Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …

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

Building height is a crucial variable in the study of urban environments, regional climates,
and human-environment interactions. However, high-resolution data on building height …

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 …

A random forest (RF) classifier is an ensemble classifier that produces multiple decision
trees, using a randomly selected subset of training samples and variables. This classifier …

The extraction of urban structures such as buildings from very high-resolution (VHR) remote
sensing imagery has improved dramatically, thanks to recent developments in deep …

The importance of land use and cover change (LUCC) has gradually attracted more
attention due to its influence on the climate and ecosystem. Consequently, the necessity of …

Effective urban planning, and urban green space management in particular, require proper
data on urban green spaces. The potential of urban green spaces to provide benefits to …

Accurate cotton maps are crucial for monitoring cotton growth and precision management.
The paper proposed a county-scale cotton map** method by using random forest (RF) …

Reliable representations of global urban extent remain limited, hindering scientific progress
across a range of disciplines that study functionality of sustainable cities. We present an …