UAV Coverage Path Optimization for Dynamic Wildlife Population Monitoring: A Spotted Seal Case Study

The existing predefined coverage path strategies in uncrewed aerial vehicle (UAV)-based vertical photogrammetry are inadequate for the monitoring and surveying of spotted seal populations with dynamic group distribution characteristics. This article presents a novel real time, perception-driven adaptive coverage path planning approach utilizing UAV edge computing for intelligent recognition. The proposed method integrates onboard target detection, multiobject tracking (MOT), and visual positioning during UAV patrol operations to achieve real-time perception of individual locations within mobile populations. To mitigate false detections and redetections, this study proposes a spatial noise filtering (SNF) method for UAV-based detection points. A three-level noise removal scheme is designed, consisting of a distance threshold, low-density point filtering within core regions, and global low-density filtering, enabling systematic suppression of spatial noise generated during detection. On this basis, the SNF method is integrated with a genetic algorithm (GA) to achieve dynamic optimization of the UAV coverage path. Experimental results show that the adopted YOLOv11 model achieves a precision of 0.9. The MOT technique based on UAV video streams reduces redundant positioning by 61% (from 389 detection points to 150 tracking points), and the average visual positioning error is 4.9 m at flight altitudes below 75 m. The proposed SNF method effectively removes most invalid coverage regions, significantly improving the spatial reliability of the detection results. Furthermore, the GA-based optimization of the UAV coverage path enables adaptation to the uncertain spatial distribution of spotted seal populations, meeting the requirements for dynamic population surveying and real-time monitoring.

• Publication year

  2026

• Venue

  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/JSTARS.2026.3657742
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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