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Review

Indoor Mapping as a Spatiotemporal Framework for Mitigating Greenhouse Gas Emissions in Buildings: A Review

by
Vinuri Nilanika Goonetilleke
,
Muditha K. Heenkenda
* and
Kamil Zaniewski
Department of Geography and the Environment, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada
*
Author to whom correspondence should be addressed.
Geomatics 2026, 6(2), 27; https://doi.org/10.3390/geomatics6020027
Submission received: 10 January 2026 / Revised: 6 March 2026 / Accepted: 17 March 2026 / Published: 19 March 2026
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Abstract

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. This review examined the role of indoor mapping in understanding, modelling, and reducing GHG emissions in buildings. It synthesized current advancements in indoor spatial data acquisition, ranging from Light Detection And Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM) to deep learning-based floor plan extraction, and evaluated their contribution to improved indoor environmental analysis. The review highlighted emerging techniques, challenges, and gaps, particularly the limited integration of physical indoor spaces with virtual layers representing assets, occupants, and equipment. Addressing this gap requires embedding spatial modelling as an intermediate analytical layer that structures and contextualizes sensor data to support spatiotemporal decision-making. Overall, this review demonstrated that indoor mapping plays a critical role in transforming spatial information into actionable insights, enabling more accurate energy modelling, enhanced real-time building management, and stronger data-driven strategies for GHG mitigation in the built environment.

Graphical Abstract

1. Introduction

Climate change refers to the long-term warming of the atmosphere, oceans, and land that leads to altered weather patterns, glacial melting, and sea level rise, affecting biodiversity, ecosystems, and human health [1]. These changes are primarily driven by anthropogenic actions such as deforestation, industrialization, and fossil fuel combustion that release greenhouse gases (GHGs) into the atmosphere [2]. GHG includes carbon dioxide (CO2), the concentration of which has increased by about 50% over the last 200 years; methane (CH4), which accounts for roughly 25% of global warming; and nitrous oxide (N2O) [1]. These gases intensify the natural “greenhouse effect” by trapping heat in the atmosphere, leading to global warming, and an increase of approximately 1.2 °C in Earth’s average surface temperature since the pre-industrial era [1].
Increasing outdoor temperatures exacerbate the demand for maintaining thermal comfort in indoor environments, where individuals spend over 90% of their time [3]. The substantial energy required for heating, cooling, ventilation, and lighting indoor environments accounts for approximately 60% of building energy consumption [4,5]. The total building energy accounts for nearly 30% of global energy consumption and contributes up to 40% of global GHG emissions, reflecting the continued reliance of buildings on fossil fuel-based electricity [2,3,6]. Therefore, the growing impacts of climate change have intensified the need to reduce GHG emissions, particularly from buildings, which are a significant source of GHG emissions [7,8]. The traditional building management systems lack high resolution data, i.e., high level of detailed spatial and temporal awareness of indoor environments. Indoor mapping technology contributes to mitigating GHG emissions by providing the location intelligence necessary to optimize building operations, primarily in energy-intensive areas such as Heating, Ventilation and Air Conditioning (HVAC) and lighting systems, and by improving resource allocation and space utilization [9]. For example, indoor maps, integrated with Internet of Things (IoT) sensors, provide real-time data on occupancy levels and air quality in specific zones. This allows intelligent building management systems to adjust HVAC automatically, and lighting to match actual demand, rather than operating at full capacity in empty or underused areas [10]. Hence, indoor mapping—through Digital Twins (DTs) combining Light Detection And Ranging (LiDAR), Wireless Fidelity (Wi-Fi) positioning, or Bluetooth and sensor fusion—provides dynamic insight into how spaces are used, enabling improved spatiotemporal decision-making for building energy optimization [11,12,13].
This study aimed to review the role of indoor mapping as a spatiotemporal data framework for understanding, modelling, and reducing GHG emissions in buildings. Particular emphasis was given to the characteristics, energy efficiency applications, and technological advancements of indoor mapping. The study identified critical gaps, notably the limited exploration of indoor mapping as a tool for real-time, energy-efficient building management to reduce GHG emissions. The review further considered complementary emerging technologies—Building Information Modelling (BIM), DTs, Geographic Information Systems (GIS), Internet of Things (IoT), and LiDAR—which together provide a comprehensive foundation for sustainable, safe, smart, and connected building management.

2. Methods

The study relied primarily on original research articles and review papers published in peer-reviewed journals. Other authoritative sources, such as organizational publications, and United Nations and its associated agency reports on climate change, supplemented these articles. These sources were accessed via the OMNI Academic Search Tool—a shared library system across Ontario, Canada universities [14]—and through scholarly databases including GeoBase, IEEE Xplore, ScienceDirect, Web of Science, and Google Scholar.
Although the review primarily focused on indoor mapping, GHG emissions, and its applications, the search criteria focused on broader environment-related keywords: climate change, greenhouse gases, energy efficiency, and sustainable building management, as well as topic-related terms such as BIM, DTs, indoor mapping, indoor navigation, space planning, asset management, emergency response, and maintenance. The review also examined technological tools for indoor mapping. The search criteria included LiDAR point clouds, SLAM, GIS, ArcGIS Indoors, IndoorGML (Geography Markup Language), IoT, Indoor Positioning System (IPS), International Organization for Standardization (ISO), and Leadership in Energy and Environmental Design (LEED). Ultimately, the study selected 248 publications and web pages.
The keywords of these 248 sources were analyzed to construct a bibliometric map in Figure 1 using VOSviewer software (version 1.6.20, Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands) [15]. This analysis captured the key terms central to the environmental and technological themes discussed in this review. While the keywords are represented as nodes, the lines denote the network of mapping each node to another, drawing relationships between the node clusters. For example, the keyword “Digital Twin” is the largest network, while the keyword “GIS” is the most central, connecting a wider range of clusters such as climate change, LiDAR, energy efficiency and energy management. This visualization helped to identify the core keywords for this review article and broaden the search process.
The search for “indoor mapping” combined with environment-related keywords using the Boolean operator “AND” proved challenging, reflecting a relative scarcity of studies at the intersection of these domains and highlighting a research gap. The relatively small number of publications on “ArcGIS Indoors” may similarly be explained by its recent adoption and its early integration into academic research. Review articles were excluded, although studies cited within those reviews were considered. Full-length theses were also omitted because they are not peer-reviewed publications. When appropriate, web pages were included to illustrate key concepts and provide relevant real-world examples. Ultimately, 70 articles and web pages were shortlisted for further analysis based on their relevance.

