Spatial multi-criteria decision analysis (MCDA) is increasingly being used to inform urban
green infrastructure planning. We explore the use of modern cloud computing technologies …

Open-source land cover products (LCPs) are essential for many areas of scientific research.
However, they have deficiencies such as low accuracy, low resolution, and poor timeliness …

Classification of multispectral optical satellite data using machine learning techniques to
derive land use/land cover thematic data is important for many applications. Comparing the …

This study investigates long-term landscape transformations (1949–2016) in urban Rome,
Central Italy, through a spatial distribution of seven metrics (core, islet, perforation, edge …

The objective of the proposed research is to estimate the land-use/land-cover (LULC)
changes by employing artificial intelligence techniques rather than doing it manually. For …

Abstract Convolutional Neural Networks (CNN) have proven to be pivotal in advancements
in the domain of computer vision. One of the most important and highly researched …

This paper intends to propose an automated deep learning-based land cover classification
model of remote sensing images. The model includes (i) pre-processing,(ii) feature …

Deep learning is widely used for the classification of images that have various attributes.
Image data are used to extract colour, texture, form, and local features. These features are …

Land cover is a powerful tool and takes advantage of Convolutional Neural Network (CNN)
in remote sensing image recognition. However, the existing datasets are pretty small or are …

Dependable land cover data are required to aid in the resolution of a broad spectrum of
environmental issues. Land cover classification at a broad scale has been carried out using …