Mapping Small Watercourses from DEMs with Deep Learning—Exploring the Causes of False Predictions

Abstract

Vector datasets of small watercourses, such as rivulets, streams, and ditches, are important for many visualization and analysis use cases. Mapping small watercourses with traditional methods is laborious and costly. Convolutional neural networks (CNNs) are state-of-the-art computer vision methods that have been shown to be effective for extracting geospatial features, including small watercourses, from LiDAR point clouds, digital elevation models (DEMs), and aerial images. However, the cause of the false predictions by machine-learning models is often not thoroughly explored, and thus the impact of the results on the process of producing accurate datasets is not well understood. We digitized a highly accurate and complete dataset of small watercourses from a study area in Finland. We then developed a process based on a CNN that can be used to extract small watercourses from DEMs. We tested and validated the performance of the network with different input data layers, and their combinations to determine the best-performing layer. We analyzed the false predictions to gain an understanding of their nature. We also trained models where watercourses with high levels of uncertainty were removed from the training sets and compared the results to training models with all watercourses in the training set. The results show that the DEM was the best-performing layer and that combinations of layers provided worse results. Major causes of false predictions were shown to be boundary errors with an offset between the prediction and labeled data, as well as errors of omission by watercourses with high levels of uncertainty. Removing features with the highest level of uncertainty from the labeled dataset increased the overall f1-score but reduced the recall of the remaining features. Additional research is required to determine if the results remain similar to other CNN methods.

Keywords

• mapping
• watercourses
• deep learning
• semantic segmentation
• lidar
• digital elevation model