Remote Sensing and Deep Learning to Understand Noisy OpenStreetMap †
Abstract
:1. Introduction
- Building footprints are regularly labeled but are up to 9 m off-center on the image plane [10]. The reason for this shift is that images used to digitize the footprint are different from the images used for further analysis. The misalignment issue is referred as registration noise, as shown in Figure 1.
- Missing objects in the annotation dataset: occurs because they have not been noticed by the volunteers, or may have been constructed in the time between the data acquisition. This ambiguity in the labels is known as omission noise (see Figure 2).
- Objects may also remain in annotation when they do not exist at present, like buildings destroyed in natural disasters.
2. Background: Reducing OSM Noise Using Remote Sensing or Deep Learning
3. Proposed Methodology
3.1. Pre-Processing
3.2. Deep Learning Approach
4. Experimental Analysis
4.1. Dataset
4.2. Experimental Setup
5. Results and Comparison
5.1. Missing Building Case
5.2. Change Detection Case
- The registration noise is handled by the CCR approach in Section 3, and Mask R-CNN achieves better performance with a quality improved dataset in building extraction than using noisy OSM with VHR images.
- The omission noise is mitigated and described in Section 5.1, and the results promote the use of VHR-updated imagery to highlight the missing objects in OSM.
- The updated mapping issue is described in Section 5.2, which leads to the change detection analysis of an area. We perform a case study on the Beirut area for change detection mapping, where an explosion occurred on 4 August 2020. The results show that this approach mitigated the problem of updated mapping and proposed a new aspect of change detection and OSM quality.
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Ohsome Quality Analyst
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Mask R-CNN (Proposed) | Precision | Recall | F1 Score | mAP | OA |
---|---|---|---|---|---|
(a) With Noisy OSM | 0.76 | 0.77 | 0.76 | 0.60 | 0.62 |
(b) With Processed OSM | 0.96 | 0.96 | 0.96 | 0.76 | 0.93 |
FCN Approach (Comparison) | 0.85 | 0.77 | 0.83 | 0.80 | 0.87 |
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Usmani, M.; Bovolo, F.; Napolitano, M. Remote Sensing and Deep Learning to Understand Noisy OpenStreetMap. Remote Sens. 2023, 15, 4639. https://doi.org/10.3390/rs15184639
Usmani M, Bovolo F, Napolitano M. Remote Sensing and Deep Learning to Understand Noisy OpenStreetMap. Remote Sensing. 2023; 15(18):4639. https://doi.org/10.3390/rs15184639
Chicago/Turabian StyleUsmani, Munazza, Francesca Bovolo, and Maurizio Napolitano. 2023. "Remote Sensing and Deep Learning to Understand Noisy OpenStreetMap" Remote Sensing 15, no. 18: 4639. https://doi.org/10.3390/rs15184639