Data for Improving Stream Network Accuracy with Deep Learning-Enhanced Detection of Road Culverts in High-Resolution Digital Elevation Models

This is the training and testing data used to train a Residual Attention UNet for segmentation and detection of road culverts. The data consists of pairs of images with the size 256x256 pixels where one image is a labeled mask and the other a image with four channels containing the remote sensing data. The remote sensing data is a combination of topographical data extracted from arial laser scanning and ortophotos from arial imagery.

An extensive culvert survey was conducted in 25 watersheds in central Sweden by the Swedish Forest Agency during the snow-free periods of 2014–2017. A total of 24,083 culverts were mapped with a handheld GPS with a horizontal accuracy of 0.3 m. Densely populated urban areas with underground drainage systems were excluded from the survey (0.3% of the combined area). The coordinates of both ends of each culvert were measured, and metrics such as diameter, length, material, working condition, and sediment accumulation were collected for most of the culverts. Additional metrics, such as the elevation difference between the outlet and stream water level, were manually measured with a ruler. The inventoried watersheds were split up into training and testing data, where 20 watersheds (23,304 culverts) were used for training, and five watersheds (5,208 culverts) were used for testing.

A compact laser-based system (Leica ALS80-HP-8236) was used to collect the ALS data from an aircraft flying at 2888–3000 m. The ALS point clouds had a point density of 1–2 points m-2 and were divided into tiles with a size of 2.5 x 2.5 km each. A DEM with 0.5 m resolution was created from the ALS point clouds using a TIN gridding approach implemented in Whitebox tools 2.2.0. The topographical index max downslope elevation change was calculated from the DEM using Whitebox Tools . Max downslope elevation change represents the maximum elevation drop between each grid cell and its neighbouring cells within a DEM. This typically resulted in values between 0 and 10.

Orthophotos from aerial imagery captured at the same time as the lidar data is also included. The orthophotos had three bands (red, green and blue) in 8-bit color depth and had a resolution of 0.5 m. The LiDAR data and orthophotos were downloaded from the Swedish mapping, cadastral and land registration authority.

The topographical data and the ortophotos were merged into 8-bit four band images where the first three band is red, green and blue, and the last band is max downslope elevation change. The merged images where then split into smaller tiles with the size 256x256 pixels.

The trained model was used to predict culverts in Sweden and the file PredictedCulvertsByIsobasins.zip contains the predicted culverts stored as shapefiles split by the watersheds in the file "isobasins.zip". Show less..