The application of the convolutional neural network has shown to greatly improve the accuracy of building extraction from remote sensing imagery. In this paper, we created and made open a ...high-quality multisource data set for building detection, evaluated the accuracy obtained in most recent studies on the data set, demonstrated the use of our data set, and proposed a Siamese fully convolutional network model that obtained better segmentation accuracy. The building data set that we created contains not only aerial images but also satellite images covering 1000 km 2 with both raster labels and vector maps. The accuracy of applying the same methodology to our aerial data set outperformed several other open building data sets. On the aerial data set, we gave a thorough evaluation and comparison of most recent deep learning-based methods, and proposed a Siamese U-Net with shared weights in two branches, and original images and their down-sampled counterparts as inputs, which significantly improves the segmentation accuracy, especially for large buildings. For multisource building extraction, the generalization ability is further evaluated and extended by applying a radiometric augmentation strategy to transfer pretrained models on the aerial data set to the satellite data set. The designed experiments indicate our data set is accurate and can serve multiple purposes including building instance segmentation and change detection; our result shows the Siamese U-Net outperforms current building extraction methods and could provide valuable reference.
While photovoltaic (PV) systems are being installed at an unprecedented rate, it is challenging to keep track of them due to their decentralized character and large number. In this paper, we present ...the 3D-PV-Locator for large-scale detection of roof-mounted PV systems in three dimensions (3D). The 3D-PV-Locator combines information extracted from aerial images and 3D building data by means of deep neural networks for image classification and segmentation, as well as 3D spatial data processing techniques. It thereby extends existing approaches for the automated detection of PV systems from aerial images by also providing their azimuth and tilt angles. We evaluate the 3D-PV-Locator using a large dataset gathered from the official German PV registry in a real-world study with more than one million buildings. In terms of azimuth and tilt angles, our evaluation shows that the 3D-PV-Locator and the official registry coincide for about two thirds of the observations and are within neighboring classes for 84 and 99 percent of the observations, respectively. In terms of detected PV system capacity, we show that the 3D-PV-Locator clearly outperforms existing approaches. It performs particularly well for the groups of small and medium-sized PV systems (3.6–33.1 percent error reduction) and PV systems tilted beyond 40° (25.6–38.1 percent error reduction). The 3D PV system data generated by the 3D-PV-Locator can inform several practical applications, such as improved forecasting of solar generation, the optimized planning and operation of distribution networks, improved integration of electric vehicles, and others. All datasets and pre-trained models associated with this paper are available online.
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•Methodology for large-scale detection of solar panels in three dimensions.•Solar panel information is extracted from aerial images and 3D building data.•Extension of existing PV detection approaches by providing azimuth and tilt angles.•Improved solar panel area and capacity estimates, especially for residential units.•All associated datasets, models, and code are publicly available.
Selecting an appropriate data persistent system for a specific use case necessitates a thorough examination of the application domain and the characteristics of the data expected to be stored. While ...comparative studies of data persistent systems exist in various domains, there is a notable absence of such studies concerning building and environmental data management. This research aims to bridge this gap by conducting a comparative evaluation based on building and environmental datasets and use cases. The study primarily focuses on two types of database systems, namely relational database systems and graph-based database systems. Two building and two city models are employed in the evaluation. The building data sets are extracted from IFC models, and environmental data are extracted from CityGML and OpenStreetMap. The assessment involves qualitatively analysing the database design process of the systems and quantitatively evaluating the efficiency of retrieving data from those systems. The comparative evaluation identifies at least two crucial aspects to consider when selecting a suitable data-persistent system for managing building and environmental data. The first aspect pertains to the stability of the data to be stored, along with the complexity of interrelationships within the building and environmental dataset. The second aspect involves the manner in which data is retrieved to accomplish different tasks within the particular business case. The findings demonstrate that use cases that typically manage interrelated data and necessitate the traversal of complex relationships between building and environmental features are better managed by graph-based database systems, particularly when dealing with large datasets. Conversely, relational databases exhibit superior performance for use cases requiring minimal or no relationship traversal, regardless of dataset size. The contributions of this study can serve as valuable input when designing information management tools and systems for building and environmental data management.
•The largest compilation of building energy data in the US, with over 750,000 buildings.•Most of the effort lies in data cleansing and mapping to a common data schema.•Paper includes comparisons to ...data in CBECS and RECS – the US national statistical datasets.•The database supports empirical comparison of energy use and data-driven savings analysis.
Building energy data has been used for decades to understand energy flows in buildings and plan for future energy demand. Recent market, technology and policy drivers have resulted in widespread data collection by stakeholders across the buildings industry. Consolidation of independently collected and maintained datasets presents a cost-effective opportunity to build a database of unprecedented size. Applications of the data include peer group analysis to evaluate building performance, and data-driven algorithms that use empirical data to estimate energy savings associated with building retrofits. This paper discusses technical considerations in compiling such a database using the DOE Buildings Performance Database (BPD) as a case study. We gathered data on over 750,000 residential and commercial buildings. We describe the process and challenges of mapping and cleansing data from disparate sources. We analyze the distributions of buildings in the BPD relative to the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS), evaluating peer groups of buildings that are well or poorly represented, and discussing how differences in the distributions of the three datasets impact use-cases of the data. Finally, we discuss the usefulness and limitations of the current dataset and the outlook for increasing its size and applications.
