sentinel2-cloud-detector
labelme
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| sentinel2-cloud-detector | labelme | |
|---|---|---|
| 3 | 6 | |
| 397 | 12,257 | |
| 3.0% | 2.7% | |
| 5.9 | 8.9 | |
| 3 months ago | 9 days ago | |
| Python | Python | |
| Creative Commons Attribution Share Alike 4.0 | GNU General Public License v3.0 or later |
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sentinel2-cloud-detector
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Earth 2020 in 3 seconds, extracted from over 3 petabytes of satellite data | [OC]
I'm not exactly sure what you mean by applying QC cloud mask. Do you mean utilizing the official available cloud masks provided by ESA? If yes, then I should mention that we in fact performed our own cloud detection, which is based on a machine-learning approach. (More info [here](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13) and [here](https://github.com/sentinel-hub/sentinel2-cloud-detector)).
Our cloud detector is open-sourced, you can check out the blog post her, or surf over to the code on GitHub directly!
You can find more info in this Jupyter notebook example, the first part is downloading the data (for this you need the account), but if you start later on with the assumption that you bring the data yourself. You can open the ticket if you run into any issues :)
labelme
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labelme VS anylabeling - a user suggested alternative
2 projects | 15 Apr 2023
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Use cases for PySide
Image, 3D, or data visualization applications using OpenCV and the SciPy ecosystem. The Graphics View Framework can display an image and let the user interact with it, and the Python ecosystem is very rich for image processing, data analysis, and visualization. For example, LabelMe for image labeling, PyQtGraph for scientific graphics, or custom QWidget integration in Maya.
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Mask RCNN Implementation for Image Segmentation | Tutorial
LabelMe is open-source tool for polygen image annotations inspired by MIT Label Me
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C++ trainable semantic segmentation models
Create your own dataset. Using labelme through "pip install" and label your images. Split the output json files and images into folders just like below:
What are some alternatives?
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
anylabeling - Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!!
json - JSON for Modern C++
biodivMapR - biodivMapR: an R package for α- and β-diversity mapping using remotely-sensed images
labelImg - LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data. [Moved to: https://github.com/HumanSignal/labelImg]
labelImg - LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.