Land-use land-cover classification by machine learning classifiers for satellite observations—A review
Rapid and uncontrolled population growth along with economic and industrial development,
especially in developing countries during the late twentieth and early twenty-first centuries …
especially in developing countries during the late twentieth and early twenty-first centuries …
[HTML][HTML] Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework
Climate change and rapid urbanisation exacerbated multiple urban issues threatening
urban sustainability. Numerous studies integrated machine learning and remote sensing to …
urban sustainability. Numerous studies integrated machine learning and remote sensing to …
Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms
Droughts are particularly disastrous in South Africa and other arid regions that are water-
scarce by nature due to low rainfall and water sources. According to some studies, droughts …
scarce by nature due to low rainfall and water sources. According to some studies, droughts …
Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach
Accurate spatial-temporal mapping of urban land-use and land-cover (LULC) provides
critical information for planning and management of urban environments. While several …
critical information for planning and management of urban environments. While several …
An integrated modelling approach to urban growth and land use/cover change
Long-term sustainable development in developing countries requires researching and
projecting urban physical growth and land use/land cover change (LUCC). This research …
projecting urban physical growth and land use/land cover change (LUCC). This research …
Deep learning semantic segmentation for land use and land cover types using Landsat 8 imagery
Using deep learning semantic segmentation for land use extraction is the most challenging
problem in medium spatial resolution imagery. This is because of the deep convolution layer …
problem in medium spatial resolution imagery. This is because of the deep convolution layer …
Soil salinity prediction using Machine Learning and Sentinel–2 Remote Sensing Data in Hyper–Arid areas
We are experiencing a considerable increase in soil salinity as a result of the influence of
climate change or environmental contamination produced by excessive industry and …
climate change or environmental contamination produced by excessive industry and …
Land use/land cover classification using hyperspectral soil reflectance features in the Eastern Himalayas, India
Abstract Information on periodic land use/land cover (LULC) changes are imperative for
regional agricultural planning and policymaking. In this study, 332 soil samples were …
regional agricultural planning and policymaking. In this study, 332 soil samples were …
Summertime microscale assessment and prediction of urban thermal comfort zone using remote-sensing techniques for Kuwait
Urbanization significantly accelerates the replacement of natural land-use and land-cover
(LULC) classes, which can raise the temperature and diminish thermal comfort zone (TCZ) …
(LULC) classes, which can raise the temperature and diminish thermal comfort zone (TCZ) …
Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support
Hyperspectral satellite missions, such as PRISMA of the Italian Space Agency (ASI), have
opened up new research opportunities. Using PRISMA data in land cover classification has …
opened up new research opportunities. Using PRISMA data in land cover classification has …