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| MCA: Career Advancement in Polar Cyberinfrastructure: Permafrost Feature Mapping and Change Detection using Geospatial Artificial Intelligence and Remote Sensing | |
| 项目编号 | 2120943 |
| Wenwen Li | |
| 项目主持机构 | Arizona State University |
| 开始日期 | 2021-09-01 |
| 结束日期 | 08/31/2024 |
| 英文摘要 | This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Polar regions play a vital role in Earth’s climate, ecosystems, and economy. Unfortunately, climate change is driving dramatic changes in the Arctic ecosystem, endangering its natural environment, infrastructure, and lives. Arctic permafrost, ground that remains below 0°C for at least two consecutive summers, is at the center of this change. Covering nearly 1/4 of the land in the northern hemisphere, thawing permafrost is causing a significant local and regional impact in the Arctic. Severe impacts include land subsidence resulting in costly damage to the built environment and increased release of greenhouse gases which further exaggerates the greenhouse effect and global warming. To improve our understanding of permafrost dynamics and its linkages to other Arctic ecosystem components in the midst of rapid Arctic change, it is critically important to have geospatial data readily available that provide high-resolution mapping of permafrost features, their geographical extent, distribution, and change. Although a coarse classification of pan-Arctic permafrost has been developed, fine granularity, local to regional-scale mapping of major permafrost features, is largely unavailable. This data gap inevitably constrains us from gaining a holistic view of the space-time dynamics of permafrost degradation across the Arctic. The goal of this project is to bridge this existing data gap by developing new analytical solutions to support intelligent and automated delineation of permafrost features at scale. Through a partnership with colleagues at Woodwell Climate Research Center, this project will explore novel ways to deepen the integration of cutting-edge AI, geospatial analysis, and cyberinfrastructure into Arctic permafrost research. Specifically, novel GeoAI (Geospatial Artificial Intelligence) solutions will be developed to empower the ongoing efforts of AI-based, high-resolution mapping of pan-Arctic permafrost thaw from Big Imagery. By enabling location-aware and multi-source deep learning and the integration of key spatial principles (i.e., spatial dependency and spatial autocorrelation), the proposed GeoAI model will create polar data products with high veracity and automation, thereby accelerating the scientific navigation of the New Arctic. A joint initiative, “Women in Polar Cyberinfrastructure,” will broaden the participation of women and underrepresented minorities in Arctic AI research. It will also serve as an important avenue for openly sharing knowledge and resources and provide mentorship to early-career scholars in Arctic science, GeoAI, and cyberinfrastructure. All datasets and tools produced in this project will be open-sourced and made available in the NSF Permafrost Discovery Gateway to increase their reuse and inspire further innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. |
| 资助机构 | US-NSF |
| 项目经费 | $359,841.00 |
| 项目类型 | Standard Grant |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/211122 |
| 推荐引用方式 GB/T 7714 | Wenwen Li.MCA: Career Advancement in Polar Cyberinfrastructure: Permafrost Feature Mapping and Change Detection using Geospatial Artificial Intelligence and Remote Sensing.2021. |
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