Natural images suffer from bad weather conditions, such as haze or fog, which decreases the contrast and degrades the color of observed images. Haze removal aims to recover haze-free images by the image degradation model. The global atmospheric light (airlight) estimation is an essential step for haze removal. With an assumption that the airlight exists in the infinite distance, we propose a novel learning-based framework for airlight estimation. Our framework is mainly composed of two steps: i) the airlight is initially determined by distant region segmentation based on U-Net; ii) the final airlight can be obtained by the weighted sum of the pixel values inside the distant region. Owing to lack of ground-truth airlight, we present a method to synthesize outdoor training examples. The proposed framework not only perform well on synthetic images but also has a good generalization ability for natural images. Experimental results demonstrate that our proposed approach can achieve more accurate estimate of airlight than state-of-the-art methods on both synthetic and natural images.