1 citations · 1 across the 6 of their papers we have counts for
6 papers · 1 filter
Uncertainty-Aware Test-Time Adaptation for Cross-Region Spatio-Temporal Fusion of Land Surface Temperature
Sofiane Bouaziz, Adel Hafiane, Raphael Canals +1
Deep learning models have shown great promise in diverse remote sensing applications. However, they often struggle to generalize across geographic regions unseen during training du…
SPyCer: Semi-Supervised Physics-Guided Contextual Attention for Near-Surface Air Temperature Estimation from Satellite Imagery
Sofiane Bouaziz, Adel Hafiane, Raphael Canals +1
Modern Earth observation relies on satellites to capture detailed surface properties. Yet, many phenomena that affect humans and ecosystems unfold in the atmosphere close to the su…
Deep semi-supervised approach based on consistency regularization and similarity learning for weeds classification
Farouq Benchallal, Adel Hafiane, Nicolas Ragot +1
Weed species classification represents an important step for the development of automated targeting systems that allow the adoption of precision agriculture practices. To reduce co…
WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface Temperature Estimation via Spatio-Temporal Fusion
Sofiane Bouaziz, Adel Hafiane, Raphael Canals +1
Urbanization, climate change, and agricultural stress are increasing the demand for precise and timely environmental monitoring. Land Surface Temperature (LST) is a key variable in…
Enhancing DeepLabV3+ to Fuse Aerial and Satellite Images for Semantic Segmentation
Anas Berka, Mohamed El Hajji, Raphael Canals +2
Aerial and satellite imagery are inherently complementary remote sensing sources, offering high-resolution detail alongside expansive spatial coverage. However, the use of these so…
Deep Learning with unsupervised data labeling for weeds detection on UAV images
M. Dian. Bah, Adel Hafiane, Raphael Canals
In modern agriculture, usually weeds control consists in spraying herbicides all over the agricultural field. This practice involves significant waste and cost of herbicide for far…