3. Results and Discussion

Indoor mapping refers to the digital representation of interior spaces—such as office buildings, campuses, hospitals, malls, or factories—to support navigation, asset tracking, operations, and analytics [16,17]. Indoor mapping is placed at the intersection of several interconnected domains.
Spatial mapping provides the geometric and topological structure of indoor environments (rooms, corridors, floors, connectivity), typically generated through LiDAR scans, SLAM techniques, and floor plan digitization [18,19]. Localization builds on this map by determining the real-time position of people or assets using technologies such as Wi-Fi, BLE, UWB, or visual markers [16]. Sensing adds contextual awareness through IoT, GioT or Geo-IoT devices that capture environmental and operational data (temperature, occupancy, motion, air quality), enriching the static map with dynamic information [20].
When these layers of spatial and real-time data are integrated, predictive analytics can identify patterns—such as crowd flow, equipment failure risks, energy usage trends, or space utilization forecasts—enabling proactive decision-making [21,22]. To ensure interoperability across systems and platforms, standardization (SensorML, CityGML, IndoorGML, IMDF) and consistent data modelling frameworks such as CAD floor plans and BIM paradigms are essential; they define how spatial relationships, attributes, and semantics are structured and exchanged [23,24].
Together, these components form an organized indoor intelligence ecosystem where mapping is not just visualization, but a foundation for real-time insight, optimization, and automation. Therefore, the shortlisted articles and web sources were categorized into these six interconnected domains (and thus core technologies) and reviewed for further understanding. Only the sources that contained relevant technological components were included, and some shortlisted references were excluded as they did not align with the classification criteria. Table 1 presents the final set of classified references.

3.1. Spatiotemporal Data Models in Indoor Environments

The unique characteristics of indoor environments, in contrast to outdoor settings, have made indoor mapping a fundamentally different concept from traditional outdoor mapping. The intricate indoor structure—characterized by multi-level layouts, vertical connections, and access-restricted areas as opposed to outdoor space—necessitates indoor mapping to be represented as three-dimensional spatial systems [11,12]. Unlike outdoor spaces, where movement can often be represented along streets or paths, indoor environments are abstracted into networks, with rooms as nodes and doors as edges, to capture spatial connectivity and movement patterns [12]. Moreover, indoor spaces are cognitively hierarchical, meaning that structural, functional, and organizational dimensions interact to influence how different user groups conceptualize the indoor environment [58]. As the foundational layer of indoor maps, indoor spatial data models are generally classified into two categories: geometric and symbolic approaches [45,50]. Geometric models represent space using points, lines, areas, and volumes, providing precise metric information on locations, distances, and directions [50]. These spatial data models can also be represented in grid structures, which divide space into regular cells and provide precise locations and directions [50]. Symbolic models focus on topological relationships and graphs to capture connectivity, hierarchy, and movement between spatial units [50]. They provide a low level of geometric detail for entities and places and are considered less accurate [45,50]. However, from an application perspective, these models are preferred over geometric-based approaches due to their level of efficiency and flexibility.
Indoor spatial data models are the foundation for delivering context-aware services [45,50]. These models aim to extend to help locate static and moving objects in indoor spaces, support user interactions, and thus allow exploration of location information and relationships within the indoors. Therefore, context-aware spatial data models integrate spatiotemporal and user behavioral dimensions, providing hierarchical and multi-granular representations that support real-time, adaptive services in indoor environments [50]. Hence, indoor mapping is recognized for providing detailed spatial representations of indoor environments that support adaptive decision-making and navigation within large, complex buildings [16,17]. The product of the indoor mapping process—an indoor map—explicitly represents the spatial relationships between elements inside a building [51]. A complete 3D indoor map is achieved when both floor-level maps and cross-floor paths—such as stairs, ramps, and elevators are combined [51].
Beyond navigation, indoor mapping supports spatial analytics that inform the design and evaluation of interior layouts to optimize space use [11,16]. Its analytical potential gained prominence during the COVID-19 pandemic, which intensified the need for effective space management in enclosed areas to accommodate physical distancing, prevent overcrowding, and optimize ventilation to reduce airborne transmission [16]. In the public health sector, indoor mapping has been acknowledged as a decision-making tool for enhancing user experience, optimizing resource allocation, and improving safety [16]. However, while its role in navigation and public health applications has matured, its integration with building performance and energy management systems remains underdeveloped. This research gap highlights the need for interdisciplinary approaches that connect indoor spatial modelling with environmental monitoring and energy analytics to achieve more sustainable building management.

