DETECTION OF SOLAR POWER PLANT FIELDS FROM SATELLITE IMAGES
DOI:
https://doi.org/10.24867/27BE11JakovljevicKeywords:
Semantic segmentation, U-net, solar panelsAbstract
This research paper presents an automated system using deep learning, particularly a CNN with a U-Net architecture, to detect and precisely locate solar power plants in satellite images. Achieving an 89% IoU overlap, the system streamlines renewable energy monitoring, promoting sustainability.
References
[1] O. Ronneberger, P. Fischer i T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015.
[2] U. Ramer, An iterative procedure for the polygonal approximation of plane curves, Computer Graphics and Image Processing, 1972.
[3] D. Douglas i P. Peucker, Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature, Cartographica: The International Journal for Geographic Information and Geovisualization, 1973.
[2] U. Ramer, An iterative procedure for the polygonal approximation of plane curves, Computer Graphics and Image Processing, 1972.
[3] D. Douglas i P. Peucker, Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature, Cartographica: The International Journal for Geographic Information and Geovisualization, 1973.
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Published
2024-06-05
Issue
Section
Electrotechnical and Computer Engineering
