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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2404.09453 |
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| _version_ | 1866916205623771136 |
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| author | Varshney, Paras Desai, Niral Ahmed, Uzair |
| author_facet | Varshney, Paras Desai, Niral Ahmed, Uzair |
| contents | This research endeavors to address the pervasive issue of light pollution through an interdisciplinary approach, leveraging data science and machine learning techniques. By analyzing extensive datasets and research findings, we aim to develop predictive models capable of estimating the degree of sky glow observed in various locations and times. Our research seeks to inform evidence-based interventions and promote responsible outdoor lighting practices to mitigate the adverse impacts of light pollution on ecosystems, energy consumption, and human well-being. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Greener Nights: Exploring AI-Driven Solutions for Light Pollution Management Varshney, Paras Desai, Niral Ahmed, Uzair Machine Learning This research endeavors to address the pervasive issue of light pollution through an interdisciplinary approach, leveraging data science and machine learning techniques. By analyzing extensive datasets and research findings, we aim to develop predictive models capable of estimating the degree of sky glow observed in various locations and times. Our research seeks to inform evidence-based interventions and promote responsible outdoor lighting practices to mitigate the adverse impacts of light pollution on ecosystems, energy consumption, and human well-being. |
| title | Towards Greener Nights: Exploring AI-Driven Solutions for Light Pollution Management |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.09453 |