Nowadays, different machine learning approaches, either conventional or more advanced,
use input from different remote sensing imagery for land cover classification and associated …

Deforestation is a global change driver that contributes to atmospheric carbon emissions,
causes biodiversity loss and ecosystem services degradation. Usually, this process has …

Land cover mapping provides spatial information on the physical properties of the Earth's
surface for various classes of wetlands, artificial surface and constructions, vineyards, water …

In Mediterranean landscapes, the encroachment of pyrophytic shrubs is a driver of more
frequent and larger wildfires. The high-resolution mapping of vegetation cover is essential …

Green space in cities has been reducing rapidly due to the intensive urban expansion,
which contributes to surface temperature growth, leading to numerous challenges in …

Land Use and Land Cover (LULC) monitoring is crucial for global transformation,
sustainable land control, urban planning, urban growth prediction, and the establishment of …

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 …

Accurate characterization of urban forest change can help quantify its impact on the urban
thermal environment. Taking Hangzhou City and Zhaoqing City, China as two cases, we …

With the development of deep learning, the use of convolutional neural networks (CNN) to
improve the land cover classification accuracy of hyperspectral remote sensing images …

This paper presents a comprehensive general overview of the extensive literature available
in the field of application of artificial intelligence (AI) in flood prediction. The initial approach …