Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …

In this paper, we present a patch-based land use and land cover classification approach
using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely …

This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2
benchmark archive. The BigEarthNet consists of 590, 326 Sentinel-2 image patches, each of …

Efficiently implementing remote sensing image classification with high spatial resolution
imagery can provide significant value in land use and land cover (LULC) classification. The …

Deep neural networks (DNNs) often perform poorly in the presence of domain shift and
category shift. How to upcycle DNNs and adapt them to the target task remains an important …

The blooming proliferation of aeronautics and astronautics platforms, together with the ever-
increasing remote sensing imaging sensors on these platforms, has led to the formation of …

Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate
construction materials and provide detailed geographic information. In remote sensing …

Remote-sensing image scene classification can provide significant value, ranging from
forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial …

General-purpose foundation models have led to recent breakthroughs in artificial
intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image …