Zobrazeno 1 - 10
of 2 585
pro vyhledávání: '"Pollán A"'
Autor:
Walsh, James, Gass, Daniel G., Pollan, Raul Ramos, Wright, Paul J., Galvez, Richard, Kasmanoff, Noah, Naradowsky, Jason, Spalding, Anne, Parr, James, Baydin, Atılım Güneş
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 interactions into a multi-modal embedding space. This model can be used
Externí odkaz:
http://arxiv.org/abs/2410.02530
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 parameter-efficient technique, can significantly accelerate the adaptation
Externí odkaz:
http://arxiv.org/abs/2409.09907
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 leveraging an existing Foundation Model (FM) we must decide whether w
Externí odkaz:
http://arxiv.org/abs/2409.08744
Autor:
Allen, Matthew J, Dorr, Francisco, Mejia, Joseph Alejandro Gallego, Martínez-Ferrer, Laura, Jungbluth, Anna, Kalaitzis, Freddie, Ramos-Pollán, Raúl
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 processing these large datasets for use in ML pipelines is technicall
Externí odkaz:
http://arxiv.org/abs/2406.04230
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 MOVRP that aims to create routes that consider drivers' and operators' dec
Externí odkaz:
http://arxiv.org/abs/2405.16051
Autor:
Gallego-Mejia, Joseph A., Jungbluth, Anna, Martínez-Ferrer, Laura, Allen, Matt, Dorr, Francisco, Kalaitzis, Freddie, Ramos-Pollán, Raúl
Self-supervised learning (SSL) models have recently demonstrated remarkable performance across various tasks, including image segmentation. This study delves into the emergent characteristics of the Self-Distillation with No Labels (DINO) algorithm a
Externí odkaz:
http://arxiv.org/abs/2310.03513
Autor:
Martínez-Ferrer, Laura, Jungbluth, Anna, Gallego-Mejia, Joseph A., Allen, Matt, Dorr, Francisco, Kalaitzis, Freddie, Ramos-Pollán, Raúl
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 on smaller labeled datasets to predict vegetation percentage, and empir
Externí odkaz:
http://arxiv.org/abs/2310.02048
Autor:
Allen, Matt, Dorr, Francisco, Gallego-Mejia, Joseph A., Martínez-Ferrer, Laura, Jungbluth, Anna, Kalaitzis, Freddie, Ramos-Pollán, Raúl
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 sensors can be used to inform intervention and policy decision making
Externí odkaz:
http://arxiv.org/abs/2310.00826
Autor:
Allen, Matt, Dorr, Francisco, Gallego-Mejia, Joseph A., Martínez-Ferrer, Laura, Jungbluth, Anna, Kalaitzis, Freddie, Ramos-Pollán, Raúl
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 Sentinel 2 RGB optical imagery, Sentinel 1 SAR radar amplitude and interferom
Externí odkaz:
http://arxiv.org/abs/2310.00119
Autor:
Ramos-Pollán, Raúl, González, Fabio A.
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 capabilities and restricted uplink bandwidths to upload. We aim at enabling
Externí odkaz:
http://arxiv.org/abs/2306.12461