Land-surface parameters derived from digital land surface models (DLSMs)(for example,
slope, surface curvature, topographic position, topographic roughness, aspect, heat load …

In recent years, the wealth of technological development revolutionised our ability to collect
data in geosciences. Due to the unprecedented level of detail of these datasets …

This paper describes an open-source geographical information system (GIS) called
Whitebox Geospatial Analysis Tools (Whitebox GAT). Whitebox GAT was designed to …

The low potential of agricultural productivity in the majority of central Iran is mainly attributed
to high levels of soil salinity. To increase agricultural productivity, while preventing any …

In machine learning, one often assumes the data are independent when evaluating model
performance. However, this rarely holds in practice. Geographic information datasets are an …

Existing GIS software mainly target at expert users and do not sufficiently integrate resources
for efficient computing. They are difficult for non-experts to use and are often slow in …

Machine learning (ML) methods, such as artificial neural networks (ANN), k-nearest
neighbors (k NN), random forests (RF), support vector machines (SVM), and boosted …

Applications of graph theory have proliferated across the academic spectrum in recent
years. Whereas geosciences and landscape ecology have made rich use of graph theory, its …

In the last decade landform classification and map** has developed as one of the most
active areas of geomorphometry. However, translation from continuous models of elevation …

Tropical forests are significant carbon sinks and their soils' carbon storage potential is
immense. However, little is known about the soil organic carbon (SOC) stocks of tropical …