QGIS Python Plugins Repository
Plugins tagged with: machine-learning
7 records found —
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| Name ↓ | Featured | Downloads | Author | Latest Plugin Version | Created on | Stars (votes) | Stable | Exp. | |
|---|---|---|---|---|---|---|---|---|---|
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Nimbo's Earth Basemaps | — | 1749 | Kermap | Oct. 23, 2023 | Sept. 4, 2023 | (121) |
0.3 | — |
| 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. | |||||||||
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Mapflow | — | 66348 | Geoalert | Oct. 2, 2023 | July 9, 2021 | (63) |
2.3.0 | — |
| 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 | — | 1894 | Quant Civil | Nov. 23, 2023 | June 16, 2023 | (2) |
2.0.4 | — |
| Uses machine learning to segment a map into ares of interest. | |||||||||
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EnMAP-Box 3 | — | 55232 | Andreas Janz, Benjamin Jakimow, Sebastian van der Linden, Fabian Thiel, Henrike Dierkes | Nov. 23, 2023 | March 7, 2019 | (83) |
3.13.2 | 3.13.0 |
| Imaging Spectroscopy and Remote Sensing for QGIS | |||||||||
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Deepness: Deep Neural Remote Sensing | — | 8473 | PUT Vision | Sept. 28, 2023 | Oct. 20, 2022 | (54) |
0.5.4 | 0.3.0 |
| Inference of deep neural network models (ONNX) for segmentation, detection and regression | |||||||||
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Deep Learning Datasets Maker | — | 2631 | deepbands (Youssef Harby and Yizhou Chen) | Jan. 18, 2022 | Dec. 10, 2021 | (3) |
— | 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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ClusterPoints | — | 24236 | Johannes Jenkner | Aug. 18, 2023 | March 30, 2020 | (24) |
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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