Sentiment analysis becomes more popular in the research area. It allocates positive or
negative polarity to an entity or items by using different natural language processing tools …

Tree species diversity is a key parameter to describe forest ecosystems. It is, for example,
important for issues such as wildlife habitat modeling and close-to-nature forest …

Studies have demonstrated the robust performance of the ensemble machine learning
classifier, random forests, for remote sensing land cover classification, particularly across …

This study investigates land cover changes, magnitude and speed of urbanization and
evaluates possible impacts on the environment by the concepts of landscape metrics and …

The iterative and convergent nature of ensemble learning algorithms provides potential for
improving classification of complex landscapes. This study performs land-cover …

Abstract The Random Forests (RF) algorithm is a machine learning method that has recently
been demonstrated as a viable technique for data-driven predictive modeling of mineral …

Map** and monitoring forest extent is a common requirement of regional forest inventories
and public land natural resource management, including in Australia. The state of Victoria …

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to
implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and …

Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa.
These systems are characterized by small, heterogeneous, and often indistinct field patterns …

The advent of advances in machine learning (ML)-based techniques has popularized wide
applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In …