Update README.md
Browse files
README.md
CHANGED
@@ -11,4 +11,62 @@ license: apache-2.0
|
|
11 |
short_description: image segmentation
|
12 |
---
|
13 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
14 |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
11 |
short_description: image segmentation
|
12 |
---
|
13 |
|
14 |
+
|
15 |
+
# Wetlands Segmentation App
|
16 |
+
|
17 |
+
This Hugging Face Space provides an interactive web interface for segmenting wetland areas in satellite imagery using a DeepLabv3+ model.
|
18 |
+
|
19 |
+
## Features
|
20 |
+
|
21 |
+
- Upload satellite imagery in common formats (JPG, PNG) or GeoTIFF format
|
22 |
+
- Optionally upload ground truth masks for evaluation
|
23 |
+
- Visualize wetland segmentation predictions
|
24 |
+
- Calculate metrics (IoU, Precision, Recall, F1) when ground truth is provided
|
25 |
+
- View wetland coverage percentage statistics
|
26 |
+
|
27 |
+
## Usage Instructions
|
28 |
+
|
29 |
+
1. **Upload Input Image**:
|
30 |
+
- Use the "Upload Image" tab for common image formats (JPG, PNG, etc.)
|
31 |
+
- Use the "Upload TIFF" tab for GeoTIFF files with multiple bands
|
32 |
+
|
33 |
+
2. **Upload Ground Truth (Optional)**:
|
34 |
+
- If you have a ground truth mask, upload it to see evaluation metrics
|
35 |
+
- The ground truth should be a binary mask where white (255) represents wetlands
|
36 |
+
|
37 |
+
3. **Analyze**:
|
38 |
+
- Click the "Analyze Image" button to process the image
|
39 |
+
- View the segmentation results and statistics
|
40 |
+
|
41 |
+
## Model Information
|
42 |
+
|
43 |
+
This app uses a model from the [dcrey7/wetlands_segmentation_deeplabsv3plus](https://huggingface.co/dcrey7/wetlands_segmentation_deeplabsv3plus) repository.
|
44 |
+
|
45 |
+
**Model Architecture:**
|
46 |
+
- DeepLabv3+ with ResNet-50 backbone
|
47 |
+
- Input: RGB satellite imagery (optimal size: 128×128 pixels)
|
48 |
+
- Output: Binary segmentation mask (Wetland vs Background)
|
49 |
+
|
50 |
+
The model was trained on a dataset of satellite imagery containing wetland regions, focusing on environmental monitoring and conservation planning applications.
|
51 |
+
|
52 |
+
## Example Output
|
53 |
+
|
54 |
+
When you upload an image, the app will display:
|
55 |
+
- The original input image
|
56 |
+
- The predicted wetland segmentation mask
|
57 |
+
- Ground truth mask (if provided)
|
58 |
+
- Statistics including wetland coverage percentage
|
59 |
+
- Evaluation metrics (if ground truth is provided)
|
60 |
+
|
61 |
+
## Limitations
|
62 |
+
|
63 |
+
- The model works best on imagery similar to its training data
|
64 |
+
- Performance may vary depending on image quality and characteristics
|
65 |
+
- The model is designed for 128×128 pixel inputs (images will be resized)
|
66 |
+
- While the model can process images with any number of bands, it was trained on RGB data
|
67 |
+
|
68 |
+
## License
|
69 |
+
|
70 |
+
This application and the underlying model are available under the Apache 2.0 license.
|
71 |
+
|
72 |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|