Datasets and Publications

The Aristotle University of Thessaloniki (hereinafter, AUTH) created the following dataset, entitled ‘3D-Flood’, within the context of the project TEMA that was funded by the European Commission-European Union.

The dataset will be used for the construction of a 3D model regarding the district of Agios Thomas in Larisa, Greece, after the flood events of 2023. It is comprised of 795 UAV video frames, taken from 4 YouTube videos.

We provide the links for each YouTube video, along with the frame numbers that we kept for each video.

Details on acquiring the dataset can be found here.

Our Master Flood Dataset consists of flood images picked from different publicly available datasets. The origins of the images is specified in the "sources.csv" file.

The dataset consists of 282 train, 87 validation and 1973 test frames. We provide the frames from the sourced videos and segmentation masks of the flooded areas.

Details on acquiring the dataset can be found here. 

The dataset is destined to be used for wildfire image classification and burnt area segmentation tasks for Unmanned Aerial Vehicles. It is comprised of 5,408 frames of aerial views taken from 56 videos and 2 public datasets. From the D-Fire public dataset, 829 photographs were used; and from the Burned Area UAV public dataset 34 images were used. For the classification task, there are 5 classes (‘Burnt’, ‘Half-Burnt’, ’Non-Burnt’, ‘Fire’, ‘Smoke’). As for the segmentation task, 404 segmentation masks on a subset have been created, which assign to each pixel of the image the class ‘burnt’ or the class ‘non-burnt’.

Details on acquiring the dataset can be found here. 

The dataset comprises of 766 social media posts in Greek language from the platform “Mastodon” spanning the 2023 wildfires in Greece. Each post was annotated internally with Plutchik-8 emotions. 

Details on acquiring the dataset can be found here.