In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

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 …

Transformer architectures have become state-of-the-art models in computer vision and
natural language processing. To a significant degree, their success can be attributed to self …

Automatic remote sensing tools can help inform many large-scale challenges such as
disaster management, climate change, etc. While a vast amount of spatio-temporal satellite …

Remote sensing images are useful for a wide variety of planet monitoring applications, from
tracking deforestation to tackling illegal fishing. The Earth is extremely diverse---the amount …

Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …

A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled,
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …

In recent years, internet technology has grown in advance, and multimedia data-sharing
growth rates have skyrocketed. As a result, protecting multimedia data in digital networks …

Deep learning methods have become an integral part of computer vision and machine
learning research by providing significant improvement performed in many tasks such as …

Self-supervised learning (SSL) has gained wide-spread attention in the remote sensing (RS)
and Earth observation (EO) communities owing to its ability to learn task-agnostic …