3 citations · 5 across the 7 of their papers we have counts for
7 papers
Tomographic SAR Reconstruction for Forest Height Estimation
Grace Colverd, Jumpei Takami, Laura Schade +2
Tree height estimation serves as an important proxy for biomass estimation in ecological and forestry applications. While traditional methods such as photogrammetry and Light Detec…
3D-SAR Tomography and Machine Learning for High-Resolution Tree Height Estimation
Grace Colverd, Jumpei Takami, Laura Schade +2
Accurately estimating forest biomass is crucial for global carbon cycle modelling and climate change mitigation. Tree height, a key factor in biomass calculations, can be measured…
M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data
Matthew J Allen, Francisco Dorr, Joseph Alejandro Gallego Mejia +4
Satellite-based remote sensing has revolutionised the way we address global challenges. Huge quantities of Earth Observation (EO) data are generated by satellite sensors daily, but…
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…