Simple Implementation of Terrain Classification Models via Fully Convolutional Neural Networks

Sarinova A., Rzayeva L., Tendikov N., Shayea I. A. M.

10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023, İstanbul, Turkey, 26 - 28 October 2023 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/wincom59760.2023.10323012
  • City: İstanbul
  • Country: Turkey
  • Keywords: Aerospace images, Deep Learning, Google Cloud Computing, Machine Learning, Satellite data, Terrain Classification
  • Istanbul Technical University Affiliated: Yes


This paper introduces a simple implementation of three versions (large, medium, and small) of terrain multi-classification models using Fully Convolutional Neural Networks (FCNNs) for imagery data. The proposed methodology involves labeled and unlabeled data collection from European Space Agency (ESA) WorldCover and Sentinel-2 MultiSpectral Instrument (MSI) on the Google Earth Engine, compressing datasets into Tensorflow records format with 9 diverse terrain types, and handling Google Cloud training computations. There were prepared different dataset portions of 10 megabytes, 200 megabytes, and around a gigabyte files. The experimental results demonstrate the effectiveness of the CNN-based approach, achieving a tolerable 71% accuracy of the Terrain Classification Model (TCM) and robust classification performance. The simplicity and efficiency of the proposed method make it suitable for real-world applications requiring reliable and fast terrain classification.