Evaluate the evolution and current capabilities of raster data analysis in geospatial applications, including the handling of multi-dimensional data types, resolution characteristics, and integration with AI-driven approaches.
This topic entry outlines the recent advances and the transformative integration of artificial intelligence with geospatial science, focusing on modern programming libraries and infrastructures for raster data analysis. We explain how the convergence of AI and geospatial technologies has revolutionized Earth observation analysis through advanced machine learning algorithms, computer vision techniques, and modern infrastructures. The entry highlights the evolution of raster data analysis, from traditional processing methods to sophisticated AI-driven approaches that enable automated feature extraction and object detection at an unprecedented scale. We discuss key developments in programming infrastructure, including Python-based frameworks, GPU-accelerated computing solutions, and cloud-native platforms that have emerged to address the challenges of big Earth data. Special attention is given to dataframe-based analysis approaches and distributed computing frameworks that have enhanced the processing capabilities of large-scale geospatial datasets. We also introduce the cloud computing platforms such as Google Earth Engine, AWS, and Microsoft’s Planetary Computer, which have democratized access to Earth observation analysis and empowered researchers to conduct impactful research using geospatial data. By presenting this comprehensive overview, we provide insights into current capabilities and future directions in the field, emphasizing the continuing evolution of GeoAI technologies and their impact on environmental monitoring, urban planning, resource management, etc.