315 citations · 623 across the 12 of their papers we have counts for
13 papers
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…
FuseTen: A Generative Model for Daily 10 m Land Surface Temperature Estimation from Spatio-Temporal Satellite Observations
Sofiane Bouaziz, Adel Hafiane, Raphael Canals +1
Urban heatwaves, droughts, and land degradation are pressing and growing challenges in the context of climate change. A valuable approach to studying them requires accurate spatio-…
Loss-Guided Model Sharing and Local Learning Correction in Decentralized Federated Learning for Crop Disease Classification
Denis Mamba Kabala, Adel Hafiane, Laurent Bobelin +1
Crop disease detection and classification is a critical challenge in agriculture, with major implications for productivity, food security, and environmental sustainability. While d…
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 for Spatio-Temporal Fusion in Land Surface Temperature Estimation: A Comprehensive Survey, Experimental Analysis, and Future Trends
Sofiane Bouaziz, Adel Hafiane, Raphael Canals +1
Land Surface Temperature (LST) plays a key role in climate monitoring, urban heat assessment, and land-atmosphere interactions. However, current thermal infrared satellite sensors…
Generative models-based data labeling for deep networks regression: application to seed maturity estimation from UAV multispectral images
Eric Dericquebourg, Adel Hafiane, Raphael Canals
Monitoring seed maturity is an increasing challenge in agriculture due to climate change and more restrictive practices. Seeds monitoring in the field is essential to optimize the…
Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images
Reenul Reedha, Eric Dericquebourg, Raphael Canals +1
Crop and weed monitoring is an important challenge for agriculture and food production nowadays. Thanks to recent advances in data acquisition and computation technologies, agricul…
VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map
Mohamed Kerkech, Adel Hafiane, Raphael Canals
Early detection of vine disease is important to avoid spread of virus or fungi. Disease propagation can lead to a huge loss of grape production and disastrous economic consequences…
Vine disease detection in UAV multispectral images with deep learning segmentation approach
Mohamed Kerkech, Adel Hafiane, Raphael Canals
One of the major goals of tomorrow's agriculture is to increase agricultural productivity but above all the quality of production while significantly reducing the use of inputs. Me…
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…