deep-unet-for-satellite-image-segmentation | Satellite Imagery Feature Detection with SpaceNet dataset | Machine Learning library

by reachsumit Python Version: Current License: No License

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kandi X-RAY | deep-unet-for-satellite-image-segmentation Summary

deep-unet-for-satellite-image-segmentation is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. deep-unet-for-satellite-image-segmentation has no bugs, it has no vulnerabilities and it has low support. However deep-unet-for-satellite-image-segmentation build file is not available. You can download it from GitHub.

This is a Keras based implementation of a deep UNet that performs satellite image segmentation.

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deep-unet-for-satellite-image-segmentation has a low active ecosystem.

It has 292 star(s) with 134 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 14 open issues and 12 have been closed. On average issues are closed in 49 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deep-unet-for-satellite-image-segmentation is current.

Quality

deep-unet-for-satellite-image-segmentation has no bugs reported.

Security

deep-unet-for-satellite-image-segmentation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

deep-unet-for-satellite-image-segmentation does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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deep-unet-for-satellite-image-segmentation releases are not available. You will need to build from source code and install.

deep-unet-for-satellite-image-segmentation has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed deep-unet-for-satellite-image-segmentation and discovered the below as its top functions. This is intended to give you an instant insight into deep-unet-for-satellite-image-segmentation implemented functionality, and help decide if they suit your requirements.

Constructs unetenernet model .
Generate a random patch
Predict the image .
Generate a picture from a given mask .
Generate n_patches for each image .
Normalize image .
Create a unetensor model .

Get all kandi verified functions for this library.

deep-unet-for-satellite-image-segmentation Key Features

No Key Features are available at this moment for deep-unet-for-satellite-image-segmentation.

deep-unet-for-satellite-image-segmentation Examples and Code Snippets

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Community Discussions

Trending Discussions on deep-unet-for-satellite-image-segmentation

Tensorflow Keras: Can `Conv2d` layers now accept multispectral images meaning bands greater than 3

QUESTION

Tensorflow Keras: Can `Conv2d` layers now accept multispectral images meaning bands greater than 3

Asked 2019-Mar-21 at 18:50

I have some 8-band satellite images and wanted to do some image segmentation with Tensorflow and Keras. I tried to do this a couple of years ago, but saw that TF and Keras could not handle images with bands greater than 3. However, I am seeing more blog posts about deep learning with multiband images.

In looking at the Keras documentation, it does not specifically list any problems with accepting multiband images. And I found this code which seems to make it work:

...

ANSWER

Answered 2019-Mar-21 at 18:50

Yes the can accept more 8 channels. Both Keras and TensorFlow layers. The main problem with images that have more than 3 channels is that majority of readily accessible pre-trained models were trained on standard imagenet dimensions, like [299,299,3] In this case it will require considerable ammout of work to fine tune such model to your data. As a solution to this you can insert a special 'resizing' convolutional layer which will reshape it to 3 layers.

Source https://stackoverflow.com/questions/55286115

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Vulnerabilities

No vulnerabilities reported

Install deep-unet-for-satellite-image-segmentation

You can download it from GitHub.
You can use deep-unet-for-satellite-image-segmentation like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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