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

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

Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …

The revolutionary capabilities of large language models (LLMs) have paved the way for
multimodal large language models (MLLMs) and fostered diverse applications across …

Remote sensing image scene classification is an active and challenging task driven by
many applications. More recently, with the advances of deep learning models especially …

This work addresses the problem of semantic foggy scene understanding (SFSU). Although
extensive research has been performed on image dehazing and on semantic scene …

Remote sensing image scene classification plays an important role in a wide range of
applications and hence has been receiving remarkable attention. During the past years …

Aerial scene classification, which aims to automatically label an aerial image with a specific
semantic category, is a fundamental problem for understanding high-resolution remote …

This paper introduces the use of single-layer and deep convolutional networks for remote
sensing data analysis. Direct application to multi-and hyperspectral imagery of supervised …

Remote sensing image scene classification has attracted great attention because of its wide
applications. Although convolutional neural network (CNN)-based methods for scene …