This review explores the comparative utility of machine learning (ML) and deep learning
(DL) in land system science (LSS) classification tasks. Through a comprehensive …

Classification is a very common image processing task. The accuracy of the classified map
is typically assessed through a comparison with real-world situations or with available …

Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, eg,
urban/rural planning, disaster management, and climate change adaptation. Recently, Deep …

The knowledge of tree species distribution at a national scale provides benefits for forest
management practices and decision making for site-adapted tree species selection. An …

Using deep learning semantic segmentation for land use extraction is the most challenging
problem in medium spatial resolution imagery. This is because of the deep convolution layer …

The Landsat archive, with a multi-decadal global coverage is a prime candidate for deep
learning classification methods due to the large data volume. Local studies have evaluated …

Recently, unmanned aerial vehicles (UAVs) have been used in several applications of
environmental modeling and land use inventories. At the same time, the computer vision …

The semantic segmentation of land use and land cover (LULC) is a crucial and widely
employed remote sensing task. Conventional convolutional neural networks and vision …

Land cover monitoring is crucial to understand land transformations at a global, regional and
local level, and the development of innovative methodologies is necessary in order to define …

Tropical montane forests (TMF) play a vital role in providing numerous ecosystem services.
This ecosystem is characterized by towering mountains, cold weather, tall trees such as …