Abstract
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.
Highlights
Africa is the world’s second largest and second most populous continent in the world with an estimate population of one billion, three hundred (1,300,000,000) people
This paper aims to review some of these notable challenges to the application of DL for satellite image-based classification tasks in developing continents
This section discusses some of the challenges to using deep learning for satellite image classification tasks in scare resource environments: 1) Lack of training data: Despite deep learning’s powerful feature extraction capability, in practice it is difficult to train Convolutional Neural Networks (CNN) models with small quantity of datasets[4]
Summary
Africa is the world’s second largest and second most populous continent in the world with an estimate population of one billion, three hundred (1,300,000,000) people. These changes have contributed to the enormous challenges it faces today, including food scarcity, degradation of habitats, outbreaks of epidemics, environmental hazards, and climate change and global warming. Its potentials in remote sensing tasks have been ascertained by many researchers in developed countries for solving problems in the domain of geological mapping, land cover mapping, land use planning, geological image classification, infrastructural development, mineral resources exploration, etc. New approaches have yielded significant breakthroughs, offering novel opportunities for its further research and development This can be taken advantage of in low resources environments such as Africa; the shortage of scientific papers discussing such emerging approaches or concepts in an easy and commonly understood way remains one major obstacle to its application. The main contributions of this paper are to: 1) Provide a brief overview of a typical DL model. 2) Provide a brief overview of satellite image classification with CNN. 3) Discuss the challenges it poses to developing countries. 4) The emerging solutions for satellite image classification in low resource environments are further discussed. 5) a discussion on the potential of DL technique in land cover mapping in Nigeria is provided
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
