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https://doi.org/10.5772/intech...
Part of book or chapter of book . 2023 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Land Use and Land Cover Simulation

Authors: Hafiz Usman Ahmed Khan;

Land Use and Land Cover Simulation

Abstract

It is important to consider the dynamics of Land Use and Land Cover (LULC) patterns and how they affect society and the environment. Traditional LULC change evaluation methods can be inaccurate and require a lot of manual labor. However, AI techniques like deep learning and machine learning have shown a lot of promise for improving and automating LULC simulation processes. In this chapter, the importance of applying AI to LULC simulation is emphasized, along with the precision, efficacy, and scalability it offers. The importance of AI-based LULC simulation for making well-informed decisions in a variety of fields, including urban planning, agriculture, and natural resource management, is highlighted in the abstract’s conclusion. AI-based methods have shown great promise in LULC analysis, producing accurate classification results and paving the way for subsequent simulations. These findings demonstrate the importance. These results highlight the significance of utilizing AI approaches to assist sustainable development, solve environmental issues, and influence land management choices.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
hybrid