Automatic map generation from nation-wide data sources using deep learning

• Main Author: Lundberg, Gustav
• Format: Others
• Language: English
• Published: Linköpings universitet, Statistik och maskininlärning 2020
• Subjects: deep learning; unstructured data; statistics; maps; point cloud; lidar; uncertainty map; data fusion; surface model; logistic regression; multinomial regression; convolutional neural network; pvcnn; Probability Theory and Statistics; Sannolikhetsteori och statistik; Other Computer and Information Science; Annan data- och informationsvetenskap
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170759

The last decade has seen great advances within the field of artificial intelligence. One of the most noteworthy areas is that of deep learning, which is nowadays used in everything from self driving cars to automated cancer screening. During the same time, the amount of spatial data encompassing not only two but three dimensions has also grown and whole cities and countries are being scanned. Combining these two technological advances enables the creation of detailed maps with a multitude of applications, civilian as well as military.This thesis aims at combining two data sources covering most of Sweden; laser data from LiDAR scans and surface model from aerial images, with deep learning to create maps of the terrain. The target is to learn a simplified version of orienteering maps as these are created with high precision by experienced map makers, and are a representation of how easy or hard it would be to traverse a given area on foot. The performance on different types of terrain are measured and it is found that open land and larger bodies of water is identified at a high rate, while trails are hard to recognize.It is further researched how the different densities found in the source data affect the performance of the models, and found that some terrain types, trails for instance, benefit from higher density data, Other features of the terrain, like roads and buildings are predicted with higher accuracy by lower density data.Finally, the certainty of the predictions is discussed and visualised by measuring the average entropy of predictions in an area. These visualisations highlight that although the predictions are far from perfect, the models are more certain about their predictions when they are correct than when they are not.