A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation
Change detection in Earth observation (EO) is critical for monitoring land surface transformations, yet recent research in the field is constrained by inconsistent evaluation protocols and a narrow focus on predictive accuracy without regard for computational efficiency. To address this, we present a standardized, open-source benchmark for evaluating state-of-the-art (SOTA) deep learning methods for Earth observation change detection. We conduct a comprehensive analysis of ten representative model architectures, ranging from convolutional networks (CNNs) to vision transformers (ViTs), across t
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- PossiblePossibly related (embedding) · 47%World Deep Learning in Machine Vision - Market Analysis, Forecast, Size, Trends and Insights - IndexBox →
- FuzzySimilar title/name (fuzzy) · 59%jeinlee1991/chinese-llm-benchmark →
“Fuzzy title match (0.73): “A comprehensive and trustworthy benchmark of AI methods for ” ≈ “jeinlee1991/chinese-llm-benchmark””
- LinkedLinked via arxiv author · 85%Tadej Tomanič →
“A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation”
- LinkedLinked via arxiv author · 85%Alice Baudhuin →
“A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation”
- LinkedLinked via arxiv author · 85%Jan Sotošek →
“A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation”
- LinkedLinked via arxiv author · 85%Jure Brence →
“A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation”
- LinkedLinked via arxiv author · 85%Panče Panov →
“A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation”
- LinkedLinked via arxiv author · 85%Nikola Simidjievski →
“A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation”