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WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning

SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineer, University College London (UCL) Gower Street, London WC1E 6BT, UK
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ISPRS Int. J. Geo-Inf. 2019, 8(12), 549; https://doi.org/10.3390/ijgi8120549
Received: 31 October 2019 / Revised: 26 November 2019 / Accepted: 28 November 2019 / Published: 30 November 2019
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it has still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used in practice. What has been achieved to-date are rather sectorial models that address a limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep convolutional neural network (CNN) models, so-called WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as dawn/dusk, day and night for time detection, glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used but are not limited to autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers. View Full-Text
Keywords: computer vision; deep learning; convolutional neural networks (CNN); weather condition; visual conditions computer vision; deep learning; convolutional neural networks (CNN); weather condition; visual conditions
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Ibrahim, M.R.; Haworth, J.; Cheng, T. WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning. ISPRS Int. J. Geo-Inf. 2019, 8, 549.

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