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…