Rapid and uncontrolled population growth along with economic and industrial development,
especially in developing countries during the late twentieth and early twenty-first centuries …

Object-based image classification for land-cover mapping purposes using remote-sensing
imagery has attracted significant attention in recent years. Numerous studies conducted over …

Existing water quality index (WQI) models assess water quality using a range of
classification schemes. Consequently, different methods provide a number of interpretations …

The type of algorithm employed to classify remote sensing imageries plays a great role in
affecting the accuracy. In recent decades, machine learning (ML) has received great …

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-
Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost …

Abstract Machine learning (ML) models are a leading analytical technique used to monitor,
map and quantify land use and land cover (LULC) and its change over time. Models such as …

The development and improvement of methods to map agricultural land cover are currently
major challenges, especially for radar images. This is due to the speckle noise nature of …

Wetlands are valuable natural resources that provide many benefits to the environment.
Therefore, mapping wetlands is crucially important. Several review papers on remote …

Predicting the penetration rate is a complex and challenging task due to the interaction
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …

The size of the training data set is a major determinant of classification accuracy.
Nevertheless, the collection of a large training data set for supervised classifiers can be a …