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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Data-Driven Approach to Nation-Scale Building Energy Modeling

In 2019, 125 million U.S. residential and commercial buildings consumed $412 billion in energy bills. These buildings currently consume 40% of the nation's primary energy, 73% of electricity, 80% of energy during peak electric grid use, and responsible for 39% of greenhouse gas emissions [14]. Urban-scale building energy modeling has grown significantly in the past decade, allowing individual campuses or communities of buildings to be modeled, simulated, and cost-effective solutions for intelligent management to be identified and implemented. While traditionally limited to individual counties and usually less than 2,000 buildings, the Automatic Building Energy Modeling (AutoBEM) soft-ware suite has been developed to process unconventional, nation-scale data sources to generate unique OpenStudio and EnergyPlus models of each building. Through the use of High Performance Computing (HPC) resources, every U.S. building has been simulated. This paper showcases the data layout, node partitioning, algorithmic approaches, and analytic results that were used to create, share, and analyze 124.4 million U.S. building models.

Berres, Andy↗
DOE OSTI2026

BrickQA: Bridging the Semantic Gap in Building Operations with Dynamic Graph Exploration

While standardized ontologies like the Brick schema address data heterogeneity in Building Automation Systems (BAS), accessing this semantic data remains a challenge as domain experts often lack the expertise to formulate complex SPARQL queries. To bridge this gap, we present BrickQA, a Large Language Model (LLM)-based framework that translates natural language into executable SPARQL queries through structured query decomposition, dynamic schema exploration, and inline validation. BrickQA utilizes an iterative reasoning agent to actively navigate graph topology through dynamic exploration actions without requiring exhaustive context injection or model fine-tuning. This approach effectively mitigates hallucinations, particularly in large-scale building knowledge graphs. Empirical evaluation on BuildingQA, a standardized benchmark, demonstrates that BrickQA significantly outperforms ReAct baselines, delivering a 0.291–0.355 absolute F1 improvement while achieving 3 × –12.7 × higher token cost-efficiency. Beyond these metrics, the framework maintains structural fidelity across heterogeneous buildings and remains resilient to ambiguous queries without requiring site-specific fine-tuning. Furthermore, a case study on operational analytics validates the framework’s capability to handle temporal and aggregation constraints, effectively transforming abstract semantic models into actionable facility management insights.1

Ko, Yun-Dam↗
DOE OSTI2020

Data-driven evaluation of HVAC operation and savings in commercial buildings

Commercial buildings consumed 36% of electricity, or 1.35 trillion kWh, in the United States in 2017, and almost 30% of this energy was wasted. Much of this loss can be attributed to inefficient heating ventilation and air con­ditioning (HVAC) systems. By improving the operational conditions of HVAC, significant savings can be achieved. However, most buildings and building equipment do not use costly sub-meters to monitor and address performance issues, and on-site auditing can be expensive and insufficient. Alternatively in this study, we propose a data-driven method to identify savings opportunities using only whole building meter data and without setting foot in the building. For this purpose, we introduced two algorithms that virtually quantify the value of a thermostat setpoint setback and HVAC rescheduling. Additionally, we developed novel methods for detecting occupancy patterns and quantifying the baseload of the HVAC operation. Using a clustering algorithm, we identified those buildings for which HVAC savings was significant and further categorized the buildings based on their potential for savings. A population study of over 432 commercial buildings demonstrated a median percentage energy savings of 1.6% from a baseload reduction and 2.1% from HVAC rescheduling. Additionally, results indicate that retail buildings have the highest potential for savings among the building types studied.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

