Update README.md
Browse files
README.md
CHANGED
@@ -1,3 +1,56 @@
|
|
1 |
---
|
2 |
license: apache-2.0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
---
|
2 |
license: apache-2.0
|
3 |
+
language:
|
4 |
+
- en
|
5 |
+
tags:
|
6 |
+
- Pytorch
|
7 |
+
- mmsegmentation
|
8 |
+
- segmentation
|
9 |
+
- Flood mapping
|
10 |
+
- Sentinel-2
|
11 |
+
- Geospatial
|
12 |
+
- Foundation model
|
13 |
+
metrics:
|
14 |
+
- accuracy
|
15 |
+
- IoU
|
16 |
---
|
17 |
+
### Model and Inputs
|
18 |
+
The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/README.md) parameter model is finetuned to segment the extend of floods on Sentinel-2 images from the [Sen1Floods11 dataset](https://github.com/cloudtostreet/Sen1Floods11).
|
19 |
+
|
20 |
+
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.
|
21 |
+
|
22 |
+
We extract the following bands for flood mapping:
|
23 |
+
|
24 |
+
1. Blue
|
25 |
+
2. Green
|
26 |
+
3. Red
|
27 |
+
4. Narrow NIR
|
28 |
+
5. SWIR 1
|
29 |
+
6. SWIR 2
|
30 |
+
|
31 |
+
Labels represent no water (class 0), water/flood (class 1), and no data/clouds (class 2).
|
32 |
+
|
33 |
+
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 here. This demonstrates that our model does not require multiple timestamps during finetuning.
|
34 |
+
|
35 |
+
|
36 |
+
|
37 |
+
### Code
|
38 |
+
Code for Finetuning is available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/)
|
39 |
+
|
40 |
+
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).
|
41 |
+
|
42 |
+
### Results
|
43 |
+
The experiment by running the mmseg stack for 80 epochs using the above config led to the following result:
|
44 |
+
|
45 |
+
| **Classes** | **IoU**| **Acc**|
|
46 |
+
|:------------------:|:------:|:------:|
|
47 |
+
| No water | 96.90% | 98.11% |
|
48 |
+
| Water/Flood | 80.46% | 90.54% |
|
49 |
+
|
50 |
+
|**aAcc**|**mIoU**|**mAcc**|
|
51 |
+
|:------:|:------:|:------:|
|
52 |
+
| 97.25% | 88.68% | 94.37% |
|
53 |
+
|
54 |
+
|
55 |
+
### Inference
|
56 |
+
The github repo includes an inference script that allows to run the flood mapping model for inference on Sentinel-2 images. These input 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)**.
|