The increasingly integrated global economy is boosting the globalization of firms’ technological activities, which also calls on innovators to make new decisions about what, where, and how to expand their technologies on a global scale. The world has experienced an unprecedented internationalization of economic activity during the last three decades. The conclusions provide new information for understanding Chinese patents abroad activities and the motivation of China’s technology globalization and provide evidence from an emerging country for research of the international diffusion of technology innovation. The host country’s intellectual property rights ( IPR) protection level, technology market size and imitation risk have significant positive effects on Chinese SEI patents abroad, while the host country’s high-tech product market size and competition risk have negative effects on Chinese patents abroad. Chinese SEI patents abroad are highly concentrated in the United States, Western Europe, and East Asia, and their influence is gradually spreading from African countries to developed countries. Our results show that the number of Chinese SEI patents abroad is growing rapidly, and the new-generation information technology industry is increasingly dominating, accounting for approximately 50% of all SEI patents abroad. This study uses strategic emerging industries (SEIs) that are important for the future development of the world as examples and constructs a novel dataset of Chinese SEI patents abroad (1993–2017) to explore the spatiotemporal evolution and determinants of the geography of these patents. What is the impact on performance for a multiclass feature extraction challenge-i.e.China’s rapid technological growth and aggressive globalization policies have led to an increasing interest in Chinese patents abroad. How have algorithms that extract buildings and roads improved since SpaceNet was launched, and how can top algorithms from previous challenges be leveraged? SpaceNet 8 aims to answer these questions: Any winning open-source algorithm from SpaceNet 1-7 may also be used. During SpaceNet 8, challenge participants will train algorithms on imagery and labels from previous challenges-as well as newly created labeled training datasets from Maxar-to rapidly map an area affected by flooding. Since its launch in 2016, SpaceNet has made significant progress advancing open-source building footprint and road extraction algorithms. The goal of SpaceNet 8 is to leverage the existing repository of datasets and algorithms from SpaceNet Challenges 1-7 and apply them to a real-world disaster response scenario, expanding to multiclass feature extraction and characterization. ![]() To help address this need, the SpaceNet 8 Flood Detection Challenge will focus on infrastructure and flood mapping related to hurricanes and heavy rains that cause route obstructions and significant damage. As these events become more frequent and severe, there is an increasing need to rapidly develop maps and analyze the scale of destruction to better direct resources and first responders. Each year, natural disasters such as hurricanes, tornadoes, earthquakes and floods significantly damage infrastructure and result in loss of life, property and billions of dollars.
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