QGIS Python Plugins Repository
Plugins tagged with: machine-learning
10 records found —
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| Name | Featured | Downloads | Author | Latest Plugin Version | Created on | Stars (votes) | Stable | Exp. | |
|---|---|---|---|---|---|---|---|---|---|
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AIAMAS - AI-Assisted Map Styler | — | 707 | Diego Fantino, Niklas Vogel, Samuel Meuli, Stefan Keller | 2024-07-17T16:35:52.952480+00:00 | 2024-07-12T13:34:50.724966+00:00 | (11) |
— | 0.2.0 |
| Symbolize your vector layers in seconds | |||||||||
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Bunting Labs AI Vectorizer | — | 38091 | Brendan Ashworth | 2024-09-05T04:14:10.787669+00:00 | 2023-12-19T20:07:11.462525+00:00 | (74) |
2.7 | — |
| Intelligent autocomplete for vectorizing raster maps. | |||||||||
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ClusterPoints | — | 29354 | Johannes Jenkner | 2023-08-18T05:52:37.378747+00:00 | 2020-03-30T11:15:13.154597+00:00 | (31) |
6.1 | — |
| Cluster Points conducts clustering of points based on their mutual distance to each other or based on supplemental information from attributes. The user can select between the K-Means or Fuzzy C-Means algorithms and (agglomerative) hierarchical clustering with several different link functions. To find the best possible grouping of points, both geographical coordinates and numerical fields can be incorporated into the algorithm to a varying degree. | |||||||||
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Deep Learning Datasets Maker | — | 3619 | deepbands (Youssef Harby and Yizhou Chen) | 2022-01-19T02:15:29.531264+00:00 | 2021-12-11T04:40:00.358152+00:00 | (8) |
— | 0.2.1 |
| tools to handle raster and vector data to split it into small pieces equaled in size for machine learning datasets | |||||||||
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Deepness: Deep Neural Remote Sensing | — | 22467 | PUT Vision | 2024-04-14T12:48:03.235279+00:00 | 2022-10-20T11:38:13.982136+00:00 | (84) |
0.6.3 | 0.3.0 |
| Inference of deep neural network models (ONNX) for segmentation, detection and regression | |||||||||
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EnMAP-Box 3 | — | 74343 | Andreas Janz, Benjamin Jakimow, Sebastian van der Linden, Fabian Thiel, Henrike Dierkes | 2024-05-24T14:40:43.216970+00:00 | 2019-03-07T11:24:07.383848+00:00 | (103) |
3.14.3 | 3.14.1 |
| Imaging Spectroscopy and Remote Sensing for QGIS | |||||||||
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Mapflow | — | 107143 | Geoalert | 2024-08-19T16:20:11.884536+00:00 | 2021-07-09T15:15:42.595345+00:00 | (96) |
2.6.2 | — |
| Extract real-world objects from satellite imagery with Mapflow by Geoalert. Mapflow provides AI mapping pipelines for building footprints, roads, fields, forest and construction sites. | |||||||||
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Map Segmenter | — | 6283 | Quant Civil | 2024-09-04T03:26:41.853402+00:00 | 2023-06-16T05:54:20.498604+00:00 | (12) |
2.1.1 | — |
| Uses machine learning to segment a map into ares of interest. | |||||||||
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Nimbo's Earth Basemaps | — | 11708 | Kermap | 2024-07-09T10:07:28.144639+00:00 | 2023-09-04T14:12:30.873471+00:00 | (144) |
0.7 | — |
| Nimbo's Earth Basemaps is an innovative Earth observation service providing cloud-free, homogenous mosaics of the world's entire landmass as captured by satellite imagery. Updated every month.<br><br>Along with this unprecedented refresh rate, Nimbo Earth Basemaps provides users with 4 data layers every month :<br><br>- Natural colors (RGB)<br>- Infrared (NIR)<br>- NDVI (vegetation helath index)<br>- Radar (SAR, VV/VH combination)<br><br>Maximum image resolution is 10m/px.<br>Images are served in WMS/WMTS format.<br><br>Nimbo Earth Basemaps meets a variety of requirements for geospatial analysis professionals, including visualizations fit for machine learning algorithm implementation, monitoring tasks and training models based on time series.<br><br>Nimbo Earth Basemaps' views consist of monthly syntheses using imagery from Sentinel 1 and 2 satellites from the European Copernicus programme.<br><br>Nimbo is developed and published exclusively by Kermap:<br><a href="https://kermap.com" >https://kermap.com</a> | |||||||||
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TreeEyed | — | 420 | Andrés Felipe Ruiz-Hurtado, Tropical Forages - CIAT | 2024-09-05T22:07:13.241867+00:00 | 2024-09-05T22:07:11.437526+00:00 | (3) |
0.1 | — |
| TreeEyed is a QGIS plugin for tree monitoring using AI. | |||||||||
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