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Improve the Versatility and Tolerance of CNN in Crop Mapping #29

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@ZihengSun

ESIP Member Organization Name

Your organization:
George Mason University

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Ziheng Sun

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Project Idea: Improve the Versatility and Tolerance of CNN in Crop Mapping

Abstract

Improve the Versatility of CNN in Crop Mapping and the tolerance of noises in the data.

Technical Details

We change the classification workflow of CNN-based crop mapping to improve the versatility of the trained model and the tolerance of the noises in the training data (coming from remote sensing images and ground truth data).

Helpful Experience

Computer programming
Machine learning
Deep learning
Python

First steps

Students doesn't need to do this before Google Summer of Code code period starts
but will be good if they do just because they will be sure if this is how they
want to spend the summer.

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