Boreal Forest Fire: UAV-collected Wildfire Detection and Smoke Segmentation Dataset

This dataset consists of annotated images and videos of smoke resulting from prescribed burning events in Finnish boreal forests. The dataset was created to train and validate learning-based methods for wildfire detection and smoke segmentation and its effectiveness in doing so was shown in the linked studies.

The data was captured as 4K (3840×2160) videos at four events in Evo, Heinola, Karkkila, and Ruokolahti, using a DJI Phantom 4 drone. Individual frames of the videos were annotated manually with bounding boxes to enable the use of the data to train and test wildfire detection models. A portion of the bounding box annotated image data was resorted and annotated at the pixel level for image segmentation model training, validation, and testing. The training and validation data were annotated automatically using the Segment Anything Model and the manually annotated bounding boxes, while a small test set was annotated with manually drawn pixelwise masks.

The three parts of the dataset are stored in the three separate directories:

* Boreal-Forest-Fire-Subset-A: The bounding box annotated image data
* Boreal-Forest-Fire-Subset-B: 30-second 4K video clips with binary annotations for smoke
* Boreal-Forest-Fire-Subset-C: The segmentation mask annotated image data

In addition, a Jupyter notebook providing an example for visualising and converting the bounding box coordinates is provided in the Visualisation-And-Coordinate-Conversion-Notebook directory. Code for using the segmentation data is found in the linked Gitlab repository.

Some errors were found in the video labels after archiving the data. Refer to the video label directory (Boreal-Forest-Fire/Boreal-Forest-Fire-Subset-B/Video-Ground-Truth) metadata for accurate information.

Detailed data description is found in our Scientific Data article: https://doi.org/10.1038/s41597-025-05634-0