Landcover classification and change detection using remote sensing and machine learning: a case study of Western Fiji

Abstract

As a developing country, Fiji is facing rapid urbanisation, as evidenced by massive development projects that include civil works such as housing and roads. In this study, we present a machine learning-based framework that utilises remote sensing data to analyse land use and land cover changes from 2013 to 2024 in Nadi, Fiji. We used Landsat 8 satellite imagery for the study region and created a training dataset with labels for supervised machine learning. We use Google Earth Engine and unsupervised machine learning via K-means clustering to generate the land cover map. We utilise a framework that uses convolutional neural networks (CNNs) and compares with conventional machine learning models to classify the land cover types of the selected regions. We present a visualisation of change detection, highlighting urban area changes over time to monitor map changes. Our results indicate that the CNN model performs similarly to other machine learning models (0.96 F1-score) in terms of classification performance, but better captures the development of urban areas as verified by qualitative analysis. Our study ascertains that Nadi has experienced a rapid urbanisation process, and the expansion extended outward, taking over the sugar farms. (c) 2026 The Author(s). Published by Elsevier B.V. on behalf of COSPAR. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). Show more