ESR-GAN: Environmental Signal Reconstruction Learning With Generative Adversarial Network

Monitoring the status of urban environmental phenomenon, which provides fundamental sensory information, is of great significance for various field of urban research. In this article, we propose a new framework, environmental signal reconstruction generative adversarial network, for reconstructing high-quality environmental signal via sensory data from sparsely distributed monitoring sites. Our framework is based on the generative adversarial network (GAN), in which a three-layer convolutional neural network (CNN)-based generative model is proposed to learn an end-to-end mapping between low- and high-quality signals and a discriminative model is introduced for quantizing the reconstruction accuracy. Considering the scattered distribution of sensory data, we further propose a metric called impact map for building loss function and guiding the adversarial training. Experiments with real-world air quality data of Beijing demonstrate that our method outperforms the state-of-the-art data inference techniques in terms of signal recovery accuracy.

Automated summary

The study introduces a new framework for reconstructing urban environmental signals utilizing a generative adversarial network based on sensory data. Experimental results demonstrate its superior performance in signal recovery accuracy.