3.1.1. Spatial Representation of Indoor Environments

An indoor map serves as an indoor spatial data model. It relies on two main data sources: primary (remote sensing data, LiDAR-derived point clouds, SLAM, and images) and secondary (existing Computer Aided Design (CAD) floor plans and BIM data) [16,25]. LiDAR is a remote sensing technique that employs laser pulses to measure distances by recording the time interval and wavelength shift of the reflected light models [27,44]. This process generates dense three-dimensional point clouds that enable the development of highly accurate, high-resolution interior models [26,27,44]. LiDAR point clouds can be processed and classified within GIS platforms such as ArcGIS Desktop software (version 10.7, Esri, Redlands, CA, USA) [18] to create detailed, analyzable indoor spatial datasets. Large structural elements, such as walls, doors, and windows, are typically represented as lines or polygons, while smaller assets, including fire alarms, smoke detectors, and fire sprinkler heads, are captured as points using immersive mapping tools to extract accurate 3D coordinates [18]. Nevertheless, LiDAR acquisition is subject to several limitations. The laser pulse may fail to return to the sensor when obstructed due to surface absorbance, scattering, or refraction—such as with glass partitions—resulting in anomalies within the scans [26]. In addition, LiDAR scanning using handheld devices can result in lower levels of detail compared to more comprehensive surveying approaches, such as high-precision terrestrial laser scanning systems [23]. Despite these constraints, LiDAR provides the spatial precision required for indoor mapping to support data-driven energy analysis, enabling the identification of inefficiencies and contributing to strategies that reduce greenhouse gas emissions [16].
The emergence of SLAM represents a significant technological advance, enabling the real-time construction of spatially accurate maps in environments without prior information [19,25]. SLAM integrates diverse data streams from LiDAR, Inertial Measurement Units (IMUs), depth cameras, and complementary sensors, and renders 3D geometry [19,25]. Therefore, SLAM provides centimeter-level precision for navigating complex indoor environments, including those characterized by suboptimal illumination and feature-poor environments including empty rooms, long corridors, and recently constructed interiors [19]. The absence of Global Navigation Satellite Systems (GNSS) signals inside buildings makes it difficult to establish precise geographic locations; therefore, SLAM is a feasible method for accurate measurement and tracking [16,19].
Secondary data comprises existing CAD floor plans and BIM data prepared in adherence to architecture, engineering, and construction (AEC) practices, and are considered the most cost-effective data source [25,32,51]. CAD was developed with the intention of design and construction of indoor spaces; therefore, this application is highly relevant in the development of indoor mapping [12]. BIM is a parametric 3D paradigm that integrates geometry, materials, and construction details, providing semantically structured information [23,43,51]. This is crucial for indoor mapping, as it allows building information to be efficiently maintained and updated over the lifecycle. While BIM follows object-oriented principles and provides greater detail than CAD drawings, it is also more complex, making integration and harmonization tasks more challenging [23]. The absence of BIM for existing buildings requires the reliance on primary data acquisition methods, such as LiDAR scanning or manual measurements [23,29]. Combining these primary and secondary data sources as spatial and non-spatial attributes, GIS plays a key role in integrating these datasets to develop indoor mapping applications [43].
The development of Artificial Intelligence (AI) has advanced data integration techniques in indoor mapping. Deep learning (DL) models such as Mask R-CNN (Region-based Convolutional Neural Network) perform instance segmentation to accurately vectorize building elements from raster floor plan images [5,25,44]. The segmentation performance varies across building elements due to differences in line thickness, length, and type; therefore, multiple models with optimized hyperparameters are applied to maintain accuracy across diverse building plans [44]. Hypergraph models develop graph-based representations of floor plans where nodes encode rooms and hyperedges capture spatial relationships, enabling topologically optimized indoor maps stored in structured databases [3,44]. Integrating hypergraph models with automated design workflows has enabled the generation and evaluation of multiple layout alternatives, improving natural light access and energy performance in high-rise residential buildings compared to traditional retrofits [3,44]. As DL relies on referencing large datasets to make decisions, feeding more CAD data to the model would generate various spatial layouts that enhance natural lighting to the indoors. Although hypergraph models are used to model indoor layouts and influence designs to promote natural light access and thereby increase energy performance, it is not a feasible solution for existing buildings as reorganizing indoor layouts to promote natural lighting can often be more expensive, and complex. Also, these hypergraph models are not optimized to collect real-time data to minimize GHG emissions in the operational phase.
These foundational technologies underpinning indoor mapping—LiDAR, SLAM, CAD, BIM and AI-based reconstruction—enable the generation of precise 3D geometric models of buildings, which serve as a critical input for carbon accounting and GHG reduction strategies [57]. LiDAR produces accurate measurements of wall areas, roof surfaces, window ratios, and room volumes, while SLAM enables continuous spatial updates and localization; CAD provides detailed geometric drafting and component representation, while the BIM paradigm encodes building geometry and metadata in a structured, interoperable format supporting preparation of inputs for energy performance simulation; AI adds semantic understanding by identifying materials, systems, and usage zones [18,57,59]. This detailed 3D geometry feeds directly into building energy models, where surface areas and volumes determine heating and cooling loads, ventilation demand, and lighting requirements [60,61]. More accurate geometry leads to more precise energy simulations, enabling optimized HVAC zoning, demand-controlled ventilation, and reduced conditioning of underused spaces—thereby lowering electricity and fuel consumption and associated emissions [60,61]. The 3D model provides a reliable foundation for quantifying material volumes (e.g., concrete, steel, glazing), which are then multiplied by standardized emission factors to calculate embodied carbon [61]. Thus, the connection between 3D spatial mapping and GHG reduction lies in the role of geometric data in underpinning energy modelling, operational optimization, and verified carbon accounting.
Recent investments in indoor mapping have driven the development of dedicated commercial platforms—such as Apple Indoor Maps and Positioning, Google Indoor Maps and ArcGIS Indoors—which provide flexible applications for indoor navigation, asset management, and spatial analysis [16,56]. ArcGIS Indoors enables the conversion of CAD floor plans into interactive indoor GIS, integrating building attributes, floorplan lines, and interior spaces into structured geodatabases [55]. Despite their widespread use in facility and space management, these commercial platforms are rarely applied to monitor or optimize building energy performance, indicating a research and application gap [16,56].

