Publication Date: 2023/11/07
Abstract: The abstract of a paper on the prospects and restrictions of applying artificial intelligence (AI) for remote sensing data interpretation will most likely discuss the junction of AI and remote sensing, stressing potential benefits and challenges. It may discuss advances in AI approaches and their applications in evaluating large and complicated remote sensing datasets, as well as limitations and issues that researchers and practitioners should be aware of. The abstract could underline the significance of precise remote sensing data interpretation for environmental monitoring, resource management, disaster response, and other essential applications. The rapid growth of artificial intelligence (AI) tools has changed the field of remote sensing data interpretation, creating previously unimaginable prospects for extracting useful insights from enormous and complicated datasets. This study provides an in-depth examination of the opportunities and restrictions involved with using AI to understand remote sensing data, offering insight on the revolutionary potential of this integration. This research also discusses the essential restrictions and challenges associated with AI integration in remote sensing. Some AI models are black boxes, which raises concerns about transparency, interpretability, and the possibility of biased decision-making. To ensure the ethical use of AI in remote sensing interpretation, a careful balance of algorithmic complexity and the capacity to give interpretable results that fit with domain knowledge must be struck. This article offers a comprehensive evaluation of the opportunities and restrictions associated with using artificial intelligence to understand remote sensing data. Researchers and practitioners can use AI's revolutionary potential to gain deeper insights into Earth's dynamics and contribute to a more sustainable and informed future.
Keywords: Artificial Intelligence, Remote Sensing, Constraints, Interpretation, Prospects.
DOI: https://doi.org/10.5281/zenodo.10077294
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23OCT1223.pdf
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