60 citations · 150 across the 17 of their papers we have counts for
6 papers · 1 filter
SpectralEarth: Training Hyperspectral Foundation Models at Scale
Nassim Ait Ali Braham, Conrad M Albrecht, Julien Mairal +3
Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potent…
Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining
Yi Wang, Conrad M Albrecht, Xiao Xiang Zhu
Self-supervised pretraining on large-scale satellite data has raised great interest in building Earth observation (EO) foundation models. However, many important resources beyond p…
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation
Chenying Liu, Conrad Albrecht, Yi Wang +1
We explore the potential of large-scale noisily labeled data to enhance feature learning by pretraining semantic segmentation models within a multi-modal framework for geospatial a…
AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation
Chenying Liu, Conrad M Albrecht, Yi Wang +2
While the volume of remote sensing data is increasing daily, deep learning in Earth Observation faces lack of accurate annotations for supervised optimization. Crowdsourcing projec…
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation
Chenying Liu, Conrad M Albrecht, Yi Wang +1
Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level informa…
One for All: Toward Unified Foundation Models for Earth Vision
Zhitong Xiong, Yi Wang, Fahong Zhang +1
Foundation models characterized by extensive parameters and trained on large-scale datasets have demonstrated remarkable efficacy across various downstream tasks for remote sensing…