A Multi-Level Semi-Automatic Procedure for the Monitoring of Bridges in Road Infrastructure Using MT-DInSAR Data
Abstract
1. Introduction
2. Proposed Methodology
2.1. Workflow
- A satellite data dataset in the AOI.The monitoring process relies on Multi-Temporal Differential Interferometric Synthetic Aperture Radar (MT-DInSAR) analysis. Different satellite data can be adopted. Among the most commonly used datasets, the data from the Sentinel constellation processed and made available by the European Ground Motion Service (EGMS) are well-suited for broad-scale applications, while, for example, data from the first- and second-generation COSMO-SkyMed (CSK) constellations are more effective when applied to smaller, localized areas.
- A georeferenced database of bridges in the AOI.The proposed methodology requires the availability of polygons representing bridges in order to enable spatial queries and the filtering of measurement points (also named as PSs, Persistent Scatterers). However, existing datasets often only provide point geometries indicating bridge locations (latitude and longitude coordinates) within Geographic Information System (GIS) environments, thus requiring additional processing to determine bridge lengths and, subsequently, potential widths.
2.2. Key Datasets
2.2.1. Satellite Data
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- Basic (Level 2a): This provides displacement measurements along the satellite Line of Sight (LOS) for single data tiles with respect to a local virtual reference point located within each data tile. This means that every data tile has its own reference point, making the velocities and displacement measures relative. It contains geo-localization information (both in plan and in elevation), temporal coherence, displacement velocity (both mean and standard deviation), acceleration (both mean and standard deviation) and seasonal component (both mean and standard deviation), together with the full time series of displacement for each PS. The full resolution (i.e., about 5 × 20 m) of the Sentinel sensor is exploited. The product is delivered as separate datasets for the different tracks and for both ascending and descending orbit geometries.
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- Calibrated (Level 2b): This provides data corrected using a GNSS-based deformation model, thereby making all the displacement measures absolute (referenced to an Earth-centered datum). This product contains the same information as the Basic one and exploits the full resolution of the Sentinel sensor of about 5 × 20 m. In this case the product is delivered as separate datasets for ascending and descending orbit geometries.
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- Ortho (Level 3): Starting from ascending and descending L2b (i.e., calibrated) products, these data are combined to solve for mean vertical and horizontal east–west velocities at each location. To achieve this result, the ascending and descending L2b datasets are down-sampled to a regular 100 m × 100 m grid. The resulting L3 dataset provides the time series and velocities of ground motion in standard geographic directions, which can be easier to interpret for large-scale ground stability assessment. Ortho products are anchored to the GNSS reference frame (i.e., the measures are absolute) and are organized in 100 km × 100 km tiles for dissemination. Unfortunately, due to the poor spatial resolution, this product cannot be used generally for structural health monitoring, even at territorial scale.
2.2.2. Bridges Dataset
2.3. Spatial Join and Deformation Analysis
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- The number and density of PS points;
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- The mean coherence of the PS points;
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- The mean and standard deviation of the LOS velocities;
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- The mean time series of displacement.
2.4. Alert System and Upscaling
3. Case Study: Definition of AOI and Data Collection
4. Results
4.1. Spatial Join and Deformation Analysis
4.2. Alert System and Upscaling
- A mean vertical velocity higher than a threshold of 4 mm/yr;
- A mean horizontal velocity higher than a threshold of 4 mm/yr;
- An ascending mean velocity along the LOS higher than a threshold of 4 mm/yr;
- A descending mean velocity along the LOS higher than a threshold of 4 mm/yr;
- Variation with respect to the previous time window larger than 100% for each of the above mentioned data points.
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Number of ASC, DES Tracks | Number of Possible Combinations | Number of Pixels |
---|---|---|
0, 0 | 0 | 342 (11.4%) |
1, 0 or 0, 1 | 0 | 455 (15.2%) |
2, 0 or 0,2 | 0 | 333 (11.1%) |
1, 1 | 1 | 493 (16.4%) |
1, 2 or 2, 1 | 2 | 1245 (41.5%) |
2, 2 | 4 | 131 (4.4%) |
Track and Orbit Combination | Number of Pixels |
---|---|
015 (A) | 59 |
117 (A) | 40 |
095 (D) | 21 |
168 (D) | 80 |
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Talledo, D.A.; Saetta, A. A Multi-Level Semi-Automatic Procedure for the Monitoring of Bridges in Road Infrastructure Using MT-DInSAR Data. Remote Sens. 2025, 17, 2377. https://doi.org/10.3390/rs17142377
Talledo DA, Saetta A. A Multi-Level Semi-Automatic Procedure for the Monitoring of Bridges in Road Infrastructure Using MT-DInSAR Data. Remote Sensing. 2025; 17(14):2377. https://doi.org/10.3390/rs17142377
Chicago/Turabian StyleTalledo, Diego Alejandro, and Anna Saetta. 2025. "A Multi-Level Semi-Automatic Procedure for the Monitoring of Bridges in Road Infrastructure Using MT-DInSAR Data" Remote Sensing 17, no. 14: 2377. https://doi.org/10.3390/rs17142377
APA StyleTalledo, D. A., & Saetta, A. (2025). A Multi-Level Semi-Automatic Procedure for the Monitoring of Bridges in Road Infrastructure Using MT-DInSAR Data. Remote Sensing, 17(14), 2377. https://doi.org/10.3390/rs17142377