Coastal zones, where land meets ocean, are home to a large portion of the global population and play a crucial role in human survival and development. These regions are shaped by complex geological processes and influenced by both natural and anthropogenic factors, making effective management essential for addressing population growth, environmental degradation, and resource sustainability. However, the inherent complexity of coastal zones complicates their study, and traditional in situ methods are often inefficient. Remote sensing technologies have significantly advanced coastal zone research, with different sensors providing diverse perspectives. These sensors are typically used for classification tasks (e.g., coastline extraction, coastal classification) and retrieval tasks (e.g., aquatic color, wetland monitoring). Recent improvements in resolution and the advent of deep learning have led to notable progress in classification, while platforms like Google Earth Engine (GEE) have enabled the development of high-quality, global-scale products. This paper provides a comprehensive overview of coastal zone interpretation, discussing platforms, sensors, spectral characteristics, and key challenges while proposing potential solutions for future research and management.

With the development of global maritime trade, ship emissions pose an increasing threat to the global atmospheric environment, especially in international navigation waters and important port areas, where their impact on coastal air quality and ecosystems is becoming increasingly significant. This study proposes a high-throughput gridding algorithm (H-Grid) based on spatial hashing to rapidly generate ship emission inventories, which overcomes the inefficiency of traditional methods caused by complex index building and maintenance. The H-Grid algorithm achieves a constant processing time per data point and possesses inherent parallelism. Based on the H-Grid algorithm, taking the Yellow Sea area between China and Republic of Korea as a case study, the emissions of atmospheric pollutants from ships in 2024 were calculated, and their spatiotemporal distribution characteristics were analyzed. In our empirical study, the algorithm’s computational efficiency for processing millions of AIS records was improved by over 10 times compared to traditional geometric calculations, and by more than 4 times when compared to mainstream database spatial queries. Our findings provide an efficient tool for large-scale maritime emission analysis, strongly supporting the green development of global shipping.