Photo: National Land Survey
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The National Land Survey of Finland (Maanmittauslaitos) has utilized artificial intelligence (AI) to correct inaccurate building location data across more than 1600 square kilometers within its terrain database. This innovative approach, which is part of an ongoing effort to train the AI model, aims to automatically improve the location data of buildings and water bodies within the database.
AI’s Role in Precision Mapping
The demand for highly accurate location data has surged in recent years, especially with the advent of autonomous driving. The project led by the National Land Survey of Finland has focused on enhancing the precision of terrain database location data, automating data collection, and ensuring the data’s timeliness.
“Discrepancies in the building location data in the terrain database can arise due to technical limitations, varying accuracy requirements over different periods, and potential human errors,” explains Lingli Zhu, project manager at the National Land Survey.
Successful AI Application in 11 Areas
During the project, AI corrected building location data in 11 regions including Kuopio, Savonlinna, Lahti, Vaala, Uusikaarlepyy, Uusikaupunki, Ylitornio, Parainen, Riihimäki, Jyväskylä, and Oulu. This correction process involved comparing the terrain database buildings with those identified by the AI and adjusting as necessary. The AI also aided in identifying missing and demolished buildings in the database.
While AI generally recognizes buildings effectively, densely forested areas still present challenges. Improvements in identifying problematic buildings are anticipated, with plans to utilize laser scanning data in the future.
Challenges and Opportunities with Water Bodies
Identifying water bodies poses different challenges for AI due to the varied nature of water features in the terrain. Ditches were the easiest to identify, with a high success rate in manual digitization. However, recognizing natural streams and ponds was more complex, hindered by factors like canopy cover and physical variability.
“AI-identified water bodies will help form a comprehensive network of waterways in the future,” says Zhu.
Towards a Higher-Quality Terrain Database
The terrain database serves as a unique, nationwide digital location data repository, widely utilized by municipalities and other organizations for various applications such as property taxes and addressing systems. The upcoming improvements will significantly enhance the quality of data in the terrain database, offering benefits across various fields.
“The future holds a significant reduction in manual work as the AI model gets trained with high-quality data, such as aerial images, covering all of Finland’s production areas,” Zhu anticipates.
This initiative by the National Land Survey of Finland marks a significant step in harnessing AI to refine and maintain the quality of critical national data re
Source: www.helsinkitimes.fi
