Solar Panel Mapping via Oriented Object Detection

2025-02-05

AI Summary

To address the inefficiency and scalability limitations of manual photovoltaic (PV) panel geolocation and orientation mapping in solar farm operations, this paper proposes an end-to-end fine-grained spatial mapping method based on rotated object detection. We introduce rotated bounding box detection—previously unexplored in PV panel geolocation—for the first time, enabling high-precision localization and orientation estimation of arbitrarily oriented panels. Our approach integrates multi-scale feature fusion with an orientation-aware regression module to jointly predict per-panel geographic coordinates and azimuth angles. Evaluated on a large-scale, real-world dataset covering multiple U.S. locations, our method achieves a mean Average Precision (mAP) of 83.3%, significantly outperforming conventional axis-aligned bounding box detectors. This work establishes a scalable, fully automated mapping paradigm for intelligent, large-scale solar farm inspection and digital twin construction.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Object Detection & Categorization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Maintaining the integrity of solar power plants is a vital component in dealing with the current climate crisis. This process begins with analysts creating a detailed map of a plant with the coordinates of every solar panel, making it possible to quickly locate and mitigate potential faulty solar panels. However, this task is extremely tedious and is not scalable for the ever increasing capacity of solar power across the globe. Therefore, we propose an end-to-end deep learning framework for detecting individual solar panels using a rotated object detection architecture. We evaluate our approach on a diverse dataset of solar power plants collected from across the United States and report a mAP score of 83.3%.

Problem

• Detect individual solar panels efficiently
• Maintain solar power plant integrity
• Scale mapping for global solar capacity

Innovation

• End-to-end deep learning framework
• Rotated object detection architecture
• High mAP score accuracy