Leveraging convolutional neural networks for semantic segmentation of global floods with PlanetScope imagery

Nicholas R. Leach, Philip Popien, Maxwell C. Goodman, Beth Tellman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Constellations of small satellites are able to achieve the coverage, revisit rate, and spatial resolution needed to generate high resolution global maps of flood waters. The PlanetScope constellation achieves daily coverage at 3-4 m resolution; however, the limited spectral channels available as well as radiometric challenges inherent to large constellations of small satellites create a challenge to accurate global flood mapping. Here, we present a convolutional neural network (CNN) which combines PlanetScope imagery with information about multidecadal water dynamics from the Global Surface Water dataset to create a globally applicable water segmentation model. We also compare this model to locally-trained Random Forest models, which are commonly used for flood mapping. The CNN achieves a mean intersection over union score of 58.1% ± 9.84% across 7 biomes when evaluated on flooded areas outside of permanent water bodies, and 70.3% ± 11.6% when evaluated on all water pixels.

Original languageEnglish (US)
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages314-317
Number of pages4
ISBN (Electronic)9781665427920
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Duration: Jul 17 2022Jul 22 2022

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2022-July

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period7/17/227/22/22

Keywords

  • Random Forest
  • Sentinel-1
  • convolutional neural network
  • flood mapping
  • image segmentation
  • remote sensing

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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