# Towards Greener Nights: Exploring AI-Driven Solutions for Light Pollution Management

arXiv: https://arxiv.org/abs/2404.09453 · HTML: https://mlsanity.com/paper/2404.09453/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset](https://mlsanity.com/paper/2308.04037/) (2023)

## Abstract (arXiv)

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.