geodeep 0.9.8

Free and open source library for AI object detection in geospatial rasters

GeoDeep

A fast, easy to use, lightweight Python library for AI object detection in geospatial rasters (GeoTIFFs), with pre-built models included.

Install

pip install -U geodeep

Usage

From the command line

geodeep [geotiff] [model ID or path to ONNX model]

Example:

geodeep orthophoto.tif cars

Here GeoDeep will find cars in the orthophoto and write the result as a GeoJSON file containing the bounding boxes, confidence scores and class labels.

A list of up-to-date model IDs can be retrieved via:

geodeep --list-models

See also geodeep --help.

From Python

from geodeep import detect
bboxes, scores, classes = detect('orthophoto.tif', 'cars')
print(bboxes) # <-- [[x_min, y_min, x_max, y_max], [...]]
print(scores) # <-- [score, ...]
print(classes) # <-- [(id: int, label: str), ...]

geojson = detect('orthophoto.tif', 'cars', output_type="geojson")

Models by default will be cached in ~/.cache/geodeep. You can change that with:

from geodeep import models
models.cache_dir = "your/cache/path"

Models

Model	Description	Resolution (cm/px)	Experimental	Classes
cars	YOLOv7-m model for cars detection on aerial images. Based on ITCVD.	10		car
trees	Retinanet tree crown detection model from DeepForest	10	:heavy_check_mark:	tree
trees_yolov9	YOLOv9 model for treetops detection on aerial images. Model is trained on a mix of publicly available datasets.	10	:heavy_check_mark:	tree
birds	Retinanet bird detection model from DeepForest	2	:heavy_check_mark:	bird
planes	YOLOv7 tiny model for object detection on satellite images. Based on the Airbus Aircraft Detection dataset.	70	:heavy_check_mark:	plane
aerovision	YOLOv8-m for multi-class detection on aerial images.	30	:heavy_check_mark:	[1]

1. small-vehicle, large-vehicle,plane,storage-tank,ship,dock,ground-track-field,soccer-field,tennis-court,swimming-pool,baseball-field,road-circle,basketball-court,bridge,helicopter,crane

All ONNX models are published on https://huggingface.co/datasets/UAV4GEO/GeoDeep-Models

Training New Models

In short, first you need to train a YOLO model, then you run yolo2geodeep. See below for details.

Requirements

You need a decent GPU and plenty of RAM. It's possible to train models on a CPU, but it will take weeks (maybe even months). There's also platforms that will do the training for you if you don't have the necessary hardware.

Step 1. Gather annotated images

A good point to start is https://universe.roboflow.com/browse/aerial, but the quality of the datasets is all over the place. Always inspect before using. When downloading a dataset, choose the YOLOv8 format.

You can also annotate your own images.

Aim to gather at least 1000 training images for decent results.

Step 2. Train a YOLO model

For up to date instructions, follow the steps on https://docs.ultralytics.com/modes/train/. Alsk make sure to install a GPU version of pytorch (https://pytorch.org/get-started/locally/).

Once you have a folder with your annotated images (e.g. dataset/train, dataset/valid), check your data.yaml to make sure you have the correct number of classes, then run:

yolo train task=detect model=yolov8s.pt data=dataset\data.yaml epochs=400

There's also several settings you can tweak, but start with the defaults.

Once the processes is done, you'll end up with a best.pt (model weights) file, usually in runs/detect/trainX/weights/best.pt.

Step 3. Convert the YOLO model to ONNX

Before converting, you should estimate the ground sampling distance (GSD) resolution of your training data (in cm/px). This affects the model quality quite a bit so it's important to have a good estimate. If you're unsure, you can just start with a reasonable value (e.g. 10 or 20 for aerial datasets) and run a few experiments to see which value yields the best results.

Then:

yolo2geodeep runs/detect/trainX/weights/best.pt [resolution]

[...]
Wrote runs/detect/trainX/weights/best.quant.onnx <-- Use this with GeoDeep

You can finally run:

geodeep orthophoto.tif runs/detect/trainX/weights/best.quant.onnx

You can also convert existing ONNX models for use with GeoDeep. See the retinanet conversion script for an example. In some cases modifications to GeoDeep might be required if the model architecture is not supported. Currently GeoDeep supports:

• YOLO 5,6,7,8,9
• Retinanet

Other architectures can be added. Pull requests welcome!

Inspect Models

You can inspect an existing model by running:

geodeep-inspect [model ID or path to ONNX model]

For example:

geodeep-inspect cars

det_type: YOLO_v5_or_v7_default
det_conf: 0.3
det_iou_thresh: 0.8
det_classes: []
resolution: 10.0
class_names: {'0': 'car'}
model_type: Detector
tiles_overlap: 10.0
tiles_size: 640
input_shape: [1, 3, 640, 640]
input_name: images

Why GeoDeep?

Compared to other software packages (e.g. Deepness), GeoDeep relies only on two dependencies, rasterio and onnxruntime. This makes it simple and lightweight.

Does this need a GPU?

It does not! Models are tuned to run fast on the CPU.

Contributing

We welcome contributions! Pull requests are welcome.

Roadmap Ideas

• Train more detection models
• Add support for semantic segmentation models
• Faster inference optimizations

Support the Project

There are many ways to contribute to the project:

• ⭐️ us on GitHub.
• Help us test the application.
• Become a contributor!

Credits

GeoDeep was inspired and uses some code from Deepness and DeepForest.

License

The code in this repository is licensed under the AGPLv3.

Made with ❤️ by UAV4GEO