首页|Findings from Northeast Forestry University Broaden Understanding of Machine Lea rning [Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Dete ction Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brig htness Temperature ...]
Findings from Northeast Forestry University Broaden Understanding of Machine Lea rning [Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Dete ction Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brig htness Temperature ...]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news originating from Harbin, People ’s Republic of China, by NewsRx correspondents, research stated, “Satellite remo te sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooki ng the positional information and brightness temperature of the fire spots in fo rest fires.” Our news journalists obtained a quote from the research from Northeast Forestry University: “This oversight significantly increases the probability of misjudgin g smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire N et (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_ FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiomete r (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshol d for wildfires. Deep learning and machine learning models were trained separate ly using the image data and fire spot area data provided by the dataset. The per formance of the deep learning model was evaluated using metric MAP, while the re gression performance of machine learning was assessed with Root Mean Square Erro r (RMSE) and Mean Absolute Error (MAE). The selected machine learning and deep l earning models were organically integrated.”
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