用AI融合激光雷达、雷达和光学影像，提升城市地图精度
Geospatial Data Fusion: Combining Lidar, SAR, and Optical Imagery with AI for Enhanced Urban Mapping
- 用全卷积网络+粒子群优化融合多源遥感数据
- 像素准确率92.3%，交并比达87.6%，优于单一传感器
- 适合城市规划、智慧基建等需要高精度地图的场景
本研究探索通过先进人工智能技术融合激光雷达（Lidar）、合成孔径雷达（SAR）和光学影像，以增强城市制图效果。针对单一传感器数据的局限性，该方法利用全卷积网络（FCNs）进行城市特征提取，实现建筑物、道路和植被等要素的像素级分类。为优化模型性能，采用粒子群优化（PSO）进行超参数调优。结果表明，FCN-PSO模型达到92.3%的像素准确率和87.6%的平均交并比（IoU），显著优于传统单源方法。研究验证了多源地理空间数据融合与AI驱动方法在城市制图中的潜力，为城市规划与管理提供重要支持，并推动实时制图与自适应基础设施规划的发展。
原文摘要 · Abstract (English)
This study explores the integration of Lidar, Synthetic Aperture Radar (SAR), and optical imagery through advanced artificial intelligence techniques for enhanced urban mapping. By fusing these diverse geospatial datasets, we aim to overcome the limitations associated with single-sensor data, achieving a more comprehensive representation of urban environments. The research employs Fully Convolutional Networks (FCNs) as the primary deep learning model for urban feature extraction, enabling precise pixel-wise classification of essential urban elements, including buildings, roads, and vegetation. To optimize the performance of the FCN model, we utilize Particle Swarm Optimization (PSO) for hyperparameter tuning, significantly enhancing model accuracy. Key findings indicate that the FCN-PSO model achieved a pixel accuracy of 92.3% and a mean Intersection over Union (IoU) of 87.6%, surpassing traditional single-sensor approaches. These results underscore the potential of fused geospatial data and AI-driven methodologies in urban mapping, providing valuable insights for urban planning and management. The implications of this research pave the way for future developments in real-time mapping and adaptive urban infrastructure planning.
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