| Title: | High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learning |
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| Authors: | ID Žalik, Mitja (Author)
ID Mongus, Domen (Author)
ID Lukač, Niko (Author) |
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| Files: |  1-s2.0-S0960148123017834-main.pdf (6,42 MB)
MD5: 2E2C0E357518CBFD717B5166DB2060DB
 https://www.sciencedirect.com/science/article/pii/S0960148123017834?via%3Dihub
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Keywords: | deep learning, fully convolutional neural network, LiDAR data, digital elevation model, solar energy, solar potential |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 07.12.2023 |
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| Article acceptance date: | 20.12.2023 |
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| Publication date: | 23.12.2023 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2024 |
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| Number of pages: | 15 str. |
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| Numbering: | Vol. 222, [article no.] 119868 |
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| PID: | 20.500.12556/DKUM-86891  |
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| UDC: | 004.8 |
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| ISSN on article: | 1879-0682 |
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| COBISS.SI-ID: | 179585539 |
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| DOI: | 10.1016/j.renene.2023.119868 |
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| Copyright: | © 2024 The Authors. Published by Elsevier Ltd. |
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| Publication date in DKUM: | 26.01.2024 |
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| Views: | 398 |
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| Downloads: | 136 |
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| Metadata: |    |
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| Categories: | Misc.
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