Probabilistic generative approaches use probability distributions generated from example designs to guide the creation of new ones. Bayesian networks have been used to generate architectural plans from examples (Merrell et al. 2010). Markov chains have been used to generate urban plans (Swahn 2018).

Abstract. Airborne light detection and ranging (LIDAR) systems allow archaeologists to capture 3D data of anthropogenic landscapes with a level of precision that permits the identification of archaeological sites in difficult to reach and inaccessible regions. These benefits have come with a deluge of LIDAR data that requires significant and costly manual labor to interpret and analyze. In order to address this challenge, researchers have explored the use of state-of-the-art automated object recognition algorithms from the field of deep learning with success. This previous research, however, has been limited to the exploration of deep learning processes that work with only 2D data, which excludes the use of available 3D data. Our research addresses this gap and contributes knowledge on the use of deep learning-based processes that can classify archaeological sites from LIDAR generated 3D point cloud datasets. LIDAR data from the UNESCO World Heritage Site of Copan, Honduras is used as the primary dataset to compare the classification accuracy of deep learning models using 2D and 3D data. The results demonstrate that models using 3D point cloud datasets provide the greatest classification accuracy in identifying Maya archaeological sites while requiring less data preparation. Further, the research contributes knowledge on the efficacy of data augmentation strategies when working with small 3D datasets.

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Mental health disorders, such as depression, have been estimated to account for the largest proportion of global disease burden. Existing research has established significant correlations between the built environment and mental health. This research, however, has relied on traditional statistical methods that are not amenable to working with large remote sensing image-based datasets. This research addresses this challenge and contributes new knowledge and a novel method for using generative deep learning for urban analysis and synthesis tasks involving mental health. The research specifically investigates three mental state measures: depression, anxiety, and the perception of safety. The experimental results demonstrate the efficacy of the process—providing a new method to find correlational signals, while providing insights on the correlation between specific urban design features and the incidence of depression.