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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.02868 |
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| _version_ | 1866913341736222720 |
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| author | Vatsal, Vishesh Nandi, Gouranga Manilal, Primo |
| author_facet | Vatsal, Vishesh Nandi, Gouranga Manilal, Primo |
| contents | Continuous monitoring for road flooding could be achieved through onboard computing of satellite imagery to generate near real-time insights made available to generate dynamic information for maps used for navigation. Given the existing computing hardware like the one considered for the PhiSat-2 mission, the paper describes the feasibility of running the road flooding detection. The simulated onboard imagery dataset development and its annotation process for the OrbitalAI Φsat-2 challenge is described. The flooding events in the city of Bengaluru, India were considered for this challenge. This is followed by the model architecture selection, training, optimization and accuracy results for the model. The results indicate that it is possible to build low size, high accuracy models for the road flooding use case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Continuous Monitoring for Road Flooding With Satellite Onboard Computing For Navigation for OrbitalAI Φsat-2 challenge Vatsal, Vishesh Nandi, Gouranga Manilal, Primo Robotics Continuous monitoring for road flooding could be achieved through onboard computing of satellite imagery to generate near real-time insights made available to generate dynamic information for maps used for navigation. Given the existing computing hardware like the one considered for the PhiSat-2 mission, the paper describes the feasibility of running the road flooding detection. The simulated onboard imagery dataset development and its annotation process for the OrbitalAI Φsat-2 challenge is described. The flooding events in the city of Bengaluru, India were considered for this challenge. This is followed by the model architecture selection, training, optimization and accuracy results for the model. The results indicate that it is possible to build low size, high accuracy models for the road flooding use case. |
| title | Continuous Monitoring for Road Flooding With Satellite Onboard Computing For Navigation for OrbitalAI Φsat-2 challenge |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.02868 |