LaserVesi collaboration in Finland will create nationwide, high-detail land cover information
- New data
Sometimes, progress requires great partners. That is certainly the case in Finland, where SCALGO has entered a unique project called ‘LaserVesi’ in cooperation with the City of Helsinki, the water utility company HSY and the Finnish Environmental Institute (SYKE). The goal of the pilot project is to develop detailed, high-resolution land cover maps for the entire “Land of a Thousand Lakes”.
It all started last year, when the Finnish government began collecting massive amounts of new point cloud data using the latest airplane-based laser scanning techniques. To increase the value of this massive data set, the Ministry of Agriculture and Forestry then launched pilot projects in which the new data could be tested and used to develop new more detailed information.
One of these projects was LaserVesi.
With LaserVesi (Finish for ‘Laser Water’) the new data set will be used to produce data for the water sector. SYKE, The Finnish Environment Institute, is leading the LaserVesi project and invited SCALGO to join.
“From the very beginning it was obvious that to achieve our ambitious goals we need to collaborate with partners with extensive experience to create models from massive data sets. SCALGO’s experience from creating a country wide permeability map in Denmark was a reference that convinced us,” says Pasi Valkama, Senior Research Scientist, Project manager for LaserVesi, SYKE.
In LaserVesi, SCALGO will use advanced machine learning to first develop a detailed landcover map for the Helsinki region. Outi Kesäniemi, a Regional Information Specialist at utility company HSY’s Climate Unit says detailed data on impervious surfaces will be used for stormwater management and climate change adaptation monitoring.
“HSY has been producing a land cover classification for the Helsinki Metropolitan Area since 2014, but so far, the classification of sand and asphalt surfaces from orthophotos has been very difficult. We are excited to see the possibilities of machine learning in this task, and we look forward to getting a more reliable classification of sand and asphalt surfaces,” she says.
Once the pilot is proven successful in Helsinki, the method can be implemented across Finland to develop a similarly detailed land cover map. This use of data at a national scale is unique and builds upon SCALGO’s unique data processing technology.
”We are mainly hoping to see more detailed information about the regional land cover data. The data is constantly used in many different analyses, including stormwater management and more precise data means more precise results,” says Valtteri Lankiniemi, M.Sc, of the City of Helsinki’s Urban Environment Division.
“The possibility to compose regional information about impermeability and runoff coefficients would greatly enhance the process of creating stormwater models. We also see potential in the new datasets when evaluating the effects of land use on green areas,” he says.
For SCALGO, an important aspect of the LaserVesi project is that we can continue to train our Machine Learning algorithms. Adding data for more countries will make the models even more accurate within each country. We see a great potential in this and will pursue this path to enhance our understanding of surface water and terrain characteristics, and help professionals working with surface water find more creative and sustainable ways of planning and designing for the future.