GeoDeep

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

Install

Install Python if you haven't already, then from a command prompt / terminal type:

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pip install -U geodeep

Usage

From the command line

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

Object Detection

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geodeep orthophoto.tif cars

This will create a GeoJSON file with the bounding boxes, confidence scores and class labels of all cars detected in the orthophoto.

Semantic Segmentation

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geodeep orthophoto.tif buildings

Areas that correspond to buildings will be saved as a GeoJSON file containing the polygons that approximate the building footprints. You can also export a georeferenced raster mask via:

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geodeep orthophoto.tif buildings -t mask

Note you should not expect the output mask to have the same width and height as the input raster, since most models are trained at a different resolution. You can always resize the mask later.

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

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geodeep --list-models

See also geodeep --help.

From Python

Object Detection

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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")

Semantic Segmentation

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from geodeep import segment
from geodeep.segmentation import save_mask_to_raster
mask = segment('orthophoto.tif', 'buildings')
print(mask.shape) # <-- np.ndarray([height, width], dtype=np.uint8)

save_mask_to_raster('orthophoto.tif', mask, 'segmentation.tif')

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

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

Models

Object Detection

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 model for multi-class detection on aerial images.	30	:heavy_check_mark:	[1]
utilities	YOLOv8 model for detecting paint markings placed on the ground by utility locators that commonly work for 811 Call before you dig.	3	:heavy_check_mark:	[2]
waldo30_nano	YOLOv8 model for multi-class detection from W.A.L.D.O	5	:heavy_check_mark:	[3]

1. small-vehicle, large-vehicle,plane,storage-tank,boat,dock,track-field,soccer-field,tennis-court,swimming-pool,baseball-field,road-circle,basketball-court,bridge,helicopter,crane
2. Gas, Manhole, Power, Reclaimed, Sewer, Telecom, Water
3. light-vehicle,person,building,upole,boat,bike,container,truck,gastank,digger,solar-panels,bus

Semantic Segmentation

Model	Description	Resolution (cm/px)	Experimental	Classes
buildings	Trained on RampDataset. Annotation masks for buildings and background.	50	:heavy_check_mark:	Background, Building
roads	The model segments the Google Earth satellite images into ‘road’ and ‘not-road’ classes. Model works best on wide car roads, crossroads and roundabouts.	21	:heavy_check_mark:	not_road, road

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

Training Detection Models

In short, first you need to train a YOLO model, then you run yolo2geodeep. See below for details. If you already have a YOLO model, skip directly to Step 3.

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/. Also 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:

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

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

You can finally run:

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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!

Step 4. (Optional) Share Your Model

The most convenient way to deploy your model is to share it. Open a pull request on https://huggingface.co/datasets/UAV4GEO/GeoDeep-Models and we'll include it in GeoDeep!

Training Segmentation Models

Instructions coming soon!

Inspect Models

You can inspect an existing model by running:

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

For example:

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geodeep-inspect cars

det_type: YOLO_v5_or_v7_default
det_conf: 0.3
det_iou_thresh: 0.8
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

Converting Existing YOLO Detection Models

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yolo2geodeep /path/to/weights.pt 10 --det-type YOLO_v8 --classes car,bus

See also yolo2geodeep --help.

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 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