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Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study

1,2,3,*,† and 1,†
1
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
2
Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 100191, China
3
Beijing Key Laboratory of Digital Media, Beijing 100191, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2019, 11(9), 1117; https://doi.org/10.3390/rs11091117
Received: 21 April 2019 / Revised: 29 April 2019 / Accepted: 7 May 2019 / Published: 10 May 2019
(This article belongs to the Special Issue Deep Learning Approaches for Urban Sensing Data Analytics)
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Abstract

The frequent hazy weather with air pollution in North China has aroused wide attention in the past few years. One of the most important pollution resource is the anthropogenic emission by fossil-fuel power plants. To relieve the pollution and assist urban environment monitoring, it is necessary to continuously monitor the working status of power plants. Satellite or airborne remote sensing provides high quality data for such tasks. In this paper, we design a power plant monitoring framework based on deep learning to automatically detect the power plants and determine their working status in high resolution remote sensing images (RSIs). To this end, we collected a dataset named BUAA-FFPP60 containing RSIs of over 60 fossil-fuel power plants in the Beijing-Tianjin-Hebei region in North China, which covers about 123 km 2 of an urban area. We compared eight state-of-the-art deep learning models and comprehensively analyzed their performance on accuracy, speed, and hardware cost. Experimental results illustrate that our deep learning based framework can effectively detect the fossil-fuel power plants and determine their working status with mean average precision up to 0.8273, showing good potential for urban environment monitoring. View Full-Text
Keywords: power plant detection; deep learning; comparison; remote sensing image power plant detection; deep learning; comparison; remote sensing image
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, H.; Deng, Q. Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study. Remote Sens. 2019, 11, 1117.

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