Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, ...
This project focuses on land use and land cover classification using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The classification task aims to predict the category of ...
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 ...
A Google Earth Engine Land use (crops) classification workflow using Random Forest, one year of ground data, Sentinel-2, and Landsats; to produce multiyear annual 30-m crop maps This project focuses ...
This work will present the challenges in using quantum-enhanced support vector machines (QSVM) for classification tasks on multi-spectral Earth Observation (EO) data. The main areas of investigation ...
Reliable information plays a pivotal role in sustainable urban planning. With advancements in computer technology, geoinformatics tools enable accurate identification of land use and land cover (LULC) ...
A new study has provided a comprehensive Land Use and Land Cover (LULC) classification for the Kurdistan Region of Iraq based on remote sensing data. The study area covered a total area of 46465.1 km2 ...
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