5 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation
Tadej Tomanič, Alice Baudhuin, Jan Sotošek +4
Change detection in Earth observation (EO) is critical for monitoring land surface transformations, yet recent research in the field is constrained by inconsistent evaluation proto…
MAPLE: Multi-Path Adaptive Propagation with Level-Aware Embeddings for Hierarchical Multi-Label Image Classification
Boshko Koloski, Marjan Stoimchev, Jurica Levatić +2
Hierarchical multi-label classification (HMLC) is essential for modeling structured label dependencies in remote sensing. Yet existing approaches struggle in multi-path settings, w…
HELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification
Marjan Stoimchev, Boshko Koloski, Jurica Levatić +2
Hierarchical multi-label classification (HMLC) is essential for modeling complex label dependencies in remote sensing. Existing methods, however, struggle with multi-path hierarchi…
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification
Ivica Dimitrovski, Ivan Kitanovski, Nikola Simidjievski +1
We investigate the utility of in-domain self-supervised pre-training of vision models in the analysis of remote sensing imagery. Self-supervised learning (SSL) has emerged as a pro…
AiTLAS: Artificial Intelligence Toolbox for Earth Observation
Ivica Dimitrovski, Ivan Kitanovski, Panče Panov +2
The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite i…