An Approach to Multiclass Industrial Heat Source Detection Using Optical Remote Sensing Images

Yi Zeng, Ruilin Liao, Caihong Ma (), Dacheng Wang and Yongze Lv
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Yi Zeng: College of Information, Beijing Forestry University, Beijing 100091, China
Ruilin Liao: College of Information, Beijing Forestry University, Beijing 100091, China
Caihong Ma: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Dacheng Wang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yongze Lv: College of Information, Beijing Forestry University, Beijing 100091, China

Energies, 2025, vol. 18, issue 4, 1-26

Abstract: Industrial heat sources (IHSs) are major contributors to energy consumption and environmental pollution, making their accurate detection crucial for supporting industrial restructuring and emission reduction strategies. However, existing models either focus on single-class detection under complex backgrounds or handle multiclass tasks for simple targets, leaving a gap in effective multiclass detection for complex scenarios. To address this, we propose a novel multiclass IHS detection model based on the YOLOv8-FC framework, underpinned by the multiclass IHS training dataset constructed from optical remote sensing images and point-of-interest (POI) data firstly. This dataset incorporates five categories: cement plants, coke plants, coal mining areas, oil and gas refineries, and steel plants. The proposed YOLOv8-FC model integrates the FasterNet backbone and a Coordinate Attention (CA) module, significantly enhancing feature extraction, detection precision, and operational speed. Experimental results demonstrate the model’s robust performance, achieving a precision rate of 92.3% and a recall rate of 95.6% in detecting IHS objects across diverse backgrounds. When applied in the Beijing–Tianjin–Hebei (BTH) region, YOLOv8-FC successfully identified 429 IHS objects, with detailed category-specific results providing valuable insights into industrial distribution. It shows that our proposed multiclass IHS detection model with the novel YOLOv8-FC approach could effectively and simultaneously detect IHS categories under complex backgrounds. The IHS datasets derived from the BTH region can support regional industrial restructuring and optimization schemes.

Keywords: industrial heat source; remote sensing image; multiclass object detection; deep learning; YOLOv8-FC (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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