Landcover classification and change detection using remote sensing and machine learning: a case study of Western Fiji
This provides technical support for land cover modeling and change detection in Fiji, addressing urbanization impacts, but it is incremental as it applies existing methods to a new region.
The study tackled land cover classification and change detection in Western Fiji from 2013 to 2024 using remote sensing and machine learning, resulting in visualizations of urban area changes over time.
As a developing country, Fiji is facing rapid urbanisation, which is visible in the massive development projects that include housing, roads, and civil works. In this study, we present machine learning and remote sensing frameworks to compare land use and land cover change from 2013 to 2024 in Nadi, Fiji. The ultimate goal of this study is to provide technical support in land cover/land use modelling and change detection. We used Landsat-8 satellite image for the study region and created our training dataset with labels for supervised machine learning. We used Google Earth Engine and unsupervised machine learning via k-means clustering to generate the land cover map. We used convolutional neural networks to classify the selected regions' land cover types. We present a visualisation of change detection, highlighting urban area changes over time to monitor changes in the map.