Chimney detection and size estimation from high-resolution optical satellite imagery using deep learning models

Abstract

This study shows an efficient method to estimate the location and size of chimneys from high-resolution satellite optical images using deep learning models. Korean multi-purpose satellite (KOMPSAT) −3 and -3A satellite images with spatial resolutions of 0.7 m and 0.55 m were used for model performance estimation, and the You Only Look Once version 8 (YOLOv8) and Residual Network (ResNet) regression models were integrated for the detection and size estimation of the chimneys. In the chimney detection and size estimation, we compared the model performances between 1) imbalanced and balanced data, 2) South Korea and Thailand data, and 3) KOMPSAT-3 and -3A data. We also analyzed the model performance according to the ResNet convolutional layers in chimney size estimation. In chimney detection, the model performances between the imbalanced and balanced data, South Korea and Thailand data, and KOMPSAT-3 and -3A data were about 0.723 and 0.739, 0.674 and 0.805, and 0.702 and 0.786 in the average precision (AP) 50–95 measure, respectively. The model performance between the South Korea and Thailand data showed a significant difference, likely because the chimneys in South Korea are very diverse, making it harder to generalize the YOLOv8 model. Furthermore, the model root mean square errors (RMSE) between the imbalanced and balanced data, South Korea and Thailand data, and KOMPSAT-3 and -3A data were about 2.917 and 2.788, 2.690 and 2.951, and 2.913 and 2.580 in chimney height, respectively, and about 1.285 and 1.190, 1.228 and 1.120, and 1.291 and 1.013 in chimney diameter, respectively. Keywords: Chimneys; deep learning; You Only Look Once version 8; Residual Network; Korean Multi-purpose Satellite-3/3A; object detection; regression model.