High-Level Model Articulation with BuildingSync and OpenStudio

The use of the BuildingSync schema for describing the contents of buildings is becoming more common with its recent integration into the Audit Template tool as well as ASHRAE’s Building Energy Quotient (bEQ) web portal. Although BuildingSync was initially created to store and transfer data related to building energy audits (as defined by ASHRAE Standard 211), it has since been expanded to store the data needed to articulate fully defined physics-based building energy models. BuildingSync combined with abstracted high-level input methods defined in OpenStudio’s Standards project and the newly developed BuildingSync gem allows BuildingSync eXtensible Markup Language (XML) (Bray, Paoli, Sperberg-McQueen, Maler, Eve (Sun Microsystems, & Francois, 2008) document to be converted into OpenStudio models. Automatic generation of a building energy model from data collected during an audit 1) eliminates the need to separately generate an energy model, 2) improves consistency between model generation, and 3) simplifies evaluation of various energy efficiency measures. This manuscript will discuss this open-source project, including: development of the validation infrastructure necessary to provide formalized expectations of informational requirements for BuildingSync documents; development of the BuildingSync gem for translation of BuildingSync documents to OpenStudio models as well as example implementations; and finally, the manuscript will elaborate on the advantages and disadvantages of using high-level models generated from BuildingSync as surrogates to detailed models.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

A Probabilistic Approach to Load Modeling for Central HVAC Systems in Large Commercial Buildings for Retrofit Decisions Under Uncertainty

Retrofitting central HVAC systems in large commercial buildings with advanced technologies like heat recovery chillers (HRCs) offers a significant opportunity to enhance energy efficiency. However, analyzing these retrofits is challenging with traditional whole-building simulation tools, which require intensive calibration and struggle to model innovative system configurations and controls. To overcome these limitations, this study proposes a load profilebased retrofit analysis framework that provides better decisions under uncertainty. The main focus of this paper is the development of a probabilistic load profile model that can be used in the framework by using exploratory data analysis (EDA) of measured building data to properly quantify its inherent variability. A non-parametric Gaussian Process (GP) model was employed to capture the time- and weather-dependent characteristics of the heating load while explicitly modeling its uncertainty. The model's effectiveness is demonstrated through strong predictive performance on unseen data and physically interpretable insights into load behavior. This data-driven, probabilistic load profile serves as a robust and flexible input for subsequent system simulations, enabling a more confident and statistically sound analysis of retrofit potential.

Ham, S W↗
NASA NTRSDate not supplied

NASA Power: Global Solar Insolation, Meteorological Parameter Data, and Web Services to Support Sustainable Building Design and Operations

The buildings industry is currently striving to adopt green solutions to make infrastructure more energy-efficient in order to meet the 2050 net-zero climate goals. This planning requires reliable environmental datasets that are crucial in designing, building, and maintaining our world’s-built environment, as well as other energy-related processes and investments. This webinar for the National Institute of Building Sciences provides an overview of NASA’s Prediction Of Worldwide Energy Resources (POWER) Project that informs decision-making and development for sustainable building design and operations by enabling public open discovery, efficient access, and convenient distribution of NASA’s Earth Observations and global atmospheric model datasets. POWER’s datastore is comprised of solar radiation and surface meteorology parameters, spanning nearly 40 years of hourly data, that are easily accessible via several access methods and tools to support three focus areas: 1) renewable energy deployment and management, 2) sustainable infrastructure, and 3) agroclimatology applications. POWER and NASA Earth Science both plan future data parameters, updated tools, and improved observations that could directly support U.S. and international sustainable development goals, climate strategies, and building information modeling. To this end, solar data from several NASA projects and meteorological data from NASA assimilation models have already been reformatted and disseminated to the public via a user-friendly web GIS-enabled based data portal through the POWER Project. POWER data is analysis-ready and accessible through an Application Programming Interface (API), ArcGIS Image Services, and the project’s Data Access Viewer enhanced (DAVe), an interactive online tool. The POWER DAVe also features data consistent with ASHRAE Climate Design Conditions and has developed web image services showing Building Climate Zones and their variability. Through those tools, the data can be downloaded into multiple formats that support the infrastructure community, including CSV and Energy Plus Weather (EPW). POWER’s entire data product catalog is available through Amazon Web Services (AWS) Open Data Registry (ODR) via a free and publicly accessible Simple Storage Service (S3). This webinar provides a full overview of the NASA POWER Project's data and services developed in collaboration with the sustainable infrastructure community. Examples of how the renewable energy and building communities have utilized POWER data products to make decisions and a preview of future data product expansion, including climate projections, and web services will also be provided. Additionally, use case stories from our broad community of users will be presented.

Paul W. Stackhouse↗
DOE OSTI2023

On data-driven energy flexibility quantification: A framework and case study

Building energy flexibility is an important resource for a sustainable and resilient power grid, and an important measure to reduce utility costs for building owners. Quantifying energy flexibility for existing buildings can provide critical insights in optimizing their operation. Data-driven methods for building energy modeling and analytics are gaining popularity due to the increasingly available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. However, their application in quantifying the energy flexibility of real buildings is still limited due to the heterogeneous data types and limited data availability. Here, this study proposes a framework for building-level data-driven energy flexibility quantification that considers different levels of data availability and use cases. Two case studies with real building data collected at different scales were conducted to demonstrate the proposed framework for different purposes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2024 Buildings Technology Baseline: Dataset Documentation

The Buildings Technology Baseline is a curated and regularly updated dataset of current and projected performance, retail, and installed price data for all major building energy technologies needed to enable cost/benefit analyses. Building technology analyses require an up-to-date understanding of installation costs and cost-effectiveness of key building energy efficiency technologies. The dataset was assembled by Guidehouse during fiscal year 2024. Data was gathered from the 2024 National Residential Efficiency Measures Database (NREMDB), the 2023 Energy Information Administration Updated Buildings Sector Appliance and Equipment Costs and Efficiencies ("EIA Building Data Report"), DOE Lighting Market Model, the 2023 RSMeans database, and the 2020 Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics ("GEB Data Report"), Lawrence Berkeley National Laboratory, various literature, as well as new data from online retailers, stakeholder interviews, and contractor databases in 2023 and 2024. The dataset has been reviewed by subject matter experts at NREL and DOE. The 2024 dataset release is intended to be a starting point for interested users to provide feedback. This database is not intended to provide specific cost estimates for a specific project. The cost estimates do not include any rebates or tax incentives that may be available for the measures. Rather, it is meant to help determine which measures may be more cost-effective. The National Renewable Energy Laboratory (NREL) makes every effort to ensure accuracy of the data; however, NREL does not assume any legal liability or responsibility for the accuracy or completeness of the information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2014

Use of Semi-Autonomous Tools for ISS Commanding and Monitoring

As the International Space Station (ISS) has moved into a utilization phase, operations have shifted to become more ground-based with fewer mission control personnel monitoring and commanding multiple ISS systems. This shift to fewer people monitoring more systems has prompted use of semi-autonomous console tools in the ISS Mission Control Center (MCC) to help flight controllers command and monitor the ISS. These console tools perform routine operational procedures while keeping the human operator "in the loop" to monitor and intervene when off-nominal events arise. Two such tools, the Pre-positioned Load (PPL) Loader and Automatic Operators Recorder Manager (AutoORM), are used by the ISS Communications RF Onboard Networks Utilization Specialist (CRONUS) flight control position. CRONUS is responsible for simultaneously commanding and monitoring the ISS Command & Data Handling (C&DH) and Communications and Tracking (C&T) systems. PPL Loader is used to uplink small pieces of frequently changed software data tables, called PPLs, to ISS computers to support different ISS operations. In order to uplink a PPL, a data load command must be built that contains multiple user-input fields. Next, a multiple step commanding and verification procedure must be performed to enable an onboard computer for software uplink, uplink the PPL, verify the PPL has incorporated correctly, and disable the computer for software uplink. PPL Loader provides different levels of automation in both building and uplinking these commands. In its manual mode, PPL Loader automatically builds the PPL data load commands but allows the flight controller to verify and save the commands for future uplink. In its auto mode, PPL Loader automatically builds the PPL data load commands for flight controller verification, but automatically performs the PPL uplink procedure by sending commands and performing verification checks while notifying CRONUS of procedure step completion. If an off-nominal condition occurs during procedure execution, PPL Loader notifies CRONUS through popup messages, allowing CRONUS to examine the situation and choose an option of how PPL loader should proceed with the procedure. The use of PPL Loader to perform frequent, routine PPL uplinks offloads CRONUS to better monitor two ISS systems. It also reduces procedure performance time and decreases risk of command errors. AutoORM identifies ISS communication outage periods and builds commands to lock, playback, and unlock ISS Operations Recorder files. Operation Recorder files are circular buffer files of continually recorded ISS telemetry data. Sections of these files can be locked from further writing, be played back to capture telemetry data that occurred during an ISS loss of signal (LOS) period, and then be unlocked for future recording use. Downlinked Operation Recorder files are used by mission support teams for data analysis, especially if failures occur during LOS. The commands to lock, playback, and unlock Operations Recorder files are encompassed in three different operational procedures and contain multiple user-input fields. AutoORM provides different levels of automation for building and uplinking the commands to lock, playback, and unlock Operations Recorder files. In its automatic mode, AutoORM automatically detects ISS LOS periods, then generates and uplinks the commands to lock, playback, and unlock Operations Recorder files when MCC regains signal with ISS. AutoORM also features semi-autonomous and manual modes which integrate CRONUS more into the command verification and uplink process. AutoORMs ability to automatically detect ISS LOS periods and build the necessary commands to preserve, playback, and release recorded telemetry data greatly offloads CRONUS to perform more high-level cognitive tasks, such as mission planning and anomaly troubleshooting. Additionally, since Operations Recorder commands contain numerical time input fields which are tedious for a human to manually build, AutoORM's ability to automatically build commands reduces operational command errors. PPL Loader and AutoORM demonstrate principles of semi-autonomous operational tools that will benefit future space mission operations. Both tools employ different levels of automation to perform simple and routine procedures, thereby offloading human operators to perform higher-level cognitive tasks. Because both tools provide procedure execution status and highlight off-nominal indications, the flight controller is able to intervene during procedure execution if needed. Semi-autonomous tools and systems that can perform routine procedures, yet keep human operators informed of execution, will be essential in future long-duration missions where the onboard crew will be solely responsible for spacecraft monitoring and control.

Brzezinski, Amy S.↗
DOE OSTI2022

Case Study: NREL Campus Chilled Water Storage Potential: Benchmark Datasets Development and Applications, Task 4 - Use Case Demonstration

The Benchmark Datasets Development and Applications project is a three-year collaboration between the National Renewable Energy Laboratory (NREL), Oak Ridge National Laboratory, Pacific Northwest National Laboratory, and Lawrence Berkeley National Laboratory. The project seeks to collect and curate high-resolution, well-calibrated time series of building operational and indoor/outdoor environmental data, which are crucial to understanding and optimizing building energy efficiency performance and demand flexibility capabilities as well as benchmarking energy algorithms. Project outcomes include approximately twelve high-fidelity building datasets, enhanced data representation tools, and four case studies to illustrate example applications. The goal of these case studies is to define and execute analyses that demonstrate how one or more datasets collected through this project can address a data gap or challenge historically faced by building stakeholders. This technical paper summarizes the findings of one of these case studies, in which we studied the operational efficiencies of the central cooling system at NREL. We looked at three years of data from the three chillers in the Field Test Laboratory Building (FTLB), from 2019 to 2021, to compare equipment operation and demand throughout the time period. Our analysis indicates that all three chillers are operating at or below the optimal loading conditions for most of the operation time, and thus there was no efficiency drop due to loading of the chillers at full capacity. Our recommendation is that no chiller capacity increase is needed; instead, the central plant could benefit from adopting advanced control logics for optimal sequencing of chillers during part load operations. Analysis of adding chilled water thermal storage to the central plant indicated 34% savings in demand cost and 24.5% savings in total cost (energy consumption and demand charge cost). The payback period is estimated to be 11-22 years with an assumed TES cost of $\$$100-$200 per ton. This case study shows how a selected dataset is used to solve a practical building problem - learning the operational status of its components, analyzing the effectiveness of a proposed new technique, and aiding decision-making for the building operations and maintenance team.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Clustering-Based Predictive Analytics to Improve Scientific Data Discovery

Given the sheer volume of scientific data archived within the data-intensive projects at the US Department of Energy's Oak Ridge National Laboratory, finding precisely what data we are looking for may not be a trivial task; conversely, we may also miss a more prominent data product. To address such issues, we propose improving the data discovery system and using data analytics methods to comprehend what specific users might be interested in based on their physiological state, search patterns, and past data usage history. This work's primary goal is to prune the complexity, increase the visibility of popular data products, and direct users toward the data that best meet their needs. The proposed algorithm constructs a user profile based on the user's explicit or implicit interactions with the system, such as items they are currently looking at on-site and the key metadata mappings related to the data set. The pattern is then used to build a training data set, which will help find relevant data to recommend to the user.

Devarakonda, Ranjeet↗
DOE OSTI2020

AI for Science: Report on the Department of Energy (DOE) Town Halls on Artificial Intelligence (AI) for Science

The report documents the DOE Town Halls held during 2019 at Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, and in Washington, DC. From July to October 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four town hall meetings attended by more than 1,000 U.S. scientists and engineers. The goal of the town hall series was to examine scientific opportunities in the areas of artificial intelligence (AI), Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these opportunities. In this report and in the Department of Energy (DOE) laboratory community, we use the term “AI for Science” to broadly represent the next generation of methods and scientific opportunities in computing, including the development and application of AI methods (e.g., machine learning, deep learning, statistical methods, data analytics, automated control, and related areas) to build models from data and to use these models alone or in conjunction with simulation and scalable computing to advance scientific research. The AI for Science town hall discussions focused on capturing the transformational uses of AI that employ HPC and/or data analysis, leveraging data sets from HPC simulations or instruments and user facilities, and addressing scientific challenges unique to DOE user facilities and the agency’s wide-ranging fundamental and applied science enterprise.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Case-based reasoning: The marriage of knowledge base and data base

The coupling of data and knowledge has a synergistic effect when building an intelligent data base. The goal is to integrate the data and knowledge almost to the point of indistinguishability, permitting them to be used interchangeably. Examples given in this paper suggest that Case-Based Reasoning is a more integrated way to link data and knowledge than pure rule-based reasoning.

Pulaski, Kirt↗
NASA NTRS2002

ISAIA: Interoperable Systems for Archival Information Access

The ISAIA project was originally proposed in 1999 as a successor to the informal AstroBrowse project. AstroBrowse, which provided a data location service for astronomical archives and catalogs, was a first step toward data system integration and interoperability. The goals of ISAIA were ambitious: '...To develop an interdisciplinary data location and integration service for space science. Building upon existing data services and communications protocols, this service will allow users to transparently query hundreds or thousands of WWW-based resources (catalogs, data, computational resources, bibliographic references, etc.) from a single interface. The service will collect responses from various resources and integrate them in a seamless fashion for display and manipulation by the user.' Funding was approved only for a one-year pilot study, a decision that in retrospect was wise given the rapid changes in information technology in the past few years and the emergence of the Virtual Observatory initiatives in the US and worldwide. Indeed, the ISAIA pilot study was influential in shaping the science goals, system design, metadata standards, and technology choices for the virtual observatory. The ISAIA pilot project also helped to cement working relationships among the NASA data centers, US ground-based observatories, and international data centers. The ISAIA project was formed as a collaborative effort between thirteen institutions that provided data to astronomers, space physicists, and planetary scientists. Among the fruits we ultimately hoped would come from this project would be a central site on the Web that any space scientist could use to efficiently locate existing data relevant to a particular scientific question. Furthermore, we hoped that the needed technology would be general enough to allow smaller, more-focused community within space science could use the same technologies and standards to provide more specialized services. A major challenge to searching for data across a broad community is that information that describe some data products are either not relevant to other data or not applicable in the same way. Some previous metadata standard development efforts (e.g., in the earth science and library communities) have produced standards that are very large and difficult to support. To address this problem, we studied how a standard may be divided into separable pieces. Data providers that wish to participate in interoperable searches can support only those parts of the standard that are relevant to them. We prototyped a top-level metadata standard that was small and applicable to all space science data.

Hanisch, Robert J.↗
DOE OSTI2023

A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management. Without accessibility to other data sources, the onsite observed temperatures or the airport temperatures are used in forecast models. In this paper, we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations (PWS) to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework. The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs. Our approach is distinguished from existing literature in various aspects. First, we leverage the crowdsourcing weather data from PWS in addition to public data sources. In this way, the data is at much finer time resolution (e.g., at 5-minute frequency) and spatial resolution (e.g., arbitrary location vs grid). Second, our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance. Here, we demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio, Texas, USA. In general, the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50% compares with persistent model and has 90% chance to outperform airport forecast in short-term forecasting. In a real-time setting, the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon. Moreover, we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance. Finally, we implement our model in other cities such as Syracuse and Chicago to test the model's performance in different landforms and climate types.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Data system interprobability

Software was developed and tested which allows an end-user to build a book data object in an interactive fashion. The electronic book model was developed for the Library front-end of the DAVID (Distributed Access View Integrated Database) system. It is a complex data object which is capable of holding data of different types and formats. After the task, the plan was to assess the Book model and investigate the possibility of it serving as a knowledge base for a expert system front-end to the DAVID system. For the rest of the fellowship period, data system interoperability was examined. Specifically, a plan was prepared titled Implementations of the Land Analysis System (LAS) to Meet UNEP/GRID Requirements. This plan is aimed at reaching the goal of building one virtual global change information system which will link existing and future data systems allowing earth and space scientists to learn about, locate, access, and correlate data of interest regardless of its physical location. The plan proposed the construction of a master directory (MD) for the European data systems that is equivalent in structure to NASA's MD. The MD is to contain brief, high-level information about existing Earth and space science data sets sufficient for a user to know where data of interest reside and how to go about getting additional information. The plan also outlined the tasks that would be involved to electronically connect the MD system to the various discipline data systems (e.g., LAS) in order to achieve general interroperabilty, that is, the abilty to perform cross-systems searches and exchange of and access to distributed information.

Wakim, Nagi T.↗
DOE OSTI2025

LandScan Mosaic

The LandScan program at Oak Ridge National Laboratory (ORNL), in collaboration with the National Geospatial-Intelligence Agency (NGA), continues to deliver the most accurate and up to date global, high resolution gridded population data. Additionally, the latest advancements in the LandScan HD methodology led to reduced latency in development of rapid updates for geopolitical events. With momentum towards reporting more up to date population estimates, feedback from the user community expressed interest in reporting population estimates in ranges - whether to express a level of uncertainty or confirm to leadership and stakeholders the modeled data are estimates. Building upon the need to understand uncertainty or confidence in the modeled data and report ranges at the global scale, LandScan Mosaic was developed. LandScan Mosaic represents the next generation of high-resolution population modeling, building upon the established success of previous LandScan HD iterations. While LandScan HD employed a deterministic big data fusion approach, LandScan Mosaic enhances this methodology by integrating advanced machine learning techniques to impute missing, yet crucial, population model parameters. This advancement allows for probabilistic modeling of building occupancy and population distribution, incorporating uncertainty quantification through Monte Carlo sampling methods. By combining big data fusion with machine learning-driven imputation and stochastic modeling, LandScan Mosaic provides a more comprehensive and robust representation of population dynamics. LandScan Mosaic will be following the in the footsteps of its longstanding counterpart LandScan Global and releasing a global gridded population raster, at the 3-arcsecond resolution. This technical report documents the current stage of development of LandScan Mosaic, detailing the methodologies and data sources behind the modeling. Stakeholders are encouraged to use this document as an authoritative reference for insight into Mosaic’s data development processes. However, readers should note that LandScan Mosaic remains in a late-stage research and development phase, and methodologies and data presented here are subject to refinements ahead of the anticipated global release in Summer 2025. Feedback and inquiries from users and stakeholders are welcomed as we continue to refine and enhance this important population resource.

97 MATHEMATICS AND COMPUTING↗