409 citations · 1.2k across the 27 of their papers we have counts for
7 papers · 1 filter
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery
Joseph A. Gallego-Mejia, Anna Jungbluth, Laura Martínez-Ferrer +4
Self-supervised learning (SSL) models have recently demonstrated remarkable performance across various tasks, including image segmentation. This study delves into the emergent char…
Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction
Laura Martínez-Ferrer, Anna Jungbluth, Joseph A. Gallego-Mejia +4
In this work we pre-train a DINO-ViT based model using two Synthetic Aperture Radar datasets (S1GRD or GSSIC) across three regions (China, Conus, Europe). We fine-tune the models o…
Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data
Matt Allen, Francisco Dorr, Joseph A. Gallego-Mejia +4
Satellite-based remote sensing is instrumental in the monitoring and mitigation of the effects of anthropogenic climate change. Large scale, high resolution data derived from these…
Fewshot learning on global multimodal embeddings for earth observation tasks
Matt Allen, Francisco Dorr, Joseph A. Gallego-Mejia +4
In this work we pretrain a CLIP/ViT based model using three different modalities of satellite imagery across five AOIs covering over ~10\% of Earth's total landmass, namely Sentine…
On-orbit model training for satellite imagery with label proportions
Raúl Ramos-Pollán, Fabio A. González
This work addresses the challenge of training supervised machine or deep learning models on orbiting platforms where we are generally constrained by limited on-board hardware capab…
Kernel Density Matrices for Probabilistic Deep Learning
Fabio A. González, Raúl Ramos-Pollán, Joseph A. Gallego-Mejia
This paper introduces a novel approach to probabilistic deep learning, kernel density matrices, which provide a simpler yet effective mechanism for representing joint probability d…
Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic Labels
Omar A. Castaño-Idarraga, Raul Ramos-Pollán, Freddie Kalaitzis
This work proposes a hybrid unsupervised and supervised learning method to pre-train models applied in Earth observation downstream tasks when only a handful of labels denoting ver…