---
license: apache-2.0
language:
- en
tags:
- Pytorch
- mmsegmentation
- segmentation
- Flood mapping
- Sentinel-2
- Geospatial
- Foundation model
metrics:
- accuracy
- IoU
---
### Model and Inputs
The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/README.md) model is finetuned to segment the extend of floods on Sentinel-2 images from the [Sen1Floods11 dataset](https://github.com/cloudtostreet/Sen1Floods11). 

The dataset consists of 446 labeled 512x512 chips that span all 14 biomes, 357 ecoregions, and 6 continents of the world across 11 flood events. The benchmark associated to Sen1Floods11 provides results for fully convolutional neural networks trained in various input/labeled data setups, considering Sentinel-1 and Sentinel-2 imagery. 

We extract the following bands for flood mapping:
 
1. Blue
2. Green
3. Red
4. Narrow NIR
5. SWIR 1
6. SWIR 2

Labels represent no water (class 0), water/flood (class 1), and no data/clouds (class 2).

The Prithvi-100m model was initially pretrained using a sequence length of 3 timesteps. Based on the characteristics of this benchmark dataset, we focus on single-timestamp segmentation. This demonstrates that our model can be utilized with an arbitrary number of timestamps during finetuning.

![](sen1floods11-finetuning.png)

### Code

The code for this finetuning is available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/)

The configuration used for finetuning is available through this [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/fine-tuning-examples/configs/sen1floods11.py).

### Results

Finetuning the geospatial foundation model for 100 epochs leads to the following performance on out-of-sample test data:

|     **Classes**    | **IoU**| **Acc**|
|:------------------:|:------:|:------:|
|      No water      | 96.90% | 98.11% |
|     Water/Flood    | 80.46% | 90.54% |

|     **aAcc**       |**mIoU**|**mAcc**|
|:------------------:|:------:|:------:|
|       97.25%       | 88.68% | 94.37% |


The performance of the model has been further validated on an unseen, holdout flood event in Bolivia. The results are consistent with the performance on the test set:


|     **Classes**    | **IoU**| **Acc**|
|:------------------:|:------:|:------:|
|      No water      | 95.37% | 97.39% |
|     Water/Flood    | 77.95% | 88.74% |

|     **aAcc**       |**mIoU**|**mAcc**|
|:------------------:|:------:|:------:|
|       96.02%       | 86.66% | 93.07% |

Finetuning took ~1 hour on an NVIDIA V100.


### Inference
The github repo includes an inference script that allows running the flood mapping model for inference on Sentinel-2 images. These inputs have to be geotiff format, including 6 bands for a single time-step described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-sen1floods11-demo)**.