Land use and land cover (LULC) analysis has become increasingly significant in environmental studies due to its direct impact on the environment. Changes in LULC affect the ecological and climatic ...
Abstract: This study presents a methodology for generating high-quality datasets of samples to train machine learning models for land use and land cover (LULC) classification, representing an initial ...
Abstract: The effective fusion of multi-modal remote sensing images, particularly hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data, is pivotal for accurate land use and land ...
This repository hosts the source code and datasets for the comparative analysis of five supervised machine learning algorithms: Random Forest (RF), Gradient Tree Boost (GTB), Classification and ...
This project maps and quantifies land use and land cover (LULC) changes across the Federal Capital Territory (FCT) of Abuja, Nigeria, between 2015 and 2024. Using cloud-based geospatial processing on ...
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