QGIS Python Plugins Repository
Deepness: Deep Neural Remote Sensing
Inference of deep neural network models (ONNX) for segmentation, detection and regression
Deepness plugin allows to easily perform segmentation, detection and regression on raster ortophotos with custom ONNX Neural Network models, bringing the power of deep learning to casual users.
Features highlights:
- processing any raster layer (custom ortophoto from file or layers from online providers, e.g Google Satellite)
- limiting processing range to predefined area (visible part or area defined by vector layer polygons)
- common types of models are supported: segmentation, regression, detection
- integration with layers (both for input data and model output layers). Once an output layer is created, it can be saved as a file manually
- model ZOO under development (planes detection on Bing Aerial, Corn field damage, Oil Storage tanks detection, cars detection, ...)
- training data Export Tool - exporting raster and mask as small tiles
- parametrization of the processing for advanced users (spatial resolution, overlap, postprocessing)
Plugin requires external python packages to be installed. After the first plugin startup, a Dialog will show, to assist in this process. Please visit plugin the documentation for details.
- Author
- PUT Vision
- Maintainer
- przemyslawaszkowski
- Tags
- analysis , classification , remote sensing , regression , supervised classification , machine learning , segmentation , deep learning , neural network , onnx , deepness , detection
- Plugin home page
- https://qgis-plugin-deepness.readthedocs.io/
- Tracker
- Browse and report bugs
- Code repository
- https://github.com/PUTvision/qgis-plugin-deepness
- Latest stable version
- 0.6.5
- Latest experimental version:
- 0.3.0
- Plugin ID
-
2802
| Version | Experimental | Min QGIS version | Max QGIS version | Downloads | Uploaded by | Date |
|---|---|---|---|---|---|---|
| 0.6.5 | no | 3.22.0 | 3.99.0 | 1489 | przemyslawaszkowski | 2024-12-10T10:38:22.549072+00:00 |
| 0.6.4 | no | 3.22.0 | 3.99.0 | 5120 | przemyslawaszkowski | 2024-10-04T09:42:41.086754+00:00 |
| 0.6.3 | no | 3.22.0 | 3.99.0 | 7228 | przemyslawaszkowski | 2024-04-14T12:48:03.235279+00:00 |
| 0.6.2 | no | 3.22.0 | 3.99.0 | 1677 | przemyslawaszkowski | 2024-03-22T12:30:43.959338+00:00 |
| 0.6.1 | no | 3.22.0 | 3.99.0 | 1828 | przemyslawaszkowski | 2024-02-23T11:32:04.941600+00:00 |
| 0.6.0 | no | 3.22.0 | 3.99.0 | 1189 | przemyslawaszkowski | 2024-02-09T10:30:03.794279+00:00 |
| 0.5.4 | no | 3.22.0 | 3.99.0 | 3849 | przemyslawaszkowski | 2023-09-28T10:09:09.402499+00:00 |
| 0.5.3 | no | 3.22.0 | 3.99.0 | 1008 | przemyslawaszkowski | 2023-09-18T20:28:56.295377+00:00 |
| 0.5.1 | no | 3.22.0 | 3.99.0 | 1912 | przemyslawaszkowski | 2023-07-17T09:31:40.398743+00:00 |
| 0.4.1 | no | 3.22.0 | 3.99.0 | 3031 | przemyslawaszkowski | 2022-11-14T14:16:58.822580+00:00 |
| 0.4.0 | no | 3.10.14 | 3.99.0 | 1857 | przemyslawaszkowski | 2022-10-27T07:21:41.082963+00:00 |
| 0.3.0 | yes | 3.10.14 | 3.99.0 | 454 | przemyslawaszkowski | 2022-10-20T11:38:14.243751+00:00 |
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