3.1.2. Temporal Dynamics in Indoor Environments

Indoor map also functions as a real-time data model. The absence of GNSS signals within enclosed spaces has compelled indoor mapping to adopt alternative positioning technologies such as Wi-Fi, Bluetooth Low Energy (BLE), and Ultra-Wideband (UWB), to implement effective Indoor Positioning Systems (IPS) and support IoT applications [13,16,19]. IPS complements these technologies by continuously estimating the positions of people and assets within confined spaces to improve spatial awareness, operational efficiency, and facility management [33,34]. The integration of static architectural elements—such as walls and floors that define spatial boundaries—and points of interest that provide functional context, with dynamic, real-time data including user locations and routes, has characterized indoor mapping as a cohesive spatiotemporal information model [11,17].
As a GNSS free system, IPS estimates the distance between the target and anchor nodes using a suitable ranging technique; and then estimates the location of the target using different localization methods [33]. IPS characterizes both space and time within a context, highlighting the spatiotemporal feature of indoor mapping [34]. However, IPS technologies are not always accurate or precise. Widely used IPS localization parameters such as Received-Signal-Strength-Indicator (RSSI) often fluctuate signals due to refraction and reflection, resulting in positioning inaccuracies [33]. Therefore, Machine Learning (ML) techniques are used to offset these fluctuations, as they can make effective decisions using observed data without an accurate mathematical formulation [33]. However, ML is application specific, and its success depends on the accuracy of already trained models for each situation and their relevance to the new environment [33].
Indoor maps comprised of BIM data serve as a foundational layer for location-based services (LBS) within enclosed environments such as airports, hospitals, and office complexes [25,51]. By integrating positioning technologies with detailed indoor spatial models, LBS can deliver real-time navigation, occupancy monitoring, and context-aware services. Indoor map achieves its full potential when systems dynamically respond to users’ real-time locations, route choices, and surrounding indoor contexts, ensuring that maps remain contextually rich and adaptive rather than static or under-informative representations [17]. This convergence supports a range of applications, from energy-efficient building management to enhanced user experiences, positioning indoor mapping as a critical enabler of next-generation smart building solutions [25].
In addition to indoor positioning, IoT represents a distinct line of innovation focused on interconnecting digital devices, such as sensors and actuators, to enable real-time data collection, invisible communication, and automation, providing innovative services in the building sector for reducing the carbon footprint [34,42]. These devices generate vast streams of environmental and operational data, including temperature, occupancy, lighting, and energy consumption, which can be analyzed to support predictive maintenance and dynamic system optimization.

3.2. Link Between the Spatial Representation of Indoors and Energy Modelling

The energy performance of a building is determined by three interrelated factors: building design, system performance, and occupant behavior [62]. Building design establishes the framework within which systems operate, and occupants interact, generating spatially and temporally variable energy demands. Variations in spatial configuration, orientation, and façade designs can produce significant differences in energy use, and when combined with inefficient service systems and suboptimal occupant behavior, total energy demand can vary up to tenfold [62]. Accurate representation of building geometry and functional elements is therefore critical to effectively model these variations, emphasizing the role of standardized indoor spatial models in energy simulations. Indoor spaces can be classified as network spaces—such as corridors and staircases which constrain movement linearly, and scene spaces—such as halls and atria which allow more flexible, less constrained movement [58]. This distinction is relevant for energy modelling because occupancy, movement patterns, and dwell times differ between network and scene spaces, directly influencing heating, cooling, and lighting demand within buildings.

3.2.1. GIS-Based Applications for Spatial Energy Modelling

At the urban scale, for example a metropolitan area, GIS-based heat maps have been developed to identify efficient low-energy buildings and support public decision-making [4,52]. A notable example is the Urban Heat Mapping Tool in the City of Calgary, Canada [53]. This online dashboard visualizes Landsat 8 satellite imagery-derived land surface temperature alongside factors influencing urban heat, such as vegetation cover, built infrastructure, and building attributes [53]. The data represents snapshots from one summer day in selected years and are updated to the web application once per year, meaning the publicly accessible data does not provide continuous real-time monitoring of the city’s heat conditions [53]. Despite this limitation, the tool remains valuable for identifying spatial patterns of urban heat and informing climate adaptation strategies, illustrating a practical application of GIS for urban climate resilience.
The pilot study in Quart de Poblet, Spain, integrated dwelling-level energy efficiency certificates from the Energy Agency database and spatially aggregated them to parcels using GIS tools [4]. For buildings without energy certificates, data was obtained based on building physics and energy audits [4]. The resulting maps identified primary energy consumption and GHG emissions, enabling local authorities to prioritize districts for targeted energy-efficient interventions [4]. In Beijing, China, a study modeled building-level carbon emissions across public, residential, industrial, and transportation sectors [54]. The data were collected from multiple sources, including point of interest data from AutoNavi maps [54], official energy consumption statistics, emission factors from literature, administrative division maps, and land use maps. These data were integrated and processed in ArcGIS to produce high-resolution spatial representations of carbon emission intensity for each building [54]. The resulting maps were used to analyze sector-specific and total carbon emissions, providing insights into urban-scale energy management and decision-making, such as identifying emission hotspots and guiding low-carbon interventions [54]. Although the studies in Spain and China integrated high spatial resolution data at building level, it was used for district-level decision-making rather than at the individual building scale. This highlights a notable scarcity of indoor spatial representations for target building level energy optimization. Furthermore, the collected energy data, i.e., energy certificates in Spain and multiple data sources in China are static and do not present real-time operational data for spatiotemporal analysis.

3.2.2. Interoperability of Spatiotemporal Data for Energy Modelling

Interoperability between applications has long been a concern, particularly in environmental monitoring [41]. Earlier, CAD and GIS were largely incompatible until tools such as Google Earth and SketchUp introduced 3D viewing capabilities [12]. BIM and GIS use information models at different levels of detail, and the two applications are not fully compatible [28,43]. Similar challenges remain between sensor data and GIS. To address these challenges, the standardization of indoor spatial data is fundamental, as it provides formally defined data models, exchange schemas and performance metrics that enable semantic interoperability, maintain geometric and topological consistency, and support seamless data integration across diverse applications and platforms [24].
A range of international standards contributes to this effort, including Open Geospatial Consortium (OGC) standards such as CityGML for 3D city and landscape models, IndoorGML for indoor navigation network models, and the Apple’s Indoor Map Data Format (IMDF) for selected indoor features based on GeoJSON [46,47,48]. In addition, standards from the ISO and the International Electrotechnical Commission (IEC) establish frameworks and performance requirements for Real-Time Locating Systems (RTLS) [25]. At the building-level, ISO standards such as the Industry Foundation Classes (IFC) for BIM, further enrich indoor mapping by providing detailed geometric and semantic representations of building components [25,43]. These standards provide interoperable and detailed representations of building geometry, component properties, and functional relationships, allowing energy models to account for spatially and temporally variable loads.
Specific schemas such as the Green Building XML (gbXML) enable BIM–GIS data exchange for use in energy simulation tools such as EnergyPlus [49]. Similarly, the standardization of sensor data through OGC Sensor Web Enablement (SWE), using SensorML and Observations & Measurements (O&M), combined with integration via GeoServer and on-the-fly conversion into GIS-compatible formats such as GML, has significantly improved sensor–GIS interoperability [41]. Collectively, these standards and schemas reduce information loss during data exchange and format conversion, enhance the fidelity of simulations, and support targeted interventions to reduce energy consumption and associated GHG emissions [24].

3.3. Digital Twins as a Bridge Between Real-Time Data and GHG Mitigation

Digital Twins (DTs), as the term implies, are virtual replicas of the physical environment developed throughout its lifecycle [43]. A DT consists of three interlinked components: the real part (the physical environment), the digital part (commonly represented through BIM), and the information link that synchronizes both domains through IoT technologies [40,43,57]. The level of granularity required should guide the placement and number of sensors deployed [40]. Studies also highlight that BIM–GIS integration is recognized as the backbone of DTs that enable real-time insight into energy consumption and optimize infrastructure planning across urban environments [28,43]. Indoor mapping, through its detailed spatial representation of space and temporal data integration, embodies the key components for serving as a DT of the indoor space.
The practical deployment of DT and IoT in real-world buildings, including quantified energy performance evaluations, remains relatively limited. The DT-based assessment at Kyungpook National University in Daegu, South Korea, demonstrated that using Passive Infrared (PIR) sensors to monitor occupancy reduced power consumption by more than 60% and that illuminance adjustment to 500 lux achieved 46% energy savings [35]. The DT-IoT integration at Shanghai East Hospital in Shanghai, China, is a significant application that highlights full lifecycle integration of data from design through the operations and maintenance phase [39]. The DT dashboard displayed real-time sensor data analytics, with red-biased colors indicating energy usage. Using this system, the hospital achieved 1% annual energy savings and avoided more than 10% facility faults [39]. The Edge office building in Amsterdam, the Netherlands, employs a Light over Ethernet (LoE) LED system with 30,000 sensors to continuously adjust energy use based on occupancy needs controlled via smartphones [30]. Furthermore, parking space allocations, desk reservations, work order requests, and temperature control are handled via the building’s centralized mobile app [30]. The combined impact of these technologies has resulted in 70% lower electricity consumption, 50% lower lighting energy use, and an outstanding 98.3% rating by the Building Research Establishment Environmental Assessment Method (BREEAM) [30].
In recreational and retail buildings, DT implementation is focused on predictive maintenance. The Sports Facility in Paris, France, combines DT technology with IoT sensors to detect HVAC equipment faults early and uses advanced analytics to quantify deviations in operating conditions, enhancing operational efficiency. This approach resulted in 97.208 MWh of total energy savings, representing a 17% energy savings rate over 7 months [21]. As a leading retail chain, Walmart Inc. in the United States has deployed DTs in 4200 locations for real-time inventory management, shelf space planning, and predictive diagnosis of refrigeration, HVAC, electrical, and plumbing failures to reduce downtime and energy costs [22,36]. This has diminished emergency alert volume by 30% and saved 20% critical downtime costs [22,36].
Table 2 provides an overview of these five case studies, highlighting the respective building typology, key technologies employed, and their corresponding energy performance outcomes. While the Edge office building demonstrates the most extensive adoption of IoT, the other four case studies integrate key technologies in spatial mapping, localization, sensing, and predictive analytics to model their respective DTs.
At Umeå University in Sweden, occupancy sensors—such as PIR, CO2, and vision-based detectors were combined with calendar booking systems to act as space-use indicators [16,42]. These indicators identified underutilized lecture halls and oversized bookings, enabling facility managers to reassign events to appropriately sized rooms or consolidate space temporarily [42]. By aligning room assignments with actual occupancy, the university could reduce unnecessary heating, cooling, and lighting, demonstrating a tangible building-level GHG mitigation strategy [16,20]. However, this model lacks a DT framework to integrate its collected real-time sensor data and measure its impact on building energy levels.
A distinct application of an urban scale DT as an interface for real-time data and GHG mitigation is the decision-support tool—the Urban Digital Twin—GHG App in Singapore [52]. This was designed to support the mitigation of GHG emissions in ageing residential buildings [52]. The platform integrates real-time and historical data from open-access data sources, including building-level electricity use, equipment inventories, and 3D city datasets, and simulates missing data using the City Energy Analyst tool, allowing climate-related insights to be more accessible to both policymakers and the public [52].
The use of cellular network data from mobile and wireless devices enables the extraction of fine-grained spatiotemporal data on where and when people are present and how they move within the urban space [31]. The aggregated anonymized counts of device connections reconstruct large-scale, time-varying population distributions and urban mobility patterns for several million users [31]. In the New York City case study [31], integrating these mobility-derived population surfaces revealed that actual human movement leads to significantly different population-weighted exposure estimates compared to traditional home-based assumptions, demonstrating that exposure hotspots shift both spatially and temporally when real mobility is considered. In a DT context, adopting this mobility-informed approach could enhance the modelling of indoor air quality, enabling more precise, occupancy-driven adjustments to ventilation and energy systems.
When integrated with AI, DTs advance into Cognitive Digital Twins (CDTs), enabling predictive, adaptive, and data-driven decision-making processes [43]. The Smart Campus Digital Twin for Toronto Metropolitan University (formerly Ryerson University) campus is a CDT study focusing on the 66 campus buildings, out of which the Daphne Cockwell Complex is of primary focus [20]. The building incorporates a sub-metering system integrated into the building automation system for real-time data collection on energy consumption and climate control to inform decisions [20]. This CDT further leverages IoT networks and machine learning algorithms for complex analysis of operational performance and energy optimization [20]. However, this application remains in a proof-of-concept phase and has not yet been fully realized for campus-wide energy efficiency.
The deployment of IoT increases energy consumption, as large-scale sensor networks, cellular connectivity, and supporting data centers contribute to heat generation and CO2 emissions [37,38]. As a viable solution, the Green Internet of Things (G-IoT) approach promotes low-power communication protocols, energy-efficient hardware, and edge/fog computing to reduce energy consumption and carbon emissions associated with data transmission and processing [37,38]. Technologies such as Low-Power Wide-Area-Networks allow sensors to communicate over longer distances with minimal power, which is essential for continuous environmental monitoring within larger building complexes [40]. In addition, IoT devices such as occupancy sensors, smart plugs, and connected HVAC controllers pose significant privacy concerns because they transmit sensitive information on occupant presence, behavior patterns, and appliance usage to cloud services or third-party platforms [38,42]. IoT devices such as smart cameras, voice assistants, and thermostats are vulnerable to cyber-attacks; therefore, careful consideration of sensor selection, placement, and data governance is essential to balance operational efficiency with privacy protection [42].
Finally, the literature review summary is illustrated in Figure 2. The figure positions indoor mapping as the foundation of an indoor DT that enables greenhouse gas emission reduction. Through spatial mapping technologies (LiDAR, SLAM), indoor environments can be modeled in 3D to support high-resolution energy simulations. Localization technologies (Wi-Fi, Bluetooth, UWB, IMU) support occupant tracking, enabling demand-driven HVAC and lighting adjustments. IoT and Geo-IoT sensors collect real-time indoor environmental data that influences energy consumption, while AI-based predictive analytics estimate energy demand and optimize building systems. Interoperability standards such as SensorML, IMDF, IndoorGML, CityGML, and IFC allow seamless integration of BIM, GIS, and sensor data. By connecting these features to their corresponding technologies and GHG-related functions, Figure 2 illustrates how the indoor DT provides the spatiotemporal intelligence necessary to improve energy efficiency and reduce emissions within buildings.

