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 …

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

Remote sensing imagery, despite its broad applications in hel** achieve Sustainable
Development Goals and tackle climate change, has not yet benefited from the recent …

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 …

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

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 …

Abstract We present AiTLAS: Benchmark Arena–an open-source benchmark suite for
evaluating state-of-the-art deep learning approaches for image classification in Earth …

Geospatial technologies are becoming increasingly essential in our world for a wide range
of applications, including agriculture, urban planning, and disaster response. To help …