Jere Raninen

Article Examining data combinations for improved accuracy in rural and urban environments • With the expansion of artificial intelligence (AI) applications, automating building extraction from remote sensing data has gained significant traction. By leveraging Lidar and aerial-image digital surface models with deep learning, a Finnish study has explored different data combinations that enhance building detection accuracy in rural and urban areas.

5. Discussion In one approach, when a network trained on a large dataset is fine-tuned, the shallow layers focusing on the lower features of data are kept unchanged, and the deeper layers are modified according to the new data to which the network will be adapted.

The National Land Survey of Finland (NLS) is a good example of how artificial intelligence (AI) is entering national mapping agencies. Approximately two years ago, NLS set up the Advanced Technology for National Topographic Map Updating (ATMU) project supported by funding from the Finnish Ministry of Finance. This project was focused on developing deep learning solutions for object detection and change recognition, for buildings, roads and hydrographic features (watercourses).