Browsing by Author "Muilu, Petteri"

Acidic sulfate soils (a.s. soils) decrease the soil pH and generate an acidic environment that is toxic for vegetation and aquatic life. Therefore, knowledge of potentially dangerous soils is necessary during, e.g. construction planning to reduce ecosystem service loss. Identifying a.s. soils accurately is a difficult task and require expensive and laborious field and lab tests. In order to identify larger areas cost-effectively, remote sensing methods could bring benefits to the construction planning field. This work shows that point clouds acquired with aerial LiDAR scanning can be used to remote sense acid sulfate soils with PointNet and PointNet++ deep learning models. However, additional research with larger data sets is required to improve the accuracy of the models for real-world applications. Therefore, work also suggests further research and improvement ideas for collecting data for such models.