text: In this paper, we attempt to employ convolutional recurrent neural networks for weather temperature estimation using only image data. We study ambient temperature estimation based on deep neural networks in two scenarios a) estimating temperature of a single outdoor image, and b) predicting temperature of the last image in an image sequence. In the first scenario, visual features are extracted by a convolutional neural network trained on a large-scale image dataset. We demonstrate that promising performance can be obtained, and analyze how volume of training data influences performance. In the second scenario, we consider the temporal evolution of visual appearance, and construct a recurrent neural network to predict the temperature of the last image in a given image sequence. We obtain better prediction accuracy compared to the state-of-the-art models. Further, we investigate how performance varies when information is extracted from different scene regions, and when images are captured in different daytime hours. Our approach further reinforces the idea of using only visual information for cost efficient weather prediction in the future.
  @cite_0: Recent descriptions of algorithms applied to images archived from webcams tend to underplay the challenges in working with large data sets acquired from uncontrolled webcams in real environments. In building a database of images captured from 1000 webcams, every 30 minutes for the last 3 years, we observe that these cameras have a wide variety of failure modes. This paper details steps we have taken to make this dataset more easily useful to the research community, including (a) tools for finding stable temporal segments, and stabilizing images when the camera is nearly stable, (b) visualization tools to quickly summarize a years worth of image data from one camera and to give a set of exemplars that highlight anomalies within the scene, and (c) integration with LabelMe, allowing labels of static features in one image of a scene to propagate to the thousands of other images of that scene. We also present proof-of-concept algorithms showing how this data conditioning supports several problems in inferring properties of the scene from image data.
  @cite_11: This paper details an empirical study of large image sets taken by static cameras. These images have consistent correlations over the entire image and over time scales of days to months. Simple second-order statistics of such image sets show vastly more structure than exists in generic natural images or video from moving cameras. Using a slight variant to PCA, we can decompose all cameras into comparable components and annotate images with respect to surface orientation, weather, and seasonal change. Experiments are based on a data set from 538 cameras across the United States which have collected more than 17 million images over the the last 6 months.
summary: In addition to weather condition classification, more weather properties have also been investigated. Jacobs and his colleagues @cite_11 initiated a project for collecting outdoor scene images captured by static webcams over a long period of time. The collected images form the Archive of Many Outdoor Scene (AMOS) dataset @cite_11 . Based on the AMOS dataset, they proposed that webcams installed across the earth can be viewed as image sensors and can enable us to understand weather patterns and variations over time @cite_0 . More specifically, they adopted principal component analysis and canonical correlation analysis to predict wind velocity and vapor pressure from a sequence of images.