Simple Implementation of Terrain Classification Models via Fully Convolutional Neural Networks

Assiya Sarinova, Leila Rzayeva, Noyan Tendikov, Ibraheem Abdullah Mohammed Shayea

2023

DOI 10.1109/wincom59760.2023.10323012

Data confidence 70 medium

How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

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.