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Deep learning model to classify flood inundated areas in multispectral satellite imagery. A brief summary of the item is not available. Add a brief summary about the item.

‎Deep learning package by

Item created: Jan 3, 2024 Item updated: Jan 2, 2025 Number of downloads: 3,112

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Description

For flood monitoring and management, including disaster response in the aftermath of a flood, identifying submerged areas is a challenge for emergency responders and policymakers. The increase in accessibility to earth observation data and the continuous evolution of deep learning methods is enabling an efficient way to automate flood monitoring and management. Flood segmentation deep learning models emerge as an important tool in helping with the task of precisely identifying and delineating flood-affected regions from satellite imagery. 

The Prithvi-100M-sen1floods11  has been developed by NASA and IBM by finetuning their foundation model for earth observation - Prithvi-100m, using Sen1Floods11 dataset. Use this model to automate the process of segmenting flood extents in multispectral satellite imagery.

Using the model

Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.

Fine-tuning the model
This model can be fine-tuned using the ArcGIS API for Python. Follow the guide to fine-tune this model.

Input

Raster, mosaic dataset, or image service. These must be a 6-band composite raster derived from either  Harmonized Landsat 8 (HLSL30) or Harmonized Sentinel 2 (HLSS30). The model can also be used with level-2 products of Sentinel-2 and Landsat-8, yet it performs most effectively with HLSL30 and HLSS30.

The composite raster should contain the following 6 bands: Blue, Green, Red, Narrow NIR, SWIR, SWIR 2.

Band numbers for the above mentioned bands are: 

  • For HLSS30 and Sentinel-2: Band2, Band3, Band4, Band8A, Band11, Band12 
  • For HLSL30 and Landsat 8: Band2, Band3, Band4, Band5, Band6, Band7


Output

Classified raster with 3 classes (no water, water/flood, and no data/clouds).

Applicable geographies

This model is expected to work well across the globe.

Model architecture

This model packages IBM and NASA's Prithvi-100M-sen1floods11 model and uses a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder (MAE) learning strategy.

Accuracy metrics

This model has a mean intersection over union of 0.88 percent and mean accuracy of 94.37 percent.

Training data
This model finetunes the pretrained Prithvi-100m model to segment the extent of flooded area on multispectral satellite images from the  Sen1Floods11 dataset.

Sample results
Here are a few results from the model.





Citations
Bonafilia, D., Tellman, B., Anderson, T., Issenberg, E. 2020. Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 210-211.

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https://downloads.esri.com/blogs/arcgisonline/esrilogo_new.png This work is licensed under the Esri Master License Agreement.

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          IBM, NASA

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