3.4. Integration with Carbon Accounting Frameworks and Sustainability Metrics

Indoor spatiotemporal data becomes meaningful for sustainability only when it can be translated into standardized measures of energy use and greenhouse gas emissions through recognized carbon accounting frameworks. These data can be converted into CO2-equivalents using Global Warming Potentials for anthropogenic greenhouse gasses, allowing for a comprehensive assessment of organizational emissions [63]. Moreover, the inclusion of detailed operational data demonstrates how a range of spatially and temporally variable activities can be systematically quantified and incorporated into GHG inventories, emphasizing the value of fine-grained spatiotemporal data into identifying high-impact emission sources [63,64].
ISO 14064 provides the methodological framework for converting detailed spatiotemporal information captured by indoor mapping, IoT sensors, and DT into verifiable greenhouse gas inventories and emission-reduction assessments [63]. ISO 14064 is structured into three parts: (1) ISO 14064-1 for organizational GHG inventories, (2) ISO 14064-2 for project-based emission reductions, and (3) ISO 14064-3 for verifying GHG statements [65,66,67]. Following such frameworks, emissions can be categorized by scope, resource type, or activity, enabling organizations to target mitigation strategies for the most significant contributors to their carbon footprint while providing a robust baseline for monitoring and continuous improvement [63]. The integration of comprehensive operational and spatial data thus facilitates both accurate accounting and evidence-based sustainability planning, highlighting the potential of higher education institutions and other organizations to reduce emissions through informed interventions systematically [64].
While carbon accounting frameworks and standardized audits provide structured measures of sustainability, traditional building rating systems often focus on design intent or periodic checks, leaving real-time operational performance underrepresented [40]. The integration of DT with sustainability rating systems such as Leadership in Energy and Environmental Design (LEED) and Building Research Establishment Environmental Assessment Method (BREEAM) addresses this gap by enabling continuous, real-time monitoring of energy use across the entire building lifecycle [40,68]. LEED, for instance, is a voluntary certification program that primarily relies on design-phase checklists and does not fully capture operational performance throughout the building lifecycle [40,69]. LEED O+M (Operations & Maintenance) certification partially offsets this limitation through periodic audits and structured credits, but it still lacks real-time environmental data for continuous assessment [70]. Moreover, as with traditional building environmental assessments, LEED and BREEAM rely on fixed indicator-based credit systems with pre-assigned weights, which introduce subjectivity and emphasize design intent over ongoing operational measurement [68]. Integrating fine-grained spatiotemporal data from DTs ensures that sustainability assessments move beyond static audits and design intentions, providing a more accurate, continuous representation of building performance across its lifecycle.

