CNN-Sentinel | Analyzing Sentinel-2 satellite data | Machine Learning library

by jensleitloff Python Version: v1.1 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | CNN-Sentinel Summary

CNN-Sentinel is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. CNN-Sentinel has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Overview about state-of-the-art land-use classification from satellite data with CNNs based on an open dataset.

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CNN-Sentinel has a low active ecosystem.

It has 142 star(s) with 53 fork(s). There are 12 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 13 have been closed. On average issues are closed in 5 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CNN-Sentinel is v1.1

Quality

CNN-Sentinel has 0 bugs and 0 code smells.

Security

CNN-Sentinel has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CNN-Sentinel code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CNN-Sentinel is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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CNN-Sentinel releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

CNN-Sentinel saves you 297 person hours of effort in developing the same functionality from scratch.

It has 716 lines of code, 5 functions and 9 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Get the mean and standard deviation of the image
Generate an image generator
Preprocessing preprocessing
Create a categorical label from full files
Preprocessing for images

Get all kandi verified functions for this library.

CNN-Sentinel Key Features

No Key Features are available at this moment for CNN-Sentinel.

CNN-Sentinel Examples and Code Snippets

No Code Snippets are available at this moment for CNN-Sentinel.

Community Discussions

Trending Discussions on CNN-Sentinel

Incompatible shape problem with Keras ( segmentation model) for batch_size>1

QUESTION

Incompatible shape problem with Keras ( segmentation model) for batch_size>1

Asked 2020-Sep-23 at 10:59

I am trying to do semantic segmentation using Unet from segmentation model for multi channel (>3) image. The code works if the batch_size =1. But if I change the batch_size to other values (e.g. 2) then error occurs (InvalidArgumentError: Incompatible shapes):

...

ANSWER

Answered 2020-Sep-23 at 10:59

This error was solved by redefining a new image generator instead of simple_image_generator(). The simple_image_generator() worked well with the shape of the images (8 Bands) but did not cope well with the shape of the mask (1 band ).

During the execution, image_generator had 4 dimensions with [2,256,256,1] ( i.e. batch_size, (image size), bands) BUT mask_generator had 3 dimensions only vs. [2,256,256] (i.e. batch_size,(image size))

So reshaping the mask of [2,256,256] to [2,256,256, 1] solved the issue.

Source https://stackoverflow.com/questions/63867982

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