Building Footprints Research Articles

Special Issue: Building Decarbonization

Abstract Emission of carbon dioxide and other greenhouse gases from human activities are considered the main contributors to the global temperature rise over the past several decades. The substantial carbon footprint of buildings stems from emissions due to buildings' energy and water usage (operational carbon) and the emissions from materials and products used for buildings during manufacturing, transportation, installation, and decommissioning (embodied carbon). As countries strive to better understand and mitigate the impacts of climate change, decarbonizing the building sector is considered a critical priority and an opportunity. This special issue brings together a collection of articles that explore innovative approaches and technologies aimed at reducing carbon emissions across various aspects of building design, construction, and operation. The Special Issue covers a wide list of topics related to building decarbonization ranging from innovative heating/cooling technologies, use of renewable energy sources, energy storage applications, novel materials for building envelopes, and review of technologies for building decarbonization in high-density urban areas.

Estimating Cooling Energy Demand from Building Attributes and Environmental Parameters using 3D City Models

Abstract. Most of the global population have shifted to urbanization with advancements in technology. With this transition comes the responsibility of applying these technologies to promote sustainable development practice. Since energy demand is highest in the urbanized areas, it is important that proper assessment and management of energy resource is considered in policy-making and urban planning. This study investigated the estimation of cooling energy demand in Iloilo City Proper, Philippines using a 3D city model integrated with building attributes including building functions and year of construction, while also taking into consideration meteorological factors in the area. The proposed method used the application SimStadt2.0, following the German computation standard DINV18599. Building functions and year of construction were extracted from available building attributes data and satellite images respectively, using the free and open-source software QGIS. A CityGML Level of Detail 1 was generated from 3Dfier using building footprints and LiDAR point cloud data, along with the extracted building attributes of the 5,426 buildings. Meteorological data from INSEL 8 were also considered in the estimation of cooling energy demand in SimStadt2.0. Results showed a monthly energy demand of 12.33 kWh to 313,530.08 kWh in the study area. The estimated energy demand values were higher than the standard mean for different building functions in the country, but within the expected range of values for each season. Urban Heat Islands (UHIs), analyzed using Land Surface Temperature (LST) values, also have significant correlation to areas with higher cooling energy demands. However, inconsistencies can imply the need for further investigation.

Evaluating the stability of architectural heritage from the perspective of InSAR: a practical study on Jianchuan Ancient Town

AbstractThe increase in human activities and natural degradation often leads to tilting, collapsing, and other forms of deterioration in architectural heritage, posing significant threats to its safety. Therefore, timely detection of abnormal deformation signals in buildings is essential for the protection of architectural heritage. Spaceborne synthetic aperture radar interferometry (InSAR) can detect slight displacements over large areas. However, relying solely on annual mean velocity maps derived from InSAR may not accurately assess the stability of ancient buildings. In this study, we developed a framework that evaluates the stability of individual buildings by integrating the InSAR displacements with building footprints. Vertical and rotational deformations, along with the temporal evolution of these deformations, are combined to generate the results for building stability assessment. We used 74 TerraSAR-X images captured from August 2017 to November 2019 to evaluate the stability of Jianchuan Ancient Town, a renowned National Historical and Cultural City in China. The displacement result suggests that the majority of Jianchuan Ancient Town remains stable, with over 93% of coherent points (CTs) displaying displacement velocities ranging from −5 to 5 mm/yr. Out of the 1,891 buildings in Jianchuan Ancient Town, 1404 are considered stable, 352 are of moderate stability, and 12 are deemed unstable. This study showed the potential of InSAR applications in assessing the stability of architectural heritages, emphasizing its crucial role in heritage preservation and management.

MLP Feature Extraction from Coordinates for Building Footprint Simplification using Graph Convolutional Networks

MLP Feature Extraction from Coordinates for Building Footprint Simplification using Graph Convolutional Networks

Automated Estimation of Building Heights with ICESat-2 and GEDI LiDAR Altimeter and Building Footprints: The Case of New York City and Los Angeles

