Recent years have witnessed a growing interest in interactive narrative-based serious games for education and training. A key challenge posed by educational serious games is the balance of fun and learning, so that players are motivated enough to unfold the narrative stories on their own pace while getting sufficient learning materials across. In this chapter, various design strategies that aim to tackle this challenge are presented through the development of Sustain City, an educational serious game system that engages students, particularly prospective and beginning science and engineering students, in a series of engineering design. Besides narrative-learning synthesis, supplementing the player's actions with feedback, and the development of a sufficient guidance system, the chapter also discusses the integration of rigorous assessment and personalized scaffolding. The evaluation of Sustain City deployment confirms the values of the serious games in promoting students' interests and learning in science, technology, engineering, and mathematics (STEM) fields.
With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, remote sensing image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of remote sensing images is still lacking. Specifically, this article discusses the main challenges of remote sensing image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.
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