DEEPSKY SYSTEM
Terrestrial Meteorological, Atmospheric and Solar Measurements
Power by Vision AI
THE SYSTEM
Where Atmospheric Physics Meet Image Processing
The DeepSky system focuses on meeting high standards of analysis, quality, and validity. To achieve this, all the theories on Atmospheric Physics are exploited in combination with deep learning and image processing methods in order to obtain the best possible results.
Vision AI System
Novel system based on the technology of commercial cameras, combined with Computer Vision and deep learning techniques for the analysis of digital all-sky images.
Cost Efficiency
Innovation in reducing the cost of terrestrial measurements while maintaining quality and ease of use.
Visible and Infrared Spectrum
Combines visible and infrared images, estimates from propagation models and Vision AI, to provide geophysical variables, both day and night.
GOALS
DeepSky Project Timeline
The main goal of the DeepSky project is to develop an innovative and flexible ground-based measurement system designed to address the needs of end-users in the meteorological, atmospheric and solar energy communities.
1.
Identify potential end-users, requirements, and market analysis
2.
DeepSky prototype development, calibration, and validation
3.
Algorithmic development for meteorological, atmospheric and solar measurements' processing of different geophysical variables
4.
Development of computer vision and deep learning techniques for cloud type identification, cloud coverage estimation and prediction
5.
System performance evaluation in real-world conditions
DATASET
All-sky Visible Dataset
The dataset comprises a large number of more than 7000 sky images that have been annotated and classified to 7 cloud types. Additional multimodal data (like temperature, humidity, irradiance, particle pollution, etc.) and different periods of time (i.e., different months/years) have been considered in the training, resulting in an overall accuracy of more than 90%.
Cumulus
Puffy clouds with clearly defined edges, white or light gray
Altocumulus
Patched clouds of small cloudlets, mosaic-like white
Cirrus
Thin clouds, fibrous, white feathery clouds of ice crystals or sky covering, whitish
Clear Sky
No cloudiness or very few cloudiness, blue
Stratocumulus
Lumpy layer of clouds, broken to almost overcast, causes fog or fine precipitation, white or gray
Cumulonimbus
Dark, thick clouds, mostly overcast, indistinct outline, thundercloud or rain cloud, gray
Mixed
More than two genera of clouds
Geometrical calibration of an all-sky imager: from spherical to cartesian and cylindrical coordinates
Time Lapse video capturing the process of external geometric calibration of the sky camera
VISION AI PLATFORM
Complete Vision AI Infrastructure
DeepSky is integrated with PerCV.ai, Irida Labs’ end-to-end software and services platform for deploying Vision AI at scale.
Our Role
Computer vision and deep learning techniques for the analysis of digital all-sky images (cloud types and cloud coverage, cloud prediction etc.)
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Funding Details
This project has been co-financed by the EU and National funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE, project no. Τ2ΕΔΚ-00681
