application of Deep Learning in Satellite Image-based Land Cover Mapping in Africa
  • 本地全文:下载
  • 作者:Nzurumike Obianuju Lynda ; Ezeomedo Innocent C ; Nwojo Agwu Nnanna
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2021
  • 卷号:12
  • 期号:9
  • DOI:10.14569/IJACSA.2021.0120948
  • 语种:English
  • 出版社:Science and Information Society (SAI)
  • 摘要:Deep Learning Networks (DLN), in particular, Convolutional Neural Networks (CNN) has achieved state-of-the-art results in various computer vision tasks including automatic land cover classification from satellite images. However, despite its remarkable performance and broad use in developed countries, using this advanced machine learning algorithm has remained a huge challenge in developing continents such as Africa. This is because the necessary tools, techniques, and technical skills needed to utilize DL networks are very scarce or expensive. Recently, new approaches to satellite image-based land cover classification with DL have yielded significant breakthroughs, offering novel opportunities for its further development and application. This can be taken advantage of in low resources continents such as Africa. This paper aims to review some of these notable challenges to the application of DL for satellite image-based classification tasks in developing continents. Then, review the emerging solutions as well as the prospects of their use. Harnessing the power of satellite data and deep learning for land cover mapping will help many of the developing continents make informed policies and decisions to address some of its most pressing challenges including urban and regional planning, environmental protection and management, agricultural development, forest management and disaster and risks mitigation.
  • 关键词:Deep learning; satellite image classification; land cover mapping; Africa
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