Using satellite imagery to understand and promote sustainable development
BACKGROUND Accurate and comprehensive measurements of a range of sustainable
development outcomes are fundamental inputs into both research and policy. For instance …
development outcomes are fundamental inputs into both research and policy. For instance …
Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery
Unsupervised pre-training methods for large vision models have shown to enhance
performance on downstream supervised tasks. Develo** similar techniques for satellite …
performance on downstream supervised tasks. Develo** similar techniques for satellite …
Geography-aware self-supervised learning
Contrastive learning methods have significantly narrowed the gap between supervised and
unsupervised learning on computer vision tasks. In this paper, we explore their application …
unsupervised learning on computer vision tasks. In this paper, we explore their application …
Domino: Discovering systematic errors with cross-modal embeddings
S Eyuboglu, M Varma, K Saab, JB Delbrouck… - ar** from high resolution remote sensing images
The combination of high-resolution satellite imagery and machine learning have proven
useful in many sustainability-related tasks, including poverty prediction, infrastructure …
useful in many sustainability-related tasks, including poverty prediction, infrastructure …
Urbanclip: Learning text-enhanced urban region profiling with contrastive language-image pretraining from the web
Urban region profiling from web-sourced data is of utmost importance for urban computing.
We are witnessing a blossom of LLMs for various fields, especially in multi-modal data …
We are witnessing a blossom of LLMs for various fields, especially in multi-modal data …
Contrastive multiview coding with electro-optics for SAR semantic segmentation
In the training of deep learning models, how the model parameters are initialized greatly
affects the model performance, sample efficiency, and convergence speed. Recently …
affects the model performance, sample efficiency, and convergence speed. Recently …