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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yi Zeng & Ruilin Liao & Caihong Ma & Dacheng Wang & Yongze Lv, 2025. "An Approach to Multiclass Industrial Heat Source Detection Using Optical Remote Sensing Images," Energies, MDPI, vol. 18(4), pages 1-26, February.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:865-:d:1589347
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    References listed on IDEAS

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    1. Caihong Ma & Jin Yang & Fu Chen & Yan Ma & Jianbo Liu & Xinpeng Li & Jianbo Duan & Rui Guo, 2018. "Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data," Sustainability, MDPI, vol. 10(12), pages 1-17, November.
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