Land Use Classification in Remote Sensing Images by Convolutional NeuralNetworks

AI Reads Satellite Photos to Map Land Use Faster and Better

Imagine a computer that looks at satellite photos and tells you what land is used for — farms, cities, forests.
This story shows how simple tools from AI can learn to do that, even when pictures are very different.
We tried a few training approaches, including starting from scratch and tuning already-trained systems, and found tuning often makes things quicker and avoid overfitting.
The outcome was clear: the system becomes more accurate at recognizing places, and it learns with less effort.
Tests on two very different sets of images showed big gains over older methods.
That means planners, researchers and everyday people can get up-to-date maps that are fast to create and really useful for decisions.
It not magic but clever design, and it works on real world data.
Expect better land maps, faster updates, and new ways to watch how our planet changes — without needing lots of manual work or expert tuning every time.

Read article comprehensive review in Paperium.net:
Land Use Classification in Remote Sensing Images by Convolutional NeuralNetworks

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