Collaborative Research: Applying 3D Deep Learning to Site Detection in Tropical Regions

合作研究：将 3D 深度学习应用于热带地区的站点检测

• 批准号: 2213066
• 负责人: Amy Thompson
• 金额: $ 2.66万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213066&HistoricalAwards=false
• 关键词: Collaborative; Research; Applying; 3D; Deep

Researchers at several universities will develop Artificial Intelligence (AI) methods to investigate long-term human impacts on tropical ecosystems. Archaeologists can provide new insights to AI because they study both spatial and temporal components critical for understanding cultural processes that shape past and present ecosystems. The remote sensing technique of airborne light detection and ranging (lidar) captures 3D data that permits researchers to identify previously unknown archaeological features beneath forest canopy and in inaccessible places, generating new data and fundamentally changing the capacity for understanding the spatial aspects of anthropogenic landscapes. However, in tropical regions, researchers face a challenge because they must manually examine the 2D images of lidar data to identify archaeological features, which is time-consuming, expensive, and produces results that commonly exclude small archaeological features, such as households. This project overcomes these issues by developing new methods that directly analyze 3D lidar data that can be used in addition to the 2D images. The research team will develop transformative methods applicable to industry, academia, and beyond providing insights into current issues of the interconnections of landuse, land transformation, and the importance of the tropics in human-environment dynamics for resilience and sustainability. The study has broad implications for the local, national, and global challenges we face on multiple fronts related to climate change, urbanization, and population growth that coincides with increasing social inequality and environmental consequences. An interdisciplinary team will use AI to develop machine learning methods that allow researchers to automatically detect archaeological features of varying sizes as well as anthropogenic landscape modifications in lidar data in relation to topography and vegetation. These methods will enhance understanding of human impacts on tropical ecosystems because they (a) produce more comprehensive documentation of the built environment, allowing for more accurate demographic reconstructions and total household counts, (b) fill in gaps in measurements of the smallest structures that constituted the majority of ancient Maya households allowing for more accurate reconstructions of household and neighborhood inequality and social networks, and (c) create more accurate maps of human-environment relationships. Beyond archaeology, these methods will benefit biology, geology, geography, civil engineering, architecture, and urban studies, which rely on accurate reconstructions of small spatial features. The collaborative focus of the project will also create and enhance educational and training opportunities for students in geospatial techniques and computer science, and strengthen connections between the US-based institutions, international agencies, and indigenous communities.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.

几所大学的研究人员将开发人工智能（AI）方法，以调查人类对热带生态系统的长期影响。考古学家可以为人工智能提供新的见解，因为他们研究的空间和时间成分对于理解塑造过去和现在生态系统的文化过程至关重要。机载光探测和测距（激光雷达）的遥感技术捕获3D数据，使研究人员能够识别森林树冠下和人迹罕至的地方以前未知的考古特征，产生新的数据，并从根本上改变理解人类景观空间方面的能力。然而，在热带地区，研究人员面临着一个挑战，因为他们必须手动检查激光雷达数据的2D图像来识别考古特征，这是耗时，昂贵的，并且产生的结果通常排除了小型考古特征，如家庭。该项目通过开发直接分析3D激光雷达数据的新方法克服了这些问题，这些数据可以在2D图像之外使用。 该研究团队将开发适用于工业界、学术界及其他领域的变革性方法，为当前土地利用、土地改造之间的相互联系以及热带地区在人类环境动态中对复原力和可持续性的重要性等问题提供见解。这项研究对我们在气候变化、城市化和人口增长等多个方面面临的地方、国家和全球挑战具有广泛的影响，这些挑战与日益严重的社会不平等和环境后果相吻合。一个跨学科的团队将使用人工智能开发机器学习方法，使研究人员能够自动检测不同大小的考古特征，以及激光雷达数据中与地形和植被相关的人为景观修改。这些方法将增进人们对人类对热带生态系统影响的了解，因为它们（a）对建筑环境产生更全面的记录，从而能够更准确地重建人口统计和家庭总数，（B）填补了构成古玛雅家庭大多数的最小结构的测量空白，从而能够更准确地重建家庭和邻里不平等以及社交网络，以及（c）绘制更准确的人类-环境关系图。除了考古学之外，这些方法还将有益于生物学、地质学、地理学、土木工程、建筑学和城市研究，这些研究依赖于对小空间特征的精确重建。该项目的合作重点还将为学生创造和加强地理空间技术和计算机科学方面的教育和培训机会，并加强美国机构、国际机构和土著社区之间的联系。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。