42 citations · 144 across the 23 of their papers we have counts for
6 papers · 1 filter
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong +3
Self-supervised pre-training bears potential to generate expressive representations without human annotation. Most pre-training in Earth observation (EO) are based on ImageNet or m…
Aboveground carbon biomass estimate with Physics-informed deep network
Juan Nathaniel, Levente J. Klein, Campbell D. Watson +2
The global carbon cycle is a key process to understand how our climate is changing. However, monitoring the dynamics is difficult because a high-resolution robust measurement of ke…
Monitoring Urban Forests from Auto-Generated Segmentation Maps
Conrad M Albrecht, Chenying Liu, Yi Wang +2
We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interac…
Self-supervised Learning in Remote Sensing: A Review
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham +2
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While the…
Self-supervised Vision Transformers for Joint SAR-optical Representation Learning
Yi Wang, Conrad M Albrecht, Xiao Xiang Zhu
Self-supervised learning (SSL) has attracted much interest in remote sensing and earth observation due to its ability to learn task-agnostic representations without human annotatio…
AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning
Conrad M Albrecht, Fernando Marianno, Levente J Klein
A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is…