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Found 2 papers, 2 papers with code
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning
Inspired by the abundance of publicly available remote sensing projects and the burgeoning development of deep learning in computer vision, our research focuses on assessing fire risk using remote sensing imagery.
Ranked #1 on
Remote Sensing Image Classification
on FireRisk
Extending global-local view alignment for self-supervised learning with remote sensing imagery
While prior studies have explored multiple self-supervised learning techniques in remote sensing domain, pretext tasks based on local-global view alignment remain underexplored, despite achieving state-of-the-art results on natural imagery.
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet-10%
(using extra training data)