Accurate estimation of building height is crucial for urban aesthetics and urban planning as it enables an accurate calculation of the shadow period, the effective management of urban energy consumption, and thorough investigation of regional climatic patterns and human-environment interactions. Although three-dimensional (3D) cadastral data, ground measurements (total station, Global Positioning System (GPS), ground laser scanning) and air-based (such as Unmanned Aerial Vehicle—UAV) measurement methods are used to determine building heights, more comprehensive and advanced techniques need to be used in large-scale studies, such as in cities or countries. Although satellite-based altimetry data, such as Ice, Cloud and land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), provide important information on building heights due to their high vertical accuracy, it is often difficult to distinguish between building photons and other objects. To overcome this challenge, a self-adaptive method with minimal data is proposed. Using building photons from ICESat-2 and GEDI data and building footprints from the New York City (NYC) and Los Angeles (LA) open data platform, the heights of 50,654 buildings in NYC and 84,045 buildings in LA were estimated. As a result of the study, root mean square error (RMSE) 8.28 m and mean absolute error (MAE) 6.24 m were obtained for NYC. In addition, 46% of the buildings had an RMSE of less than 5 m and 7% less than 1 m. In LA data, the RMSE and MAE were 6.42 m and 4.66 m, respectively. It was less than 5 m in 67% of the buildings and less than 1 m in 7%. However, ICESat-2 data had a better RMSE than GEDI data. Nevertheless, combining the two data provided the advantage of detecting more building heights. This study highlights the importance of using minimum data for determining urban-scale building heights. Moreover, continuous monitoring of urban alterations using satellite altimetry data would provide more effective energy consumption assessment and management.

An OpenStreetMap derived building classification dataset for the United States

Building classification is crucial for population estimation, traffic planning, urban planning, and emergency response applications. Although essential, such data is often not readily available. To alleviate this problem, this work presents a comprehensive dataset by providing residential/non-residential building classification covering the entire United States. We developed a dataset of building types based on building footprints and the available OpenStreetMap information. The dataset is validated using authoritative ground truth data for select counties in the U.S., which shows a high precision for non-residential building classification and a high recall for residential buildings. In addition to the building classifications, this dataset includes detailed information on the OpenStreetMap data used in the classification process. A major result of this work is the resulting dataset of classifying 67,705,475 buildings. We hope that this data is of value to the scientific community, including urban and transportation planners.

Pixels relationship analysis for extracting building footprints

Abstract. Currently, remote sensing research is focused on developing an automated algorithm that can compete with empirical methods for mapping the contours of individual buildings. Despite facing numerous challenges related to suboptimal imaging conditions, diverse building architecture, and complex backgrounds, creating such an algorithm is essential for monitoring urban and natural areas, generating 3D city models, managing disasters, and estimating population density. Obtaining the polygonal boundary of a building and extracting a vectorized building mask as output for direct use is one of the current challenges in drawing building outlines. This work provides a comprehensive workflow for building extraction and improves their predicted area by regularization the boundaries of buildings. First, a convolutional neural network is used to train the binary semantic segmentation model and then regularization and vectorization processes are performed. The main difference from existing methods is a new regularization method based on compiling a neighborhood matrix for each point belonging to the “building” class. According to the experimental results, the algorithm shows high efficiency: IOU (intersection over union) = 91.2%, AP (average precision) = 64.1% and AR (average recall) = 75.1%, comparable to leading building boundary extraction methods such as PolyWorld.

A Comparison of Uncertainty Estimation Methods for Building Footprint Change Detection from Sentinel-2 Imagery

Abstract. This manuscript investigates the effects of uncertainty methods applied to the problem of deep learning-based semantic segmentation of building footprints on moderate-resolution satellite imagery. While the recent efforts of big corporations to add information about building locations and sizes on a global or continental scale are generally valuable, still the overall challenge persists in identifying the spatial-temporal patterns of growing urbanization. In this work, we extend UNet-type architectures to perform binary building footprint classification based on Sentinel-2 imagery resulting in five different models. While previous studies focused on urban areas in the Western world we conduct all training and evaluation in India. All models are trained on Microsoft building footprint products while for evaluation purposes high-quality reference data is manually selected from regions with especially good open-street-map coverage. Quantitative and qualitative experiments are conducted where a significant performance gain is found for a model trained with a mixture of aleatoric and epistemic uncertainty measures. The performance gain is even more pronounced for subsequent quantitative multi-temporal change detection experiments.

Multiscale assessment of land use efficiency in functional urban areas of Munich and Augsburg

Abstract. This study aims to understand the spatio-temporal variations in land use efficiency (LUE) estimations at different scales of assessment, focusing on the per-capita built-up area within the Functional Urban Areas (FUAs) of Munich and Augsburg, Germany. The analysis is done for the time epochs 1985, 1995, 2005, 2015 and 2020. FUAs are defined as the economic area of influence in each urban area, making the study of land consumption in FUAs crucial for spatial planning and policy analysis. Land use efficiency is computed using built up surface and population data extracted from the Global Human Settlement Layer. This is done at municipal level and 100 m grid level. Furthermore, the effects of using high resolution building footprints for LUE computations are assessed. The analysis shows that urban cores and the adjacent regions are densifying with lower per capita built surface consumption while smaller urban areas utilise more per capita built surface leading to inefficent land use. It is also observed that higher resolution assessments helps in identifying varying intra-regional LUE estimates for a focused urban planning strategy development. Our study concludes that land use efficiency has generally decreased over time in the commuting zones of FUAs while the urban cores have remained efficient.

Navigating Disparities in Dental Health-A Transit-Based Investigation of Access to Dental Care in Virginia.

To identify vulnerable areas and populations with limited access to dental care in Virginia, the study aimed (1) to calculate travel time and accessibility scores to dental care in Virginia using a transit-based accessibility model for all dental clinics and dental clinics participating in the Medicaid dental program and (2) to estimate factors associated with accessibility to dental clinics participating in the Medicaid dental program in Virginia. The study used building footprints as origins of transit trips to dental care services (or destinations). The study then computed transit-based origin-destination travel time matrices based on the detailed trip information, including in-vehicle and out-of-vehicle travel time. Accessibility scores were calculated by counting the number of dental clinics that can be reached within 60 min. Regression analysis was used to measure factors associated with accessibility scores to dental clinics participating in Medicaid. Residents in smaller regions spent longer travel time to dental clinics by public transit compared with those who resided in larger regions. Medicaid participants also faced longer travel time compared with the general population. Residents spent more than three-fourths of the time waiting for public transit and walking to clinics regardless of where they live and what type of insurance they have. Associations between sociodemographic factors and accessibility scores to dental clinics participating in the Medicaid dental program varied across regions. Disparities in dental care accessibility exist depending on the size of regions and Medicaid participation in Virginia. The disparities in transit-based access to dental clinics and a disproportionate amount of time spent waiting for public transit and walking to dental clinics could be improved through tailored interventions taking into account the sociodemographic and geographic characteristics of each region.

Urban Canopy Parameters’ Computation and Evaluation in an Indian Context Using Multi-Platform Remote Sensing Data

Urban Canopy Parameters (UCPs) are crucial for urban microclimate modeling; however, the scarce availability of precise UCP data in developing regions limits their application for urban climates. This study investigated the use of multi-platform remote sensing data viz. very high-resolution satellite (VHRS) optical stereo and Unmanned Aerial Vehicle (UAV) datasets for the computation of UCPs in high-density urban scenarios in India, with varied development characteristics. The results demonstrated high accuracy in terms of building height and footprint extraction from both datasets, key inputs for UCP computation. However, UCPs from UAV data have displayed relatively high accuracy for building footprints (86%), building height (RMSE ~ 0.05 m), and land use/land cover classification (90%). Performance evaluation of computed UCPs against a 3D reference geodatabase showed high prediction accuracy for most UCPs, with overall biases, mean absolute error, and root-mean-square error values significantly better than 1 m, with strong correlation (0.8–0.9). It was concluded that VHRS optical stereo and UAV datasets offer a secure, reliable, and accurate solution for UCP computation in urban areas, particularly in developing regions. These findings have significant implications for urban climate research and the sustainable development of rapidly urbanizing areas facing resource and policy constraints.

3D Reconstruction of Geometries for Urban Areas Supported by Computer Vision or Procedural Generations

This work presents a numerical mesh generation method for 3D urban scenes that could be easily converted into any 3D format, different from most implementations which are limited to specific environments in their applicability. The building models have shaped roofs and faces with static colors, combining the buildings with a ground grid. The building generation uses geographic positions and shape names, which can be extracted from OpenStreetMap. Additional steps, like a computer vision method, can be integrated into the generation optionally to improve the quality of the model, although this is highly time-consuming. Its function is to classify unknown roof shapes from satellite images with adequate resolution. The generation can also use custom geographic information. This aspect was tested using information created by procedural processes. The method was validated by results generated for many realistic scenarios with multiple building entities, comparing the results between using computer vision and not. The generated models were attempted to be rendered under Graphics Library Transmission Format and Unity Engine. In future work, a polygon-covering algorithm needs to be completed to process the building footprints more effectively, and a solution is required for the missing height values in OpenStreetMap.

