A high-resolution dataset for forest disturbance mapping

The Deep4Dist dataset is a comprehensive, high-resolution remote sensing data product specifically designed for forest disturbance mapping. It comprises approximately 17,500 georeferenced image patches extracted from high-resolution digital orthophotos acquired in Rhineland-Palatinate, Germany. Each image patch measures 500 × 500 pixels at a spatial resolution of 20 cm and contains five spectral channels: red, green, blue, near-infrared (NIR), and a normalized digital surface model (nDSM). Together, these channels capture both spectral and structural information essential for distinguishing various forest disturbance types, including bark beetle damage, clear-cuts, and windthrow events.

Key Features:

High Resolution: 20 cm spatial resolution enables fine-grained mapping of forest disturbances.

Multiple Disturbance Classes: Bark beetle damage, clear-cut and windthrow.

Multispectral & Structural Data: Five channels (RGB, NIR, and nDSM) provide detailed spectral and structural insights.

Large-Scale Coverage: ~17,500 georeferenced image patches support robust statistical analysis and deep learning applications.

Rigorous Curation: Data were generated from high-resolution digital orthophotos and ground disturbance records. Extensive quality control, including expert-based external validation.

Deep Learning Ready: The dataset is organized and annotated for direct use in semantic segmentation tasks. Train (~70%), validation (~25%) and test (~5%) splits are provided.

Applications:

Deep4Dist is ideally suited for:

Developing and validating deep learning models for forest disturbance mapping.

Investigating the spatial dynamics of forest disturbances.

Supporting adaptive forest management and conservation strategies.

Integrating with medium-resolution satellite data for multi-modal forest disturbance mapping.

Class Description:

The classes in the segmentation masks are encoded as integers ranging from 0 to 3, corresponding to:

0: Background

1: Bark beetle damage

2: Clear-cuts

3: Windthrow

Metadata Description:

The metadata.csv file contains the following fields and information:

tile_name: corresponding to the image/mask name

split: the assigned data partition set (train, validation, test)

x_center: the x coordinate of the tile centroid (EPSG:25832)

y_center: the y coordinate of the tile centroid (EPSG:25832)

acquisition_date: aerial image acquisition/flight date

Spatial Data Description:

The tile_geometries.gpkg is a vector file containing the geometries (polygons) for each image sample (EPSG:25832).  

Coordinate Reference System: Both, images and masks are georeferenced using the EPSG: 25832 (DE_ETRS89_UTM32) crs. 

Folder Structure:

Each folder-set (train, validation and test), contains the subfolders "image" and "mask", where the 5-channels aerial images and dense pixel labels are stored. 

Additional Resources:

Code : The GitHub repository (https://github.com/enmanuelrodpau/deep4dist) contains code for model training and dataset validation.

Model weights: The HuggingFace repository (https://huggingface.co/enmanuelrp/Dee4Dist-ResU-net-34)  holds the pretrained model weights.

Acknowledgement: