Indoor Mapping as a Spatiotemporal Framework for Mitigating Greenhouse Gas Emissions in Buildings: A Review
建物の温室効果ガス排出削減のための時空間フレームワークとしての屋内マッピング：レビュー (AI 翻訳)
Vinuri Nilanika Goonetilleke, Muditha K. Heenkenda, K. Zaniewski
🤖 gxceed AI 要約
日本語
本レビューは、屋内マッピング技術（LiDAR、SLAM、深層学習によるフロアプラン抽出など）が、建物からの温室効果ガス排出削減にどのように貢献できるかを総合的に検討。デジタルツイン、BIM、GIS、IoTとの統合により、エネルギー効率の向上やリアルタイム管理が可能となり、データに基づく排出削減戦略を強化する。特に、物理空間と仮想レイヤーの統合不足という課題を指摘し、時空間的意思決定を支える中間解析レイヤーとしての空間モデリングの重要性を強調。
English
This review synthesizes how indoor mapping technologies (LiDAR, SLAM, deep learning-based floor plan extraction) contribute to reducing greenhouse gas emissions from buildings. Integration with Digital Twins, BIM, GIS, and IoT enables improved energy efficiency and real-time management, strengthening data-driven mitigation strategies. It identifies the limited integration of physical and virtual layers as a key gap and advocates for spatial modeling as an intermediate analytical layer for spatiotemporal decision-making.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の建築分野はGHG排出の約3割を占め、省エネ・脱炭素が急務。本レビューは、屋内マッピング×AIの技術動向を整理しており、日本のスマートビル・ZEB推進や、SSBJのスコープ2排出削減（電力使用効率化）にも示唆を与える。
In the global GX context
As buildings account for ~30% of global emissions, indoor mapping offers a practical pathway to operational decarbonization. The review's emphasis on digital twins and AI-driven spatial analysis aligns with global trends in smart buildings and energy management, providing actionable insights for TCFD/ISSB reporting on Scope 1&2 reductions.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of indoor mapping methods for building GHG mitigation, highlighting gaps and future research directions.
🏢実務担当者:Building managers and sustainability teams can adopt indoor mapping-integrated energy management systems to reduce operational carbon and costs.
🏛政策担当者:Informs building codes and incentive programs by demonstrating the potential of spatial technologies for emissions reduction.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/geomatics6020027first seen 2026-07-02 06:27:26
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