An automated system for 2D building detection from UAV-based geospatial datasets

The focus of this manuscript is on integrating optical images and laser point clouds carried on low-cost UAVs to create an automated system capable of generating urban city models. After pre-processing both datasets, we co-registered both datasets using the DLT transformation model. We estimated structure heights from the LiDAR dataset through a progressive morphological filter followed by removing bare ground. Unsupervised and supervised image classification techniques were applied to a six-band image created from the optical and LiDAR datasets. After finding building footprints, we traced their edges, outlined their borderlines, and identified their geometric boundaries through several image processing and rule-based feature identification algorithms. Comparison between manually digitized and automatically extracted buildings showed a detection rate of about 92.3 % with an average of 7.4 % falsely identified areas with the six-band image in contrast to classifying only the RGB image that detected about 63.2 % of the building pixels with 25.3 % pixels incorrectly identified. Moreover, our building detection rate with the 6-band image was superior to that attained by performing traditional image segmentation for only the LiDAR DEM. Shifts in the horizontal coordinates between corner points identified by a human operator and those detected by the proposed system were in the range of 10–15 cm. This is an improvement over traditional satellite and manned-aerial large mapping systems that have lower accuracies due to sensor limitations and platform altitude. These findings demonstrate the benefits of fusing multiple UAV remote sensing datasets over utilizing a single dataset for urban area mapping and 3D city modeling.

Semantic Segmentation Uncertainty Assessment of Different U-net Architectures for Extracting Building Footprints

Abstract. Automatic extraction of building footprints from aerial and space imageries has been found ever increasing importance in urban planning, disaster management, and environmental monitoring. However, achieving accurate building footprint extraction poses significant challenges due to diverse building characteristics and their similarities to their background elements. While conventional methods in building footprint extraction have mainly relied on image processing techniques, recent advancements in deep learning, particularly semantic segmentation algorithms like U-Net, have shown promise in addressing these challenges through machine learning. This study explores different depths of the U-Net model for building footprint extraction, aiming to identify the optimum architecture while investigating the semantic uncertainty of the building footprint extraction. Utilizing aerial imagery from cities including Berlin, Paris, Chicago, and Zurich, collected from Google Maps and OpenStreetMap (OSM) data, five U-Net models have been compared with varying depths. In addition, the impact of dataset sizes and learning rates on model performance has been investigated. Results confirmed that the U-Net-32-1024 model achieves the highest intersection over union (IoU), Accuracy, and F1-score. Moreover, increasing the training dataset size leads to significant improvements in model performance with IoU, Accuracy and F1-score reaching their values of 73.73%, 88.65% and 88.53%. However, challenges remain in accurately delineating buildings in dense urban areas. Nonetheless, our findings demonstrated the effectiveness of U-Net models in building footprint extraction.

Spatial configuration of warehouses of different sizes in the City of Cape Town municipality

Background: Various metropolitan areas have become key centres for logistics activities, leading to a significant increase in the number of warehouses. However, there is a gap in the literature regarding the locational patterns of warehouses of different sizes in Africa.Objectives: To analyse the locational patterns of warehouses of different sizes within the City of Cape Town municipality, South Africa. The analysis focuses on the main factors influencing the placement of warehouses: transport infrastructure, land availability, proximity to customers and land-use zoning.Method: The study categorised warehouses based on their building footprint sizes, using the classifications of xx-small, x-small, small, medium, x-large and mega. A total of 396 warehouses were categorised accordingly. Proportional symbol mapping was then conducted in ArcGIS 10.1, assigning each warehouse type a specific weighting.Results: The study found that the areas surrounding the Port of Cape Town predominantly accommodated x-small warehouses. A diverse concentration of warehouses was identified along the railway, near the junctions of regional and national roads and within the areas enclosed by these roads. In addition, warehouses of diverse sizes were primarily situated in the main industrial zones of the municipality.Conclusion: The City of Cape Town municipality is encouraged to promote the consolidation of particularly large warehouses in strategic locations. These include industrial areas not geographically near the Port of Cape Town but with railway access to the port.Contribution: The study will contribute towards enhancing the sustainability and efficiency of logistics processes within the City of Cape Town.

