Causally Aware Generative Adversarial Networks for Light Pollution Control

TL;DR

This paper introduces CAGAN, a causally aware generative adversarial network, to identify and mitigate light pollution in urban areas by uncovering its main drivers and generating targeted pollution maps.

Contribution

The paper presents a novel causally aware GAN framework that integrates causal relationships into light pollution modeling for urban planning.

Findings

Building types significantly influence light pollution levels

CAGAN effectively generates light pollution maps for diverse areas

The approach provides actionable insights for pollution mitigation

Abstract

Artificial light plays an integral role in modern cities, significantly enhancing human productivity and the efficiency of civilization. However, excessive illumination can lead to light pollution, posing non-negligible threats to economic burdens, ecosystems, and human health. Despite its critical importance, the exploration of its causes remains relatively limited within the field of artificial intelligence, leaving an incomplete understanding of the factors contributing to light pollution and sustainable illumination planning distant. To address this gap, we introduce a novel framework named Causally Aware Generative Adversarial Networks (CAGAN). This innovative approach aims to uncover the fundamental drivers of light pollution within cities and offer intelligent solutions for optimal illumination resource allocation in the context of sustainable urban development. We commence by examining light pollution across 33,593 residential areas in seven global metropolises. Our findings reveal substantial influences on light pollution levels from various building types, notably grasslands, commercial centers and residential buildings as significant contributors. These discovered causal relationships are seamlessly integrated into the generative modeling framework, guiding the process of generating light pollution maps for diverse residential areas. Extensive experiments showcase CAGAN's potential to inform and guide the implementation of effective strategies to mitigate light pollution. Our code and data are publicly available at https://github.com/zhangyuuao/Light_Pollution_CAGAN.