Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …

This study aimed to provide a systematic overview of the progress made in utilizing remote
sensing for assessing the impacts of land use and land cover (LULC) changes on water …

Map** the distribution, pattern, and composition of urban land use categories plays a
valuable role in understanding urban environmental dynamics and facilitating sustainable …

The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

During the past decades, multiple remote sensing data sources, including nighttime light
images, high spatial resolution multispectral satellite images, unmanned drone images, and …

Although deep neural networks hold the state-of-the-art in several remote sensing tasks,
their black-box operation hinders the understanding of their decisions, concealing any bias …

The accurate map** of crop types is crucial for ensuring food security. Remote Sensing
(RS) satellite data have emerged as a promising tool in this field, offering broad spatial …

The multi-layer structures of Deep Learning facilitate the processing of higher-level
abstractions from data, thus leading to improved generalization and widespread …

Mangroves are an ecologically and economically valuable ecosystem that provides a range
of ecological services, including habitat for a diverse range of plant and animal species …

Climate scientists are gaining an understanding and data of the past and are projecting what
the future climate might be like through applying the climate models. A climate model is like …