In this study, the Brick ontology is a unified semantic metadata standard for building assets and their relationships, serving as a key enabler for effective interoperability and automation of ...building systems and analytics. However, creating a Brick model, in other words, standard semantic metadata based on the Brick ontology for a building dataset, can be a complex task. This paper presents two case studies of the creation of Brick models for real-world residential and commercial building datasets, highlighting the challenges during the Brick model creation process. Additionally, the paper introduces VizBrick, an interactive authoring tool for creating semantic building metadata. VizBrick facilitates the creation of Brick models by providing an intuitive visual interface and interactive capabilities, such as keyword search, automatic mapping suggestions, and recommendations. The use of VizBrick is shown to significantly reduce the time and effort required during the Brick model creation process.
Buildings are considered the enormous source of untapped energy efficiency potential in the global carbon neutrality. It is necessary to ensure that buildings are energy-efficient using operational ...pattern analytics and diagnostics. Therefore, this study proposes a novel symbolic hierarchical clustering method (named HOS-SAX) to evaluate the building system operation, efficiency, and energy usage patterns. The proposed HOS-SAX method is intended to enhance the existing methods that focus only on the energy usage characteristics and thus offer limited insights on the building system and operational efficiency. The proposed method consists of: (1) Holistic Operational Signature (HOS) and (2) HOS-based symbolic aggregate approximation (SAX) analyses. A HOS analysis is conducted to derive the representative operational signatures for building operation and efficiency using system-, building-, and weather-level data. Then, SAX is performed with the operational signatures derived from the HOS to cluster the building operation patterns. In a case study for a district heating substation serving residential buildings, the HOS-SAX cluster analysis showed 15 sections in the cluster map that visualize the: (1) energy usage, (2) design efficiency, and (3) control efficiency. The cluster map revealed that the sections that operated inefficiently account for approximately 71% of the entire operation period. Moreover, it is expected that the supply temperature of 0.87 °C can be reduced in the most inefficient sections.
•Operational signature-based symbolic aggregation approximation (SAX) method is proposed.•Holistic Operational Signatures (HOS) are presented for a district heating substation.•Severe inefficient sections derived from the HOS-SAX map accounted for approximately 71%.•HOS-SAX cluster analysis can evaluate the building system's operation and efficiency.
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•There is a lack of open data on urban rooftop typology and current use of roofs.•A deep learning and GIS workflow to map and quantify green and solar roofs.•A generated dataset that ...covers 17 cities, scalable to include more locations.•An index to benchmark the proliferation of green and solar roofs in cities.
Sustainable roofs, such as those with greenery and photovoltaic panels, contribute to the roadmap for reducing the carbon footprint of cities. However, research on sustainable urban roofscapes is rather focused on their potential and it is hindered by the scarcity of data, limiting our understanding of their current content, spatial distribution, and temporal evolution. To tackle this issue, we introduce Roofpedia, a set of three contributions: (i) automatic mapping of relevant urban roof typology from satellite imagery; (ii) an open roof registry mapping the spatial distribution and area of solar and green roofs of more than one million buildings across 17 cities; and (iii) the Roofpedia Index, a derivative of the registry, to benchmark the cities by the extent of sustainable roofscape in term of solar and green roof penetration. This project, partly inspired by its street greenery counterpart ‘Treepedia’, is made possible by a multi-step pipeline that combines deep learning and geospatial techniques, demonstrating the feasibility of an automated methodology that generalises successfully across cities with an accuracy of detecting sustainable roofs of up to 100% in some cities. We offer our results as an interactive map and open dataset so that our work could aid researchers, local governments, and the public to uncover the pattern of sustainable rooftops across cities, track and monitor the current use of rooftops, complement studies on their potential, evaluate the effectiveness of existing incentives, verify the use of subsidies and fulfilment of climate pledges, estimate carbon offset capacities of cities, and ultimately support better policies and strategies to increase the adoption of instruments contributing to the sustainable development of cities.
The Brick ontology is a unified semantic metadata standard for building assets and their relationships, serving as a key enabler for effective interoperability and automation of building systems and ...analytics. However, creating a Brick model, in other words, standard semantic metadata based on the Brick ontology for a building dataset, can be a complex task. This paper presents two case studies of the creation of Brick models for real-world residential and commercial building datasets, highlighting the challenges during the Brick model creation process. Additionally, the paper introduces VizBrick, an interactive authoring tool for creating semantic building metadata. VizBrick facilitates the creation of Brick models by providing an intuitive visual interface and interactive capabilities, such as keyword search, automatic mapping suggestions, and recommendations. The use of VizBrick is shown to significantly reduce the time and effort required during the Brick model creation process.
•Creating semantic building metadata models for building requires semantic technology expertise and often results in trials and errors.•The paper presents the challenges encountered during Brick model creation for two real-world building data sets.•VizBrick is an interactive authoring tool that enables users to create semantic building metadata visually, reducing the time and efforts.
The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are ...mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
OpenStreetMap (OSM) is currently an important source for building data, despite the existence of potential quality issues. Previous studies have assessed OSM data quality by comparing it with ...reference building data, which may not otherwise be readily available. This study assessed OSM building completeness using population data, and investigated the effectiveness of using population data for building reference data. We proposed various approaches, including type-based and regression-based approaches and their subtypes, and designed measures and methods to evaluate these approaches. Our evaluation examined four study areas in two countries, using global population data sets at three spatial resolutions (1-km, 100-m, and 30-m). Results showed that the type-based approach correctly classified approximately 80-99% of the assessed grid cells. The regression-based approach resulted in a high linear correlation (0.7 or greater) between the population counts and the referenced building count/building area size, with the strongest correlation present for the 1-km population dataset. We conclude that the use of population data as referenced building data is an effective method for the assessment of OSM building completeness. The paper concludes with the advantages and limitations of using both the type-based and the regression-based approaches.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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