The present work aimed at the spatiotemporal analysis of Land Surface Temperature (LST)
and several land-use land-cover spectral indices, namely Normalized Difference Vegetation …

Over the past two and a half decades, rapid urbanization has led to significant land use and
land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC …

Recently, with the remarkable advancements of deep learning in the field of image
processing, convolutional neural networks (CNNs) have garnered widespread attention from …

Cellular automata (CA) models are employed for simulating geographical distributions,
while Markov-Chain models are utilized for simulating temporal changes. This study aims to …

The fusion of multimodal data plays a crucial role in classification tasks. However, existing
research typically mines and analyzes the individual features of each data source separately …

The integration of multi-source remote sensing data, bolstered by advancements in deep
learning, has emerged as a pivotal strategy for enhancing land use and land cover (LULC) …

Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and
agriculture, decreases reservoir storage capacity due to sedimentation, and increases the …

Hyperspectral image (HSI) target detection plays a pivotal role in both military and civilian
sectors. Nevertheless, this task is fraught with challenges because of the limited availability …

Transformers have shown remarkable success in modeling sequential data and capturing
intricate patterns over long distances. Their self-attention mechanism allows for efficient …

The advent of cloud computing and advanced processing technologies has elevated deep
learning (DL) as a leading method for hyperspectral imaging (HSI) classification. Classifying …