A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and...

Area of Science:

• Earth Science
• Computer Science
• Artificial Intelligence

Background:

• Remote sensing (RS) provides vast environmental data via satellite imagery and sensors.
• Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is crucial for processing complex RS datasets.
• AI enhances capabilities in environmental monitoring, disaster response, agriculture, and urban planning.

Purpose of the Study:

• To provide a comprehensive survey of recent advancements in the integration of RS and AI.
• To highlight key methodologies, applications, and challenges at the intersection of these fields.
• To discuss future research directions for AI-driven Earth observation.

Main Methods:

• Review of AI algorithms including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Reinforcement Learning (RL).
• Analysis of AI applications in feature extraction, classification, anomaly detection, and predictive modeling for RS data.
• Examination of challenges such as model generalization, explainability, data heterogeneity, and ethical considerations.

Main Results:

• AI significantly enhances the automation, efficiency, and precision of RS data analysis.
• AI models demonstrate strong performance in interpreting complex environmental datasets.
• Significant opportunities exist for AI-driven decision-making in Earth observation.

Conclusions:

• AI-driven RS offers transformative potential for global-scale environmental monitoring and management.
• Addressing challenges in model generalization, explainability, and ethics is critical for future progress.
• Future research should focus on multimodal learning and real-time AI deployment for enhanced Earth observation.