3.5. Future Work and Recommendations

Future research should focus on directly linking indoor DTs with established carbon accounting systems to enable continuous emission monitoring rather than periodic audits. This requires indoor energy-mapping methods that automatically translate sensor and spatial data into recognized sustainability metrics, including scope-based GHG reporting, CO2 equivalents, lifecycle emission factors, and energy-use intensity indicators. Further work should also test AI-supported automation for indoor carbon mapping, ensuring that analytical models support practical decision-making without increasing system energy demand. In addition, developing simplified and standardized integration workflows connecting BIM, GIS, and IoT platforms with carbon accounting frameworks will be essential for scalability and reproducibility.
In practice, this integration can support building managers in identifying emission hotspots, prioritizing retrofits, optimizing HVAC zoning, improving space utilization, and strengthening evidence-based carbon reporting. By translating spatial intelligence into targeted operational strategies, the framework provides a practical pathway for reducing building-level carbon footprints.

4. Conclusions

The building sector is a major contributor to climate change, accounting for nearly 30% of global greenhouse gas emissions. Reducing these emissions requires advanced methods for capturing and analyzing indoor spatial data, particularly within complex indoor environments. Current research highlights significant advancements in indoor spatial data acquisition through two prominent approaches: (1) on-site remote sensing techniques such as LiDAR combined with SLAM, and (2) vectorizing existing floor plans using DL models such as Mask R-CNN. These data sources support the development of indoor mapping to examine building dynamics and energy consumption. In parallel, IoT technologies have been increasingly adopted to enable smart building management through real-time monitoring and control. However, IoT itself is an energy-consuming technology, promoting interest in greener alternatives such as G-IoT. At the urban scale, GIS-based heat maps have been used to identify high-emission buildings and recommend retrofit measures, illustrating the broader capacity of spatial information to guide emission-reduction strategies across urban environments. These developments highlight an important shift toward spatially enabled sustainability analytics.
Despite these advances, the integration of indoor mapping as a bridge between indoor spatial data and smart building management remains largely underexplored. While indoor spatial data capture technologies are well established and IoT systems are increasingly used to support smart building operations, limited research has connected these two domains to enhance building performance in an integrated spatiotemporal manner. For example, indoor maps rarely serve as a dynamic analytical layer that fuses sensor streams with geometry, material attributes, occupant positions, and equipment states—capabilities that would significantly improve decision-making for HVAC optimization, space utilization, or energy simulation. The absence of common standards and interoperable frameworks between BIM, GIS, and IoT further constrains this integration, limiting the development of comprehensive indoor DTs. Adoption of G-IoT platforms is further constrained by low stakeholder awareness, insufficient financial incentives, and the perceived complexity of integrating multiple spatial technologies. Moreover, high-resolution, GIS-based energy mapping (as heat maps) at the indoor scale is largely absent, reducing the ability to monitor and manage emissions effectively within individual rooms, zones, or building systems.
To address these gaps, this review underscores the emerging role of indoor mapping as a spatiotemporal decision-support tool for reducing GHG emissions in buildings. An indoor map can serve as an intermediate analytical layer that links indoor spatial data capture technologies with smart management systems, enriching these systems with geometry, spatial relationships, semantic attributes, and temporal dynamics. When extended into a full indoor DT, indoor mapping can support predictive analytics, simulate future energy demand, enable occupant-centric building control, and enhance the integration of renewable energy systems. Future research could extend this work by developing indoor mapping paradigms informed by qualitative input from key stakeholders (e.g., facility managers), quantifying actual reductions in energy use and associated utility costs, exploring AI-enhanced models for automated indoor energy mapping, and integrating these systems with G-IoT or renewable energy technologies such as photovoltaic panels and battery storage. Additionally, efforts to develop standards for BIM-GIS-sensor interoperability and to create algorithms for spatiotemporal fusion of indoor datasets will be critical for advancing sustainable smart building management.

Author Contributions

Conceptualization, V.N.G. and K.Z.; Methodology, V.N.G.; Data curation, V.N.G.; Formal Analysis, V.N.G.; Writing—original draft, V.N.G.; Supervision, M.K.H. and K.Z.; Writing—review & editing, M.K.H. and K.Z.; Project Administration, M.K.H. and K.Z.; Funding Acquisition, M.K.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Mitacs Accelerate program (IT37659) in partnership with Four Rivers Environmental Services Group, Matawa First Nations Inc., Thunder Bay, ON, Canada.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

The authors express their gratitude to the team at Four Rivers Environmental Services Group, Matawa First Nations Inc, Thunder Bay, ON, Canada for their support in disclosing indoor spatial information; Esri Canada for being the software partner; and Reg Nelson, the GIS Technician at Geospatial Data Centre, Lakehead University for his technical assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AECArchitecture, Engineering, and Construction
AIArtificial Intelligence
BIMBuilding Information Modelling
BLEBluetooth Low Energy
BREEAMBuilding Research Establishment Environmental Assessment Method
CADComputer Aided Design
CDTCognitive Digital Twin
DLDeep Learning
DTDigital Twin
Geo-IoTGeospatial Internet of Things
GISGeographic Information Systems
GHGGreenhouse Gas
GIoTGreen Internet of Things
GMLGeography Markup Language
GNSSGlobal Navigation Satellite Systems
HVACHeating, Ventilation, and Air Conditioning
IMUInertial Measurement Units
IECInternational Electrotechnical Commission
IFCIndustry Foundation Classes
IoTInternet of Things
IPSIndoor Positioning System
ISOInternational Organization for Standardization
LEEDLeadership in Energy and Environmental Design
LBSLocation-Based Services
LiDARLight Detection And Ranging
MLMachine Learning
O&MObservations & Measurements
OGCOpen Geospatial Consortium
R-CNNRegion-based Convolutional Neural Network
RTLSReal-Time Locating Systems
SLAMSimultaneous Localization and Mapping
SWESensor Web Enablement
UNDPUnited Nations Development Programme
UNEPUnited Nations Environment Programme
UWBUltra-Wideband
Wi-FiWireless Fidelity

