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

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，因为他们研究了空间和时间成分，这对于理解塑造过去和现在生态系统的文化过程至关重要。航空光探测和测距遥感技术(激光雷达)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，这既耗时又昂贵，而且产生的结果通常不包括家庭等小型考古特征。该项目通过开发直接分析3D激光雷达数据的新方法来克服这些问题，除了2D图像外，3D激光雷达数据也可以使用。研究小组将开发适用于工业界、学术界和其他领域的变革性方法，就土地利用、土地转化的相互联系以及热带地区在人类-环境动态中对弹性和可持续性的重要性等当前问题提供见解。这项研究对我们在与气候变化、城市化和人口增长相关的多个方面面临的地方、国家和全球挑战具有广泛的影响，这些挑战与日益加剧的社会不平等和环境后果不谋而合。一个跨学科的团队将使用人工智能来开发机器学习方法，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。这些方法将提高对人类对热带生态系统影响的了解，因为它们(A)更全面地记录已建成的环境，从而能够更准确地重建人口和家庭总数，(B)填补构成古代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和社会网络，以及(C)创建更准确的人与环境关系地图。除了考古学，这些方法还将有利于生物学、地质学、地理学、土木工程、建筑学和城市研究，这些研究依赖于对微小空间特征的准确重建。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，并加强总部设在美国的机构、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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.