Assessment of a new GeoAI foundation model for floodinundation mapping
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Abstract
Vision foundation models are a new frontier in GeoAI research because of their potential to enable powerful image analysis by analyzing and extracting important image features from vast amounts of geospatial data. This paper evaluates the performance of the first-of-its-kind geospatial foundation model, IBM-NASA’s Prithvi, to support a crucial geospatial analysis task: flood inundation mapping. This model is compared with popular convolutional neural networks and vision transformer-based architectures regarding mapping accuracy for flooded areas. A benchmark dataset, Sen1Floods11, is used in the experiments, and the models' predictability, generalizability, and transferability are evaluated based on both validation datasets and datasets completely unseen by the model. Results show the impressive transferability of the Prithvi model, highlighting its performance advantages in segmenting flooded areas in previously unseen regions. The findings also suggest areas for improvement for the Prithvi model in adopting multi-scale representation learning, developing more end-to-end pipelines for high-level image analysis tasks, and offering more flexibility in allowable input data bands.
Suggested Citation
Li, W., Lee, H., Wang, S., Hsu, C., Arundel, S., 2023, Assessment of a new GeoAI foundation model for floodinundation mapping, in Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI '23), Hamburg Germany, November 13, 2023, p. 102-109, https://doi.org/10.1145/3615886.3627747.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Assessment of a new GeoAI foundation model for floodinundation mapping |
| DOI | 10.1145/3615886.3627747 |
| Publication Date | November 20, 2023 |
| Year Published | 2023 |
| Language | English |
| Publisher | Association for Computing Machinery |
| Contributing office(s) | Center for Geospatial Information Science (CEGIS) |
| Description | 8 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Conference publication |
| Larger Work Title | Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI '23) |
| First page | 102 |
| Last page | 109 |
| Conference Title | 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI '23) |
| Conference Location | Hamburg Germany |
| Conference Date | November 13, 2023 |