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Geomatics 06 00027 g001
Figure 1. Co-occurrence network of keywords generated using VOSviewer software [15]. Node colors represent the average publication year, with lighter colors indicating more recent research activity. The legend bar reflects this publication timeline, illustrating that much of the literature related to “Digital Twin” consists of recent publications. Cluster sizes and link strengths reflect the degree of co-occurrence among keywords.
Figure 1. Co-occurrence network of keywords generated using VOSviewer software [15]. Node colors represent the average publication year, with lighter colors indicating more recent research activity. The legend bar reflects this publication timeline, illustrating that much of the literature related to “Digital Twin” consists of recent publications. Cluster sizes and link strengths reflect the degree of co-occurrence among keywords.
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Figure 2. The conceptual framework illustrates how indoor mapping, functioning as a DT, integrates spatial mapping, localization, sensing, predictive analytics, standardization, and data modelling to support greenhouse gas emission reductions identified in the reviewed literature. The diagram links core features with enabling technologies and their direct relevance to optimize energy use, improving building performance, and informing climate-responsive decision-making.
Figure 2. The conceptual framework illustrates how indoor mapping, functioning as a DT, integrates spatial mapping, localization, sensing, predictive analytics, standardization, and data modelling to support greenhouse gas emission reductions identified in the reviewed literature. The diagram links core features with enabling technologies and their direct relevance to optimize energy use, improving building performance, and informing climate-responsive decision-making.
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Table 1. Classification of shortlisted references.
Table 1. Classification of shortlisted references.
FeatureKey TechnologiesReferences
Spatial MappingLight Detection And Ranging (LiDAR)
Simultaneous Localization and Mapping (SLAM)
Photogrammetry		[16,18,19,23,25,26,27,28,29]
LocalizationIndoor Positioning System (IPS)
Wireless Fidelity (Wi-Fi)
Bluetooth
Ultra-Wideband (UWB)
Inertial Measurement Units (IMU)		[13,25,30,31,32,33,34]
SensingInternet of Things (IoT)
Green Internet of Things (GIoT)
Geospatial Internet of Things (Geo-IoT)		[13,20,21,22,25,30,33,34,35,36,37,38,39,40,41,42,43]
Predictive AnalyticsArtificial Intelligence (AI)
Machine Learning (ML)
Deep Learning (DL)
Convolutional Neural Network (CNN)
Cognitive Digital Twin (CDT)		[3,10,20,21,22,32,33,39,43,44]
StandardizationCityGML (Geography Markup Language)
IndoorGML (Geography Markup Language)
Indoor Mapping Data Format (IMDF)
Industry Foundation Classes (IFC)
Green Building XML (gbXML)
SensorML (Model Language)
Observations & Measurements (O&M)		[12,24,25,43,45,46,47,48,49]
Data ModellingComputer Aided Design (CAD)
Building Information Modelling (BIM)
Geographic Information Systems (GIS)		[4,12,16,18,20,21,23,25,28,34,39,40,41,43,50,51,52,53,54,55,56,57]
Table 2. Overview of Digital Twin and key technology implementations for building energy management.
Table 2. Overview of Digital Twin and key technology implementations for building energy management.
TypologyCase StudyKey TechnologiesEnergy Performance Metrics
AcademicKyungpook National University in Daegu, Republic of Korea [35]
  • PIR sensors for occupancy and lighting control
  • 60% energy savings by using PIR sensors
  • 46% energy savings by illuminance adjustment
HealthcareShanghai East Hospital in Shanghai, China [39]
  • 1900 smart sensors for the elevator, 2 monitoring systems for electricity & water supply, & 100 sensors from 3 medical gas systems
  • Bluetooth beacons for indoor positioning
  • AI-driven fault diagnosis & automated decision-making
  • 1% energy savings per year
  • 10% more facility faults avoided with DT diagnosis
Office 1The Edge in Amsterdam, The Netherlands [30]
  • LoE LED system with 30,000 sensors
  • Mobile app control for parking, office space, work orders & temperature
  • 70% less electricity consumption
  • 50% reduction in lighting energy
RecreationalSports Facility in Paris, France [21]
  • Electric meters and vibration sensors for predictive monitoring of HVAC equipment failures
  • Digital meters, occupancy and motion sensors for HVAC and lighting control
  • 97.208 MWh total energy savings over a 7-month period
  • 17% reduction in energy consumption
RetailWalmart Inc. in Bentonville, AR, USA [22,36]
  • Sensors for real-time inventory tracking, shelf space planning & predictive diagnosis
  • Drone imagery for DT modelling
  • 30% reduction in emergency alerts
  • 20% savings in critical downtime costs
  • $1.4 M total savings of downtime costs in 6 months
1 In the above list of case studies, only the Edge building does not have a DT framework.
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MDPI and ACS Style

Goonetilleke, V.N.; Heenkenda, M.K.; Zaniewski, K. Indoor Mapping as a Spatiotemporal Framework for Mitigating Greenhouse Gas Emissions in Buildings: A Review. Geomatics 2026, 6, 27. https://doi.org/10.3390/geomatics6020027

AMA Style

Goonetilleke VN, Heenkenda MK, Zaniewski K. Indoor Mapping as a Spatiotemporal Framework for Mitigating Greenhouse Gas Emissions in Buildings: A Review. Geomatics. 2026; 6(2):27. https://doi.org/10.3390/geomatics6020027

Chicago/Turabian Style

Goonetilleke, Vinuri Nilanika, Muditha K. Heenkenda, and Kamil Zaniewski. 2026. "Indoor Mapping as a Spatiotemporal Framework for Mitigating Greenhouse Gas Emissions in Buildings: A Review" Geomatics 6, no. 2: 27. https://doi.org/10.3390/geomatics6020027

APA Style

Goonetilleke, V. N., Heenkenda, M. K., & Zaniewski, K. (2026). Indoor Mapping as a Spatiotemporal Framework for Mitigating Greenhouse Gas Emissions in Buildings: A Review. Geomatics, 6(2), 27. https://doi.org/10.3390/geomatics6020027

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