409 citations · 1.2k across the 27 of their papers we have counts for
28 papers
LunarFM: A Shared Multimodal Representation of the Moon's Surface
Marc Girona-Mata, Jakob Gawlikowski, Sumit Goski +6
The renewed global focus on lunar exploration, driven by the prospect of in-situ resource utilization and a sustained human presence on the Moon, has created growing demand for acc…
Zero-Shot Satellite Image Retrieval through Joint Embeddings: Application to Crisis Response
James Walsh, William Fawcett, Grace Colverd +1
Semantic search of Earth observation archives remains challenging. Visual foundation models such as CLAY produce rich embeddings of satellite imagery but lack the natural-language…
A Foundation Model for the Solar Dynamics Observatory
James Walsh, Daniel G. Gass, Raul Ramos Pollan +7
SDO-FM is a foundation model using data from NASA's Solar Dynamics Observatory (SDO) spacecraft; integrating three separate instruments to encapsulate the Sun's complex physical in…
Rapid Adaptation of Earth Observation Foundation Models for Segmentation
Karthick Panner Selvam, Raul Ramos-Pollan, Freddie Kalaitzis
This study investigates the efficacy of Low-Rank Adaptation (LoRA) in fine-tuning Earth Observation (EO) foundation models for flood segmentation. We hypothesize that LoRA, a param…
Uncertainty and Generalizability in Foundation Models for Earth Observation
Raul Ramos-Pollan, Freddie Kalaitzis, Karthick Panner Selvam
We take the perspective in which we want to design a downstream task (such as estimating vegetation coverage) on a certain area of interest (AOI) with a limited labeling budget. By…
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…
A Bi-Objective Approach to Last-Mile Delivery Routing Considering Driver Preferences
Juan Pablo Mesa, Alejandro Montoya, Raul Ramos-Pollán +1
The Multi-Objective Vehicle Routing Problem (MOVRP) is a complex optimization problem in the transportation and logistics industry. This paper proposes a novel approach to the MOVR…
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…
Deep learning based landslide density estimation on SAR data for rapid response
Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh +5
This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources for rapid response. We use the Unit…
SAR-based landslide classification pretraining leads to better segmentation
Vanessa Böhm, Wei Ji Leong, Ragini Bal Mahesh +5
Rapid assessment after a natural disaster is key for prioritizing emergency resources. In the case of landslides, rapid assessment involves determining the extent of the area affec…
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes
Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh +5
With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency response…
The Pierre Auger Observatory: Contributions to the 36th International Cosmic Ray Conference (ICRC 2019)
The Pierre Auger Collaboration, A. Aab, P. Abreu +383
Contributions of the Pierre Auger Collaboration to the 36th International Cosmic Ray Conference (ICRC 2019), 24 July - 1 August 2019, Madison, Wisconsin, USA.
Probing the origin of ultra-high-energy cosmic rays with neutrinos in the EeV energy range using the Pierre Auger Observatory
The Pierre Auger Collaboration, A. Aab, P. Abreu +389
Neutrinos with energies above eV are detectable with the Surface Detector Array of the Pierre Auger Observatory. The identification is efficiently performed for neutrinos…
Limits on point-like sources of ultra-high-energy neutrinos with the Pierre Auger Observatory
The Pierre Auger Collaboration, A. Aab, P. Abreu +389
With the Surface Detector array (SD) of the Pierre Auger Observatory we can detect neutrinos with energy between eV and eV from point-like sources across the…
Measurement of the average shape of longitudinal profiles of cosmic-ray air showers at the Pierre Auger Observatory
The Pierre Auger Collaboration, A. Aab, P. Abreu +385
The profile of the longitudinal development of showers produced by ultra-high energy cosmic rays carries information related to the interaction properties of the primary particles…
Large-scale cosmic-ray anisotropies above 4 EeV measured by the Pierre Auger Observatory
The Pierre Auger Collaboration, A. Aab, P. Abreu +374
We present a detailed study of the large-scale anisotropies of cosmic rays with energies above 4 EeV measured using the Pierre Auger Observatory. For the energy bins [4,8] EeV and…
Observation of inclined EeV air showers with the radio detector of the Pierre Auger Observatory
The Pierre Auger Collaboration, A. Aab, P. Abreu +392
With the Auger Engineering Radio Array (AERA) of the Pierre Auger Observatory, we have observed the radio emission from 561 extensive air showers with zenith angles between 60$^\ci…
Indication of anisotropy in arrival directions of ultra-high-energy cosmic rays through comparison to the flux pattern of extragalactic gamma-ray sources
The Pierre Auger Collaboration, A. Aab, P. Abreu +390
A new analysis of the dataset from the Pierre Auger Observatory provides evidence for anisotropy in the arrival directions of ultra-high-energy cosmic rays on an intermediate angul…