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Causally Aware Generative Adversarial Networks for Light Pollution Control

Yuyao Zhang, Ke Guo, Xiao Zhou

TL;DR

The paper tackles the problem of understanding and mitigating urban light pollution by revealing causal drivers and generating fine-grained light pollution maps. It introduces Causally Aware Generative Adversarial Networks (CAGAN), a two-stage framework that first estimates causal effects of building-type proxies on light pollution via a Debiased Machine Learning approach, then conditions a CVAE-GAN generator on these causal drivers to produce spatially coherent pollution maps. The authors quantify contributions of different land-use categories across seven metropolises, demonstrating that grasslands, commercial centers, and residential buildings notably influence light pollution. Evaluation shows CAGAN achieves strong performance on pixel- and perceptual-quality metrics and yields maps that accurately reflect the underlying causal structure, enabling targeted illumination policy and resource allocation. The work advances interpretable, causality-informed urban sensing and has practical implications for sustainable urban development and light-management strategies.

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.

Causally Aware Generative Adversarial Networks for Light Pollution Control

TL;DR

The paper tackles the problem of understanding and mitigating urban light pollution by revealing causal drivers and generating fine-grained light pollution maps. It introduces Causally Aware Generative Adversarial Networks (CAGAN), a two-stage framework that first estimates causal effects of building-type proxies on light pollution via a Debiased Machine Learning approach, then conditions a CVAE-GAN generator on these causal drivers to produce spatially coherent pollution maps. The authors quantify contributions of different land-use categories across seven metropolises, demonstrating that grasslands, commercial centers, and residential buildings notably influence light pollution. Evaluation shows CAGAN achieves strong performance on pixel- and perceptual-quality metrics and yields maps that accurately reflect the underlying causal structure, enabling targeted illumination policy and resource allocation. The work advances interpretable, causality-informed urban sensing and has practical implications for sustainable urban development and light-management strategies.

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.
Paper Structure (24 sections, 7 equations, 9 figures, 3 tables)

This paper contains 24 sections, 7 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Light Pollution Assessment.
  4. Causal Inference in Urban Computing.
  5. Application of GANs in Urban Computing.
  6. Preliminaries
  7. Data Collection and Processing
  8. Light Pollution Evaluation
  9. Methodology
  10. Causal Inference
  11. Causally Aware Generative Adversarial Networks
  12. Experiments
  13. Experimental Setup
  14. Data Processing.
  15. Causal Inference Experiment Setup.
  16. ...and 9 more sections

Figures (9)

  • Figure 1: The segmentation of POI in a residential area.
  • Figure 2: The results of light pollution assessment in all residential areas across the seven metropolises.
  • Figure 3: The comprehensive architecture of the proposed CAGAN, containing two training stages.
  • Figure 4: Regression from confounders $X$ to treatment $T$ and outcome $Y$ to achieve unconfoundness.
  • Figure 5: Estimation of Average Treatment Effect.
  • ...and 4 more figures