wildfires

Fire Hazard Forecasting (FireWatchGreece)

Orion Lab has developed deep learning methods for daily wildfire danger forecasting. We approach daily fire danger prediction as a machine learning task, using historical Earth observation data from the last decade to predict next-day’s fire danger. Our work aims at producing fire hazard forecasts at large scales and with better spatial resolution compared to other existing methods and operational tools.

Fire Hazard Forecasting (FireWatchGreece)
FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2
Over the last decade there has been an increasing frequency and intensity of wildfires across the globe, posing significant threats to …
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FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2
Self-supervised learning on datacubes for wildfire forecasting

Our lab has created large-scale datasets for wildfire forecasting (Greek Datacube, Mesogeos, SeasFire Datacube). The potential of these datasets remains untapped and could potentially be harvested with Self-Supervised Learning (SSL) methods.

Burnt area mapping with aerial data
The aim of this thesis is to leverage aerial data for the automatic mapping of areas burnt by wildfires. The data were captured by a Light Manned Aircraft (LMA) during 3 separate flights over Delfi, Domokos, Yliki and Prodromos.
Burnt area mapping with aerial data
Burnt area mapping with MODIS data
Orion Lab has assembled a novel benchmark dataset, namely FLOGA [1], for the mapping of burnt areas in the Greek region. The dataset comprises bitemporal image acquisitions from Sentinel-2 and MODIS satellites and the ground truth labels are photointerpretation mappings produced by Hellenic Fire Brigade experts.
Burnt area mapping with MODIS data
Data curation and burnt area mapping with SAR
Orion Lab has assembled a novel benchmark dataset, namely FLOGA [1], for the mapping of burnt areas in the Greek region. The dataset comprises bitemporal image acquisitions from Sentinel-2 and MODIS satellites and the ground truth labels are photointerpretation mappings produced by Hellenic Fire Brigade experts.
Data curation and burnt area mapping with SAR
Data curation and model finetuning for burnt area mapping
Orion Lab has assembled a novel benchmark dataset, namely FLOGA [1], for the mapping of burnt areas in the Greek region. The dataset comprises bitemporal image acquisitions from Sentinel-2 and MODIS satellites and the ground truth labels are photointerpretation mappings produced by Hellenic Fire Brigade experts.
Data curation and model finetuning for burnt area mapping
Deep Learning for Global Wildfire Forecasting
Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to …
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Deep Learning for Global Wildfire Forecasting
Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables …
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Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting
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TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting