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Found 11 papers, 7 papers with code
Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks
With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation.
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment
Our two-stage procedure, comprises of robust fine-tuning CLIP in order to deal with the distribution shift, accompanied by the cross-modal alignment of a RS modality encoder, in an effort to extend the zero-shot capabilities of CLIP.
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.
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)
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum.
