Artificial Intelligence and Blockchain-Based Trading Framework for Satellite Images

Hashim, Faiza; Navaz, Alramzana; Zaki, Nazar; Shuaib, Khaled; Serhani, Mohamed Adel

doi:10.1109/iwcmc58020.2023.10182415

Cited by 2 publications

References 22 publications

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AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data

Segar,

Kaliyaperumal,

Pazhanivelan

et al. 2024

AST

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Abstract. Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficiency of plantation mapping. Utilizing AI and machine learning tools for plantation mapping offers a transformative approach to efficient and accurate land management. These technologies enable automated analysis of satellite imagery and other geospatial data, facilitating rapid and precise identification of plantations, crop health assessment, and yield predictions. The integration of AI enhances the mapping process, providing valuable insights for sustainable agriculture, resource optimization, and environmental monitoring. The application of these advanced tools in plantation mapping represents a significant leap towards data-driven and environmentally conscious land management practices. It presents a promising advancement in agricultural practices. By leveraging these technologies for automated analysis of satellite imagery and geospatial data, accurate and timely mapping of plantations becomes feasible. The use of AI and ML tools in Plantation mapping, challenges in integration, the possible solutions and its future prospects are reviewed in this paper not only to enhance efficiency but also to offer insights into crop health, aiding in precision agriculture and resource optimization.

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AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data

Segar,

Kaliyaperumal,

Pazhanivelan

et al. 2024

AST

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Navigating the Crescendo of Challenges in Harnessing Artificial Intelligence for Disaster Management

Manoharan,

Razak,

Rao

et al. 2024

Advances in Computational Intelligence and Robotics

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Global warming worsens natural disasters and humanitarian issues. Disaster prediction relies on satellites and weather stations. AI may help catastrophe management. AI reduces disaster risk in many ways. Early warning systems, weather forecasts, disaster recovery, and reconstruction improve. AI could help us predict, prepare, and recover from natural calamities. These technologies provide climate change mitigation and community protection hope. They propose a better future amid climate change catastrophes. DRR is aggressively adopting AI, notably ML. This field encompasses severe event prediction, hazard mapping, real-time detection, situational awareness, decision assistance, and more. Growing usage of AI in disaster management raises questions about its benefits. We face what issues? How can these difficulties be resolved and opportunities maximised? What can AI tell policymakers, stakeholders, and the public to reduce disasters? The chapter introduces catastrophe management AI, AI implementation issues, and solutions to make the world more peaceful will be addressed.

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Artificial Intelligence and Blockchain-Based Trading Framework for Satellite Images

Cited by 2 publications

References 22 publications

AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data

AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data

Navigating the Crescendo of Challenges in Harnessing Artificial Intelligence for Disaster Management

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