Current
Climate change and machine vision www-service demo.
Invasive species mapping in Estonia 2019
1. The purpose is to identify and evaluate the potential for machine learning and artificial intelligence in water resource management and water safety.
2. Machine learning and cloud services are being developed into new future services:
- in hydrology (the state of the bed, the ice situation)
- flood risk management & flood preparedness (water level)
- environmental monitoring (aquatic vegetation, alien species) and
- biomass assessments in water bodies.
Machine learning and ice movement.
Classifying ice movement from images.
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Drone a
nd machine vision services.
Biomassa evaluation with image recgonition (waterlily) .
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IoT water temperature station
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3. Deep learning in water level prediction - experiment.
4. Internet of Things (IoT) for monitoring surface and groundwater.
The aim is to evaluate the suitability of the new IoT technology for the various monitoring tasks in order to significantly reduce the cost and energy consumption of the device and the cost of data transmission and storage without compromising quality. The project will build, test and evaluate the functionality of IoT services at surface and groundwater stations. During the project, the stations will be connected to the IoT network and the functionality of the measurement device, data transmission networks and cloud information services will be evaluated and coordinated with existing monitoring systems.
Rakkolanjoki camera and neural network.
Accuracy of water level reading with image recgontion.
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Multispectral image for mapping invasives. 
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Fixed wind drone mapping invasives 15.8.2019.
Drone water quality map (multispectral image - turbidity + chl-a) autumn 2019. .
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AlyVesi -project using 5G and machine learning in real time
(news, August 30, 2019)
Eagle Eyes -video
More information
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Jari Silander
, Senior Researcher, Finnish Environment Institute SYKE, firstname.surname@ymparisto.fi