TY  - JOUR
JF  - ISPRS International Journal of Geo-Information
A1  - Ibrahim, MR
A1  - Haworth, J
A1  - Cheng, T
KW  - Computer vision; deep learning; convolutional neural networks (CNN); weather condition; visual conditions
N2  - 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.
ID  - discovery10087294
UR  - https://doi.org/10.3390/ijgi8120549
IS  - 12
N1  - This work is licensed under a Creative Commons Attribution 4.0 International License. The images
or other third party material in this article are included in the Creative Commons license,
unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material. To view a copy of this
license, visit http://creativecommons.org/licenses/by/4.0/
TI  - Weathernet: Recognising weather and visual conditions from street-level images using deep residual learning
AV  - public
Y1  - 2019/11/30/
VL  - 8
ER  -