Artificial Intelligence-Based Learning Approaches for Remote Sensing

Price: $107.52 – $90.49
(as of Dec 27,2024 12:27:26 UTC – Details)

Publisher ‏ : ‎ Mdpi AG (December 13, 2022)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 382 pages
ISBN-10 ‏ : ‎ 3036560831
ISBN-13 ‏ : ‎ 978-3036560830
Item Weight ‏ : ‎ 2.25 pounds
Dimensions ‏ : ‎ 6.69 x 1.19 x 9.61 inches

Artificial Intelligence-Based Learning Approaches for Remote Sensing

Remote sensing technologies have revolutionized our ability to gather information about the Earth’s surface from a distance. From monitoring deforestation and urban growth to tracking weather patterns and natural disasters, remote sensing plays a crucial role in various fields. However, the vast amounts of data generated by remote sensing satellites can be overwhelming to analyze manually.

This is where artificial intelligence (AI) comes into play. AI-based learning approaches are being increasingly utilized to process and analyze remote sensing data more efficiently and accurately. Machine learning algorithms can be trained to recognize patterns in imagery, classify land cover types, detect anomalies, and predict future trends.

One of the key advantages of using AI in remote sensing is its ability to handle large datasets quickly and accurately. By training AI models with labeled data, researchers can automate the process of image interpretation and classification, saving time and resources.

Furthermore, AI can help improve the resolution and accuracy of remote sensing data by correcting for atmospheric distortions and enhancing image quality. This can lead to more precise and reliable data for various applications, such as agriculture, forestry, urban planning, and disaster response.

Overall, artificial intelligence-based learning approaches are revolutionizing the field of remote sensing by enabling faster, more accurate, and more insightful analysis of Earth’s surface. As AI technologies continue to advance, we can expect even greater insights and discoveries to be made using remote sensing data.
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