Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study
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 c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/9/1117 |
| _version_ | 1798018589091954688 |
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| author | Haopeng Zhang Qin Deng |
| author_facet | Haopeng Zhang Qin Deng |
| author_sort | Haopeng Zhang |
| collection | DOAJ |
| description | 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<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mn>2</mn> </msup> </semantics> </math> </inline-formula> 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. |
| first_indexed | 2024-04-11T16:25:25Z |
| format | Article |
| id | doaj.art-3331f3ca770144f29319e303741c0af6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T16:25:25Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3331f3ca770144f29319e303741c0af62022-12-22T04:14:10ZengMDPI AGRemote Sensing2072-42922019-05-01119111710.3390/rs11091117rs11091117Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative StudyHaopeng Zhang0Qin Deng1Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, ChinaImage Processing Center, School of Astronautics, Beihang University, Beijing 100191, ChinaThe 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<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mn>2</mn> </msup> </semantics> </math> </inline-formula> 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.https://www.mdpi.com/2072-4292/11/9/1117power plant detectiondeep learningcomparisonremote sensing image |
| spellingShingle | Haopeng Zhang Qin Deng Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study Remote Sensing power plant detection deep learning comparison remote sensing image |
| title | Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study |
| title_full | Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study |
| title_fullStr | Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study |
| title_full_unstemmed | Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study |
| title_short | Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study |
| title_sort | deep learning based fossil fuel power plant monitoring in high resolution remote sensing images a comparative study |
| topic | power plant detection deep learning comparison remote sensing image |
| url | https://www.mdpi.com/2072-4292/11/9/1117 |
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