Classified land use/ land cover (LULC) maps of Gedaref state for the years 1988, 1998, 2008 and 2018 produced using multi-date Landsat images and random forest classification algor... by Maysoon A. A. Osman (16641628)

Area (ha) and percent cover (%) of each land use/ cover (LULC) class in Gedaref state for the years 1988, 1998, 2008, and 2018 estimated using multi-date Landsat images and random... by Maysoon A. A. Osman (16641628)

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 by Andrew Rogers (17623239)

“…</li></ul><h3>Analysis Scripts</h3><p dir="ltr">Complete set of R scripts for reproducing all analyses:</p><ul><li><b>percent cost increase_line plot.R</b>: Creates visualizations of energy cost impacts under different conservation scenarios</li><li><b>Zonation curves.R</b>: Generates conservation performance curves and coverage statistics</li><li><b>NPV_bar_plot.R</b>: Produces economic analysis plots with Net Present Value breakdowns</li><li><b>domestic_export_map_iterations.R</b>: Creates spatial maps of renewable energy infrastructure for domestic and export scenarios</li></ul><h2>Technical Specifications</h2><h3>Data Formats</h3><ul><li><b>Spatial Data</b>: ESRI Geodatabase (.gdb), Shapefile (.shp), GeoTIFF (.tif)</li><li><b>Tabular Data</b>: CSV, Microsoft Excel (.xlsx, .xls)</li><li><b>Analysis Code</b>: R scripts (.R)</li></ul><h3>Software Requirements</h3><ul><li><b>R</b> (≥4.0.0) with packages: sf, dplyr, ggplot2, readr, readxl, tidyr, furrr, ozmaps, ggpattern</li><li><b>ESRI ArcGIS</b> or <b>QGIS</b> for geodatabase access and spatial analysis</li><li><b>Zonation</b> conservation planning software (for methodology understanding)</li></ul><h3>Hardware Recommendations</h3><ul><li><b>RAM</b>: 16GB minimum (32GB recommended for full spatial analysis)</li><li><b>Storage</b>: 15GB free space for data extraction and processing</li><li><b>CPU</b>: Multi-core processor recommended for parallel processing scripts</li></ul><h2>Detailed Description of the VRE Siting and Cost-Minimization Model</h2><p><br></p><p dir="ltr">This section provides an in-depth description of the Variable Renewable Energy (VRE) siting model, including the software, the core algorithm, and the optimisation process used to determine the least-cost, spatially constrained development trajectory for VRE infrastructure in Queensland, Australia.…”

DataSheet1_Remote sensing modeling of environmental influences on lake fish resources by machine learning: A practice in the largest freshwater lake of China.docx by Tan Chen (571832)

“…The approach developed in this study can save the labor and financial costs for large-area investigation and the assessment of lake fishery resources compared to conventional methods. It is expected to demonstrate an efficient way for public authorities, stakeholders, and decision-makers to guide fishery conservation and management strategies.…”

Data Sheet 1_Fifty years of land use and land cover mapping in the United Arab Emirates: a machine learning approach using Landsat satellite data.docx by Mubbashra Sultan (20413835)

“…These findings underscore the role of machine learning and geospatial technologies in monitoring LULC distribution in challenging environments, and the results serve as a vital tool for policymakers to manage land resources, urban planning, and environmental conservation.…”