GeoPatch 1.3.2

GeoPatch generates patches from remote sensing data and shapefiles for semantic segmentation and object detection with YOLO-format annotations

GeoPatch

GeoPatch is a package for generating patches from remote sensing data

GeoPatch enables users to read, process, and export GeoTIFF patches for training deep learning models. Built on the Rasterio library, it simplifies reading and exporting GeoTIFF patches for semantic segmentation and object detection. The package supports generating patches from raster labels or polygon shapefiles, producing outputs in GeoTIFF or NumPy formats with optional YOLO-format bounding box annotations.

Users can feed satellite imagery and corresponding label data (raster or shapefile) to export patches, with support for data augmentation (vertical flip, horizontal flip, and 90/180/270-degree rotations) for NumPy output. The package ensures complete image coverage, including edge pixels, and supports visualization of patches with optional bounding box overlays.

Any feedback is welcome! Contact me at [e-mail] for contributions or suggestions.

Quick Tutorial on How to Use GeoPatch

1. Installation

pip install GeoPatch

This automatically installs dependencies, including GDAL, numpy, rasterio, geopandas, shapely, tqdm, matplotlib, and scikit-image for handling GeoTIFF files, shapefiles, and image processing.

2. Calling the Package

from GeoPatch import TrainPatch, PredictionPatch

3. Feeding Data

For the image variable, you can pass either a string (file path to .tif or .npy) or a NumPy array. For the label variable, you can pass a string (file path to .tif, .npy, or .shp for shapefiles) or a NumPy array. The package automatically processes and reads the dataset.

# For segmentation or detection with raster labels
patch = TrainPatch(image="xxx/image.tif", label="xxx/label.tif", patch_size=128, stride=64, channel_first=True, shapefile_path=None, label_field=None)

# For segmentation or detection with shapefile labels
patch = TrainPatch(image="xxx/image.tif", label=None, patch_size=128, stride=64, channel_first=True, shapefile_path="xxx/labels.shp", label_field="class_id")

4. Input Data Specifications

Display the shape and size of the input data:

patch.data_dimension()

5. Patch Details

Show the number of original image patches generated based on the given patch size and stride:

patch.patch_info()

6. Saving Image Patches as GeoTIFF Files (Segmentation)

Save image and label patches as GeoTIFF files in the specified folder_name under the current working directory. If only_label=True, only patches with non-zero labels are saved.

patch.generate_segmentation(format="tif", folder_name="seg_tif", only_label=True)

7. Saving Image Patches as NumPy Arrays (Segmentation)

Generate image and label patches in NumPy format with optional data augmentation (vertical flip, horizontal flip, 90/180/270-degree rotations). Augmentations are applied only for format="npy".

patch.generate_segmentation(
    format="npy",
    folder_name="seg_npy",
    only_label=False,
    return_stacked=False,
    save_stack=False,
    V_flip=True,
    H_flip=True,
    Rotation=True
)

# To return stacked NumPy patches:
patch_stacked, label_stacked = patch.generate_segmentation(
    format="npy",
    folder_name="seg_npy",
    only_label=False,
    return_stacked=True,
    save_stack=False,
    V_flip=True,
    H_flip=True,
    Rotation=True
)

8. Saving Patches for Object Detection (YOLO Format)

Generate patches for object detection, producing image patches, optional segmentation masks, and YOLO-format bounding box annotations (.txt files). Augmentations are applied only for format="npy".

patch.generate_detection(
    format="npy",
    folder_name="det_npy",
    only_label=True,
    return_stacked=False,
    save_stack=False,
    V_flip=True,
    H_flip=True,
    Rotation=True,
    segmentation=True
)

9. Saving Patches from Shapefiles (Segmentation and/or Object Detection)

Generate patches for segmentation and/or object detection using a polygon shapefile. The shapefile is reprojected to WGS84 (EPSG:4326) before clipping and rasterization to ensure alignment with the image. Specify the label_field containing integer class IDs (e.g., class_id) during TrainPatch initialization. Outputs include image patches, optional segmentation masks, and YOLO-format bounding box annotations. Augmentations are applied only for format="npy".

patch = TrainPatch(
    image="xxx/image.tif",
    label=None,
    patch_size=128,
    stride=64,
    channel_first=True,
    shapefile_path="xxx/labels.shp",
    label_field="class_id"
)

# For segmentation
patch.generate_segmentation(
    format="npy",
    folder_name="shp_npy",
    only_label=True,
    return_stacked=True,
    save_stack=True,
    V_flip=True,
    H_flip=True,
    Rotation=True
)

# For detection
patch.generate_detection(
    format="npy",
    folder_name="shp_npy",
    only_label=True,
    return_stacked=True,
    save_stack=True,
    V_flip=True,
    H_flip=True,
    Rotation=True,
    segmentation=True
)

10. Patch Visualization

Display patches with their corresponding labels or bounding boxes. Specify the exact folder_name where patches are saved. Use show_bboxes=True to overlay YOLO bounding boxes.

patch.visualize(
    folder_name="shp_npy",
    patches_to_show=2,
    band_num=1,
    fig_size=(10, 20),
    dpi=96,
    show_bboxes=True
)

11. Generating Prediction Patches

Generate patches for prediction using the PredictionPatch class:

prediction = PredictionPatch(image="xxx/test_image.tif", patch_size=128, stride=128, channel_first=True)

12. Saving Prediction Patches

Save prediction patches as GeoTIFF or NumPy arrays. Edge pixels are included to ensure complete image coverage.

# Save as GeoTIFF
prediction.save_Geotif(folder_name="pred_tif")

# Save as NumPy arrays
prediction.save_numpy(folder_name="pred_npy")

Change Log

1.0 (04/07/2022)

• First Release

1.1 (03/08/2022)

• Fixed issues with loading NumPy arrays
• Fixed random visualization of samples

1.1.1 (15/12/2022)

• Fixed visualization issues in Linux environments
• Added PredictionPatch class for generating prediction patches

1.1.1 (22/11/2023)

• Fixed edge pixel issue in prediction patch generation to ensure entire image is patched
• Added GDAL to automatically installed packages

1.2 (27/07/2025)

• Added generate_detection method to TrainPatch for object detection with YOLO-format bounding box annotations
• Modified generate_segmentation and generate_detection to apply augmentations (V_flip, H_flip, Rotation) only for format="npy", not for format="tif", to prevent shape mismatch errors

1.3 (28/07/2025)

• Added support for generating patches from polygon shapefiles in TrainPatch, enabling segmentation and/or object detection
• Updated shapefile processing to reproject to WGS84 (EPSG:4326) before clipping and rasterization to prevent label mismatches due to CRS issues
• Removed redundant shapefile_path parameter in generate_from_shapefile, using label and label_field from TrainPatch initialization
• Added dependencies (geopandas, shapely, scikit-image) to support shapefile processing and bounding box generation

1.3.1 (28/07/2025)

• minor bugs resolved

1.3.2 (28/07/2025)

• Removed verbose print statements in preprocess_and_rasterize function to streamline output and improve user experience
• Updated TrainPatch initialization to explicitly handle shapefile_path and label_field parameters for clarity
• Improved documentation to reflect updated TrainPatch usage with shapefile inputs