Mapping refugee populations at high resolution by unlocking humanitarian administrative data

BackgroundInforming local decision-making, improving service delivery and designing household surveys require having access to high-spatial resolution mapping of the targeted population. However, this detailed spatial information remains unavailable for specific population subgroups, such as refugees, a vulnerable group that would significantly benefit from focused interventions. Given the continuous increase in the number of refugees, reaching an all-time high of 35.3 million people in 2022, it is imperative to develop models that can accurately inform about their spatial locations, enabling better and more tailored assistance.MethodsWe leverage routinely collected registration data on refugees and combine it with high-resolution population maps, satellite imagery derived settlement maps and other spatial covariates to disaggregate observed refugee totals into 100-m grid cells. We suggest a deterministic grid cell allocation inside monitored refugee sites based on building count and a random-forest-derived grid cell allocation outside refugee sites based on geolocating the textual geographic information in the refugee register and on high-resolution population mapping. We test the method in Cameroon using the registration database monitored by the United Nations High Commissioner for Refugees.ResultsUsing OpenStreetMap, 83% of the manually inputted information in the registration database could be geolocated. The building footprint layer derived from satellite imagery by Ecopia AI offers extensive coverage within monitored refugee sites, although manual digitization was still required in rapidly evolving settings. The high-resolution mapping of refugees on a 100-m grid basis provides an unparalleled level of spatial detail, enabling valuable geospatial insights for informed local decision-making.ConclusionsGathering information on forcibly displaced persons in sparse data-setting environment can quickly become very costly. Therefore, it is critical to gain the most knowledge from operational data that is frequently collected, such as registration databases. Integrating it with ancillary information derived from satellite imagery paves the way for obtaining more timely and spatially precise information to better deliver services and enhance sampling frame for target data collection exercises that further improves the quality of information on people in need.

Transforming satellite imagery into vector maps using modified GANs

Vector maps find widespread utility across diverse domains due to their capacity to not only store but also represent discrete data boundaries such as building footprints, disaster impact analysis, digitization, urban planning, location points, transport links, and more. Although extensive research exists on identifying building footprints and road types from satellite imagery, the generation of vector maps from such imagery remains an area with limited exploration. Furthermore, conventional map generation techniques rely on labor-intensive manual feature extraction or rule-based approaches, which impose inherent limitations. To surmount these limitations, we propose a novel method called HPix, which utilizes modified Generative Adversarial Networks (GANs) to generate vector tile map from satellite images. HPix incorporates two hierarchical frameworks: one operating at the global level and the other at the local level, resulting in a comprehensive model. Through empirical evaluations, our proposed approach showcases its effectiveness in producing highly accurate and visually captivating vector tile maps derived from satellite images. We achieved a pixel-level accuracy of 61.04% and an SSIM score of 0.75, outperforming all other existing methods. We further extend our study’s application to include mapping of road intersections and building footprints cluster based on their area. We also show usability of our proposed architecture as a general-purpose solutions in other tasks like edges-to-photo, BW-to-color, or labels-to-street scene. GitHub:https://github.com/aditya-taparia/Satellite-Image-to-Vector-Map.

Maintenance, operational costs, and plant performance in three extensive green roof designs from Northwest México

Green roofs (GR) are nature-based solutions for climate change adaptation and mitigation in buildings and cities. However, few GR maintenance, installation, and challenge-solving guidelines are available for semiarid climates. We present how three case-study types of GR (urban gardens (UGR), native gardens (NGR), and wetland roofs (WR)) varied in installation costs, irrigation volumes, maintenance times, vegetation cover, survival, biomass and yield, in Ensenada, México, from 2022 to 2023. In UGR, nine edible species were cultivated to yield produce; in NGR, six native plants were planted, and four were grown in WR. UGR installation costs were 31 % and 21 % lower, but maintenance costs were four and 1.5 times higher than NGR and WR, respectively. UGR consumed 17 % more water annually than NGR and 42 % less than WR. Likewise, UGR demanded 94 and 92 % more annual maintenance time than NGR and WR. WR presented the highest survival, height, cover, and biomass values, followed by NGR (except for biomass) and UGR. Our results point to different GR solutions for distinct types of applications and users, demonstrating their versatility in design. All GR categories are suitable to reduce the ecological footprint of buildings; however, we emphasize that each GR design involves different economic, time, and challenge-solving commitments that should be considered before installation and operation.

Enhanced building footprint extraction from satellite imagery using Mask R-CNN and PointRend

Enhanced building footprint extraction from satellite imagery using Mask R-CNN and PointRend

Carbon footprint of a sports hall building – discrepancies between reference buildings and the actual

Carbon footprint of a sports hall building – discrepancies between reference buildings and the actual