Geospatial Data Fusion: Combining Lidar, SAR, and Optical Imagery with AI for Enhanced Urban Mapping

TL;DR

This paper presents a novel AI-based method that fuses Lidar, SAR, and optical imagery to improve urban mapping accuracy, outperforming traditional single-sensor approaches in pixel classification tasks.

Contribution

It introduces a deep learning framework using FCNs optimized with PSO for multi-sensor geospatial data fusion in urban mapping.

Findings

Achieved 92.3% pixel accuracy in urban feature classification

Obtained 87.6% mean IoU surpassing traditional methods

Demonstrated the effectiveness of multi-sensor data fusion with AI

Abstract

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.