The scientific and engineering advancements in the 21st century pose grand computing challenges in managing big data, using complex algorithms to extract information and knowledge from big data, and simulating complex and dynamic physical and social phenomena. Cloud computing emerged as new computing model with the potential to address these computing challenges. This entry first introduces the concept, features and service models of cloud computing. Next, the ideas of generalized architecture and service models of spatial cloud computing are then elaborated to identify the characteristics, components, development and applications of spatial cloud computing for geospatial sciences.
Mobile devices refer to a computing system intended to be used by hand, such as smartphones or tablet computers. Mobile devices more broadly refer to mobile sensors and other hardware that has been made for relatively easy transportability, including wearable fitness trackers. Mobile devices are particularly relevant to Geographic Information Systems and Technology (GIS&T) in that they house multiple locational sensors that were until recently very expensive and only accessible to highly trained professionals. Now, mobile devices serve an important role in computing platform infrastructure and are key tools for collecting information and disseminating information to, from, and among heterogeneous and spatially dispersed audiences and devices. Due to the miniaturization and the decrease in the cost of computing capabilities, there has been widespread social uptake of mobile devices, making them ubiquitous. Mobile devices are embedded in Geographic Information Science (GIScience) meaning GIScience is increasingly permeating lived experiences and influencing social norms through the use of mobile devices. In this entry, locational sensors are described, with computational considerations specifically for mobile computing. Mobile app development is described in terms of key considerations for native versus cross-platform development. Finally, mobile devices are contextualized within computational infrastructure, addressing backend and frontend considerations.
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.