geo-inference is a Python package designed for feature extraction from geospatial imagery using compatible deep learning models. It provides a convenient way to extract features from large TIFF images and save the output mask as a TIFF file. It also supports converting the output mask to vector format (file_name.geojson), YOLO format (file_name.csv), and COCO format (file_name.json). This package is particularly useful for applications in remote sensing, environmental monitoring, and urban planning.

Geo-inference requires Python 3.11. To install the package, use:

pip install geo-inference

Input: GeoTiffs with compatible TorchScript model. For example: A pytorch model trained on high resolution geospatial imagery with the following features:

• pixel size (0.1m to 3m)
• data type (uint8)

expects an input image with the same features. An example notebook for how the package is used is provided in this repo.

Here's an example of how to use Geo Inference (Command line and Script):

Command line

python geo_inference.py -a <args>

• -a, --args: Path to arguments stored in yaml, consult ./config/sample_config.yaml

python geo_inference.py -i <image> -m <model> -wd <work_dir> -bs <batch_size> -v <vec> -d <device> -id <gpu_id>

• -i, --image: Path to Geotiff
• -bb, --bbox: AOI bbox in this format "minx, miny, maxx, maxy" (Optional)
• -m, --model: Path or URL to the model file
• -wd, --work_dir: Working Directory
• -bs, --batch_size: The Batch Size
• -v, --vec: Vector Conversion
• -y, --yolo: Yolo Conversion
• -c, --coco: Coco Conversion
• -d, --device: CPU or GPU Device
• -id, --gpu_id: GPU ID, Default = 0

You can also use the -h option to get a list of supported arguments:

python geo_inference.py -h

Import script

from geo_inference.geo_inference import GeoInference

# Initialize the GeoInference object
geo_inference = GeoInference(
    model="/path/to/segformer_B5.pt",
    work_dir="/path/to/work/dir",
    batch_size=4,
    mask_to_vec=False,
    vec_to_yolo=False,
    vec_to_coco=False, 
    device="gpu",
    gpu_id=0
)

# Perform feature extraction on a TIFF image
image_path = "/path/to/image.tif"
patch_size = 512
geo_inference(tiff_image = image_path, patch_size = patch_size,)

The GeoInference class takes the following parameters:

• model: The path or URL to the model file (.pt for PyTorch models) to use for feature extraction.
• work_dir: The path to the working directory. Default is "~/.cache".
• batch_size: The batch size to use for feature extraction. Default is 4.
• mask_to_vec: If set to "True", vector data will be created from mask. Default is "False"
• vec_to_yolo: If set to "True", vector data will be converted to YOLO format. Default is "False"
• vec_to_coco: If set to "True", vector data will be converted to COCO format. Default is "False"
• device: The device to use for feature extraction. Can be "cpu" or "gpu". Default is "gpu".
• gpu_id: The ID of the GPU to use for feature extraction. Default is 0.

The GeoInference class outputs the following files:

• mask.tif: The output mask file in TIFF format.
• polygons.geojson: The output polygon file in GeoJSON format. This file is only generated if the mask_to_vec parameter is set to True.
• yolo.csv: The output YOLO file in CSV format. This file is only generated if the mask_to_vec, vec_to_yolo parameters are set to True.
• coco.json: The output COCO file in JSON format. This file is only generated if the mask_to_vec, vec_to_coco parameters are set to True.

Each file contains the extracted features from the input geospatial imagery.

Geo Inference is released under the MIT License. See LICENSE for more information.

For any questions or concerns, please open an issue on GitHub.