Geospatial Data Fusion: Combining Lidar, SAR, and Optical Imagery with AI for Enhanced Urban Mapping
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909441264189440 |
|---|---|
| author | Afroosheh, Sajjad Askari, Mohammadreza |
| author_facet | Afroosheh, Sajjad Askari, Mohammadreza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18994 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Geospatial Data Fusion: Combining Lidar, SAR, and Optical Imagery with AI for Enhanced Urban Mapping Afroosheh, Sajjad Askari, Mohammadreza Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing 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. |
| title | Geospatial Data Fusion: Combining Lidar, SAR, and Optical Imagery with AI for Enhanced Urban Mapping |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2412.18994 |