We attempted to employ convolutional neural networks to extract visual features and developed recurrent neural networks for weather property estimation using only image data. Four common weather properties are estimated, i.e., temperature, humidity, visibility, and wind speed. Based on the success of previous works on temperature prediction, we extended them in terms of two aspects. First, by considering the effectiveness of deep multi-task learning, we jointly estimated four weather properties on the basis of the same visual information. Second, we propose that weather property estimations considering temporal evolution can be conducted from two perspectives, i.e., day-wise or hour-wise. A two-dimensional recurrent neural network is thus proposed to unify the two perspectives. In the evaluation, we show that better prediction accuracy can be obtained compared to the state-of-the-art models. We believe that the proposed approach is the first visual weather property estimation model trained based on multi-task learning.

中文翻译：

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基于多任务学习和二维RNN的视觉天气属性预测

我们尝试使用卷积神经网络提取视觉特征，并开发了仅使用图像数据进行天气属性估计的递归神经网络。估计了四个常见的天气属性，即温度，湿度，能见度和风速。基于先前的温度预测工作的成功，我们从两个方面进行了扩展。首先，考虑到深度多任务学习的有效性，我们基于相同的视觉信息共同估算了四个天气属性。其次，我们建议考虑时间演变的天气特性估计可以从两个角度进行，即逐日或逐时。因此，提出了二维递归神经网络来统一这两种观点。在评估中，我们表明，与最新模型相比，可以获得更好的预测准确性。我们认为，提出的方法是基于多任务学习训练的第一个视觉天气属性估计模型。