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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Building-Level Comparison of Microsoft and Google Open Building Footprints Datasets

Large-scale datasets of building footprints are a crucial source of information for a variety of efforts. In 2023, the general public benefits from open access to multiple sources of building footprints at the country scale or larger, such as those produced by Microsoft and Google. However, none of the available datasets have attained complete global coverage, and researchers and analysts may need to combine multiple sources to assemble a complete set of building footprints for their area of interest or choose between overlapping sources, requiring an understanding of the differences between different building sources. This paper presents a method to closely examine the quality of different building footprint sources by matching corresponding buildings across datasets, using building footprints in Ethiopia published by Microsoft and Google as an example set.

Gonzales, Jack↗
DOE OSTI2026

Improving Building Footprint Extraction Using NAIP and 3DEP Lidar Derived Features with Deep Learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Liu, Jung Kuan [United States Geological Survey (U↗
DOE OSTI2025

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 satellite datum and other remotely sensed products. Billions of building footprints have recently been released by companies such as Microsoft and Google at a global scale. However, research has shown that depending on the methods leveraged to produce a footprint dataset, discrepancies can arise in both the number and shape of footprints produced. Therefore, each footprint dataset should be examined and used on a case-by-case study. In this work, we find through two experiments on Oak Ridge National Laboratory and Microsoft footprints within the same geographic extent that our approach of inferring height from footprint morphology features is source agnostic. Regardless of the differences associated with the methods used to produce a building footprint dataset, our approach of inferring height was able to overcome these discrepancies between the products and generalize, as evidenced by 98% of our results being within 3m of the ground-truthed height. This signifies that our approach can be applied to the billions of open-source footprints which are freely available to infer height, a key building metric. This work impacts the broader domain of urban science in which building height is a key, and limiting factor.

Stipek, Clinton [ORNL] (ORCID:0000000280501096)↗
DOE OSTI2021

Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints

Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

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 the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Deep Reinforcement Learning Based HVAC Control for Reducing Carbon Footprint of Buildings

In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of 23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

carbon emission↗
DOE OSTI2025

Projected Urban Morphology of the Los Angeles Area by the Year 2100

This dataset provides projections of urban building morphologies for the Los Angeles urban area at 30-meter spatial resolution. It contains 192 raster files that detail two primary building attributes: building footprint fractions (ranging from 0 to 1) and average building heights (ranging from 0 to 75 meters). The projections account for a wide range of future pathways, covering two Shared Socioeconomic Pathway (SSP) scenarios (SSP3 and SSP5), two population scenarios, two developed land intensification scenarios, and four distinct levels of intensification. The dataset was created using dual Generative Adversarial Networks (GANs) trained on 2015 land cover and building properties from the National Land Cover Database (NLCD) and Model America datasets. Supporting information on the dataset has been described in the LAUrbanAreaMorphologyProjections2100_README.txt file.

Pandey, Bhartendu↗
DOE OSTI2019

Research Technology Laboratory (RTL) Disposition Program Final Status Survey Report

As part of the Battelle Facility Restoration Program (Smoter and Biebesheimer 2017), the Research Technology Laboratory (RTL) Complex was remediated through demolition and surveyed to show it meets the radiological release criteria for unrestricted use under the clearance process (DOE 2011). The restoration program has directed the implementation of the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) methodology (NRC 2011) to verify the suitability of the site to be released for unrestricted use under the clearance process in U.S. Department of Energy (DOE) Order 458.1 (Biebesheimer 2018a; DOE 2011). The use of MARSSIM is accepted by federal and state agencies, including DOE. This report documents how the MARSSIM process was used to assure any residual radioactivity is within acceptable levels and as low as reasonably achievable. Past land use for the RTL Complex property included agricultural, residential, and industrial activity. The historical site assessment (HSA) (Lindenmeier et al. 2015; Biebesheimer 2018b) identified use of radionuclides in past RTL Complex research and development activities from commissioning in 1966 until it was vacated in 2017. Based on the information provided in the HSA, the RTL Complex was delineated into six survey units. The 520 building footprint, tank vault and 530 building footprint, other building footprints, and pipeline survey units included excavated areas. The paved areas and open areas survey units were unexcavated areas. Survey units were all determined to be Class 3, with low probability of containing residual radioactivity, after demolition of the structures within the RTL Complex. The Research Technology Laboratory (RTL) Disposition Program: Final Status Survey Plan (Bunn et al. 2018) was designed and conducted to provide quantitative data regarding the residual radioactivity present in each survey unit. Authorized radiological release limits were developed and approved by the DOE Office of Science in December 2016 (DOE 2017). Fourteen random sample locations were identified within each survey unit. Forty-four specific judgmental sample locations were selected across the site based on information in the HSA. The number of sample locations within a survey unit ranged from 15 to 28, for a total of 127 random and judgmental sample locations identified across the RTL Complex. Sampling for the final status survey occurred over 12 days between November 11 and December 7, 2018. Laboratory results were independently validated by Analytical Quality Associates prior to evaluation. All of the over 880 residual radioactivity measurements were less than the release limits and action levels established through the RTL Disposition Program. A sum of fractions calculation for each survey unit resulted in values that were less than unity. Based on the results presented in this report, the restoration program recommends that the RTL Complex be released for unrestricted use.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Areal interpolation of population projections consistent with different SSPs from 1-km resolution to block level based on USA Structures dataset

Population data are normally collected at various census administrative levels, and areal interpolation of population is often required to transform population to the desired spatial resolution. Building footprint datasets, such as Microsoft building footprints, have proven to be useful in estimating population distribution and can therefore be used for areal interpolation of population. In addition to Microsoft building footprints, the recently released USA Structures dataset provides additional information such as building type and building height for some regions, which may provide valuable information for a better depiction of population distribution and improved population areal interpolation accuracy. In this study, we have conducted areal interpolation of population projections consistent with three different Shared Socioeconomic Pathways (SSP2, SSP3, and SSP5) from 1-km grid cells to block level in Washington state for every ten years from 2020 to 2040 based on USA Structures. We assessed USA Structures-based population downscaling accuracy using U.S. decennial survey data in 2020 under three different downscaling schemes, including population downscaling from census tracts to block groups, from census tracts to blocks, and from block groups to blocks. The resulting accuracies were compared with those based on Microsoft building footprints. The comparison showed that USA Structures achieved higher accuracies across different population density regions and areas with different urbanization extent within our study area.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology

Building occupancy classification plays a crucial role in urban planning, disaster management, and population modeling. Traditional methods often require extensive field surveys or detailed datasets, which can be time-consuming, expensive, and may yield incomplete or erroneous data. In this paper, we present a novel approach for classifying buildings as residential or non-residential using only building footprint data. By extracting geometric shape derivatives that characterize building morphology, we developed a high-accuracy classification model employing a combination of unsupervised and supervised learning methods. We utilized open-source data from Open Street Map, aggregating it to create binary labels for buildings based on their respective human use type. Our approach demonstrates the potential for scalability without the need for additional data sources other than building footprints and labels, offering a more efficient solution for building occupancy classification.

Adams, Daniel↗
DOE OSTI2021

Aerial 3D Building Reconstruction from Drone Imagery (A3DBR) v1

This toolkit is composed of several modules for extracting buildings geometrical and thermal characteristics from RGB and thermal imagery captured using a drone. - Building 3D reconstruction module: leverage a photogrammetry software to construct a 3D point cloud from RGB drone imagery, which is then used in conjunction with image processing and geometric methods to extract building footprint and building height (i.e., 3D model of the building). - Windows to wall ratio estimation module: leverage deep learning semantic segmentation modeling to detect windows on 2D drone RGB images. The detected windows are then projected onto the extracted building 3D model (using building 3D reconstruction module) and their area is computed to obtain window to wall ratio estimation. - Thermal anomalies detection module: leverage image processing and machine learning algorithm to detect on 2D drone thermal images potential thermal anomalies within building's facades and roofs.

Granderson, Jessica↗
DOE OSTI2024

Efficient Extraction Of Building Elevation Attributes For Flood Risk Management Using Airborne LiDAR Data

In this paper, we address the need for extracting two key building elevation attributes—Lowest Adjacent Grade (LAG) and Highest Adjacent Grade (HAG)—which are crucial for effective flood risk management. Conventional methods, involving onsite surveying or the use of optical imagery-derived building footprints combined with Digital Elevation Models (DEMs), often face misalignment and time discrepancy issues due to varied remote sensing sources. We introduce a new, scalable method that exclusively relies on airborne LiDAR data to overcome these challenges. Our approach employs an object-based ground filtering technique, and the results were evaluated using two different DEMs and building footprint sets. The findings demonstrate that our single-source method, utilizing only airborne LiDAR data, significantly improves the accuracy of LAG and HAG calculations compared to traditional methods that use hand-digitized building footprints. The proposed approach offers a solution for comprehensive flood risk management endeavors.

Song, Hunsoo↗
DOE OSTI2018

Developing a landscape of urban building energy use with improved spatiotemporal representations in a cool-humid climate

Urban buildings account for up to 75% of total energy use in the United States (U.S.). Understanding urban building energy use is important for developing feasible options to mitigate energy use and greenhouse gas emissions. In this study, an improved bottom-up building energy use model, named City Building Energy Use Model (City-BEUM), was developed to estimate building energy use for all buildings in Polk County, Iowa. First, 28 commercial and 6 residential building prototypes were designed by combing Assessor's parcel data and building footprint data. Then, the EnergyPlus in the CityBEUM was calibrated for all building prototypes using the U.S. Energy Information Administration's survey data, monthly utility meter data, and actual weather data. Finally, spatial and temporal variations of building energy use in the study area were estimated using the CityBEUM. Results indicate that the spatial variation of building energy use in the study area can be captured using the CityBEUM. With the monthly-calibrated model, the temporal pattern of urban building energy use can be well represented. The comparison of building energy use using the Typical Meteorological Year and actual weather data demonstrates the importance of using actual weather data in building energy modeling for an improved temporal representation.

CityBEUM, Urban building energy use, High spatiote↗
DOE OSTI2022

Scaling Building Energy Audits through Machine Learning Methods on Novel Drone Image Data

Building energy audits are time-consuming and labor-intensive. This paper describes a new method using machine learning (ML) techniques on novel data sources (drone images) to improve the identification of building characteristics and retrofit opportunities, and thereby reduce the effort for audits. The new ML method includes: (1) Building footprint extraction using line extraction, polygonization, and polygon-merging, (2) Building envelope extraction using PIX4d modeling software to reconstruct a building 3D model, (3) Visualization tool for viewing images from the 3D model, (4) Window-to-wall ratio (WWR) using state-of-art deep neural network semantic segmentation, (5) Envelope thermal anomaly detection using an unsupervised machine learning clustering algorithm, and (6) Rooftop energy equipment detection based on an object detection algorithm. The testing of this method involved a comparison of additional ML-generated information overlaid on current ‘state-of-practice’ audit and remote assessment baselines using evaluation metrics: labor time and associated cost, marginal benefits of using ML-generated information in workflows for audits and remote assessments, integration potential with existing processes and tools, and replicability/scalability of the method. In two test buildings in California that had comprehensive drawings and meter data available, the ML method effectively generated a building footprint, envelope, rooftop equipment, WWR, and locations of envelope thermal anomalies. Projected target segments of the ML method are sites with minimal drawings and energy data, and underserved sectors such as multistoried housing, disadvantaged communities, and schools for which the ML method can enable identification of building asset characteristics and prioritization of envelope retrofits and decentralized energy equipment retrofits.

Singh, Reshma↗
DOE OSTI2018

Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States

Establishing up-to-date large scale building maps is essential to understand the urban dynamics, such as estimating population, urban planning, and many other applications. Although many computer vision tasks have been successfully carried out with deep convolutional neural networks, there is a growing need to understand their large scale impact on building mapping with remote sensing imagery. In this paper, taking advantage of the scalability of convolutional neural networks (CNNs) and using only few areas with the abundance of building footprints, for the first time we conduct a comparative analysis of four state-of-the-art CNNs for extracting building footprints across the entire continental United States. The four CNN architectures namely: Branch-out CNN, fully convolutional network (FCN), conditional random field as recurrent neural network (CRFasRNN), and SegNet, support semantic pixelwise labeling and focus on capturing textural information at multiscale. We use 1-meter resolution aerial images from National Agriculture Imagery Program as the test-bed, and compare the extraction results across the four methods. In addition, we propose to combine signed-distance labels with SegNet, the preferred CNN architecture identified by our extensive evaluations, to advance building extraction results to instance level. We further demonstrate the usefulness of fusing additional near IR information into the building extraction framework. Large scale experimental evaluations are conducted and reported using metrics that include: Precision, recall rate, intersection over union, and the number of buildings extracted. With the improved CNN model and no requirement of further postprocessing, we have generated building maps for the United States with an average processing time less than one minute for an area of size &#x223C;<!-- ~ --> 56 km 2 . Finally, the quality of extracted buildings and processing time demonstrated that the proposed CNN based framework fits the need of building extraction at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Unique Building Identifier Generator

We developed a grid reference system-based Unique Building Identifier (UBID). The method converts a building footprint on a map to a unique code. Unlike geographic coordinates (latitude and longitude), which require high precision and can only locate points on the earth, the UBID code records two-dimensional information about a building footprint in a concise and practical manner. The UBID itself is neither a database nor a data schema. Rather, it acts as an external common key between databases to facilitate data mapping. The developed ruleset ensures that individual implementers can reach the same conclusion utilizing publicly available digital maps. It can also automatically resolve discrepancies, caused by physical space or structure changes over time, during data exchange. The method is also flexible and scalable to identify a portion of a building or a group of buildings.

Vlachokostas, EfthimiosAlex↗