Publications
Showing 24 results for Author: Daniel S. Adams
Apr, 2026
ORNL Report
Morphology-Based Building Use-Type Modeling: Learnability-First Schema Discovery
Mar, 2026
Conference Paper
At Risk Population Estimates for Belarus, Poland and Slovakia with Machine Learning
High-resolution gridded population modeling is crucial for various applications, including disaster response planning, infectious disease spread modeling, climate change impact estimation, policy development, and more. Multiple gridded population datasets have been developed, each tailored to meet specific objectives. Among them, LandScan Global dataset is designed to repr…
Mar, 2026
Conference Paper
Empirically Categorizing the Built Environment in Relation to Height
Buildings are a core component of the urban environment and affect human populations, energy usage, city development, city planning, and urban heat islands. Buildings span an enormous range of sizes, from a 2m tall shelter to the Burj Khalifa; and at the same time there are widely recognized categories of similar buildings, with homes, office buildings, or skyscrapers as s…
Mar, 2026
Conference Paper
Leveraging Open-Source Satellite-Derived Building Footprints for Height Inference
At a global scale, cities are growing and characterizing the built environment is essential for deeper understanding of human population patterns, urban development, energy usage, climate change impacts, among others. Buildings are a key component of the built environment and significant progress has been made in recent years to scale building footprint extractions from sa…
Mar, 2026
Conference Paper
Detecting Important Drivers of Gridded Population Modeling With Machine Learning
High-resolution population datasets have been lever-aged across a broad swath of domains, such as climate change, public policy, humanitarian aid, and rescue operations, among others. Machine learning methods were adopted to generate high-resolution or gridded population estimates by using various geospatial input features such as buildings, roads, and nighttime lights. In…
Mar, 2026
Conference Paper
Multivariate Testing of Sampling Techniques to Address Class Imbalance in Building Use Type Classification
This study addresses the challenges inherent in building use type classification, particularly focusing on the issue of class imbalance in the training datasets for machine learning classifiers. We comprehensively analyze the efficacy of various class-balancing sampling techniques. Employing Monte Carlo simulations and Bayesian optimization, we evaluated the performance of…
Mar, 2026
Conference Paper
SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics
Building characteristics are often absent in building stock datasets, particularly in regions most vulnerable to climate change and requiring effective disaster management strategies. Traditional machine learning approaches, while widely used to predict building attributes, typically neglect the spatial context of the data, leading to less accurate and reliable outcomes. T…
Mar, 2026
ORNL Report
Satellite Embedding-Based Population Imputation for Areas with Missing Building Footprint Data: A Computer Vision-Based Approach
High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of th…
Jan, 2026
Journal
LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty
Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optim…