Search Results for author:
Found 10 papers, 8 papers with code
Kuro Siwo: 12.1 billion $m^2$ under the water. A global multi-temporal satellite dataset for rapid flood mapping
Kuro Siwo stands out for its unparalleled annotation quality to facilitate rapid flood mapping in a supervised setting.
FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2
We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas, approached as a change detection task.
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting
To achieve such accurate long-term forecasts at a global scale, it is crucial to employ models that account for the Earth system's inherent spatio-temporal interactions, such as memory effects and teleconnections.
Deep Learning for Global Wildfire Forecasting
We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time.
Hephaestus: A large scale multitask dataset towards InSAR understanding
Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products in particular, are one of the largest sources of Earth Observation data.
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning.
Self-supervised Contrastive Learning for Volcanic Unrest Detection
Ground deformation measured from Interferometric Synthetic Aperture Radar (InSAR) data is considered a sign of volcanic unrest, statistically linked to a volcanic eruption.
Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection
We report detection accuracy that amounts to the highest reported accuracy on a large test set for volcanic unrest detection.
Benchmarking and scaling of deep learning models for land cover image classification
In this work, we use the BigEarthNet Sentinel-2 dataset to benchmark for the first time different state-of-the-art DL models for the multi-label, multi-class LULC image classification problem, contributing with an exhaustive zoo of 60 trained models.
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet
(official split metric)
Deep Learning Methods for Daily Wildfire Danger Forecasting
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability.
