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

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.

Chimney Detection Based on Faster R-CNN and Spatial Analysis Methods in High Resolution Remote Sensing Images

“…The Faster R-CNN is chosen for preliminary detection for its high accuracy in chimney detection compared with other methods [ 21 ]. As mentioned before, the Faster R-CNN contains two steps [ 10 ].…”

“…Yao et al [ 20 ] used the Faster R-CNN to detect the chimney and condensing tower. Zhang et al [ 21 ] established the BUAA-FFPP60 dataset, which can be used not only to detect the targets, but also to confirm their working status. Comparison among different deep learning algorithms [ 6 , 10 , 22 , 23 , 24 , 25 , 26 , 27 ] is also made based on performance indicators, such as accuracy, model memory size, and running time, and results show that no single algorithm performs well in all aspects.…”

“…In this paper, we use BUAA-FFPP60 dataset [ 21 ] and Faster R-CNN algorithm to train the preliminarily detection model. Then, two spatial analysis methods, the DTM filtering and main direction test, are introduced to remove the false chimneys.…”

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

“…Artificial intelligence approaches are powerful tools to model and predict parameters of air pollution, including mercury emissions through finding correlations among variables [37,[55][56][57][58]. Among artificial intelligence approaches, machine learning methods are particularly known as powerful algorithms for delivering insight into non-linear relationships among parameters [56,59,60].…”

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

“…Artificial intelligence approaches are powerful tools to model and predict the parameters of air pollution, including mercury emissions through finding correlations between variables [37,[55][56][57][58]. Among the Artificial intelligence approaches, the machine learning methods are particularly known as the powerful algorithms in delivering insight into the nonlinear relationship between parameters [56,59,60].…”