Building Footprints Research Articles

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

A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data.

Building height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China's Gaofen-7 satellite (GF-7). In this study, we developed a method combining photogrammetry and deep learning to extract building height using GF-7 data in the rural area of Pingquan in northern China. The deep learning model DELaMa was proposed for digital surface model (DSM) editing based on the Large Mask Inpainting (LaMa) architecture. It not only preserves topographic details but also reasonably predicts the topography inside the building mask. The percentile value of the normalized digital surface model (nDSM) in the building footprint was taken as the building height. The extracted building heights in the study area are highly consistent with the reference building heights measured from the ICESat-2 LiDAR point cloud, with an R2 of 0.83, an MAE of 1.81 m and an RMSE of 2.13 m for all validation buildings. Overall, the proposed method in this paper helps to promote the use of satellite data in large-scale building height surveys, especially in rural areas.

Conservation threats from tourism land grabs and greenwash

We report a new threat to conservation, namely land grabs by large tourism developers inside public protected areas. Locally, these tourism land grabs damage national parks through building footprints and access corridors, bringing habitat fragmentation, noise, light, roadkill, fire risk, and invasive plant and animal species and pathogens. They also create negative impacts on social equity and regional economies. The global tourism industry now perceives private development in public national parks as a mechanism to profit from land speculation, rather than merely monopoly provision of visitor services. Investment funds now use tourism, often with socialwashing components, as a political lever for land grabs. International “nature positive” marketing by tourism industry associations and multilateral tourism advocacy organisations is greenwash: it lacks substance, and aims to coopt conservation organisations.

Variability in Heating Demand Predictions: A Comparative Study of PHPP and Mc001-2022 in Existing Residential Buildings

The construction industry is a key driver of environmental change due to its extensive use of resources and high emissions, thus significantly burdening global efforts towards sustainable development targets. A large portion of the environmental footprint of buildings results from the energy required to sustain indoor comfort levels. Thus, enhancing the energy efficiency of existing buildings becomes critical in reducing their environmental impact. This study explores the impact of thermal performance improvements on the heating demand, employing numerical modeling and two energy performance methodologies, PHPP and Mc001-2022, across various climatic datasets and case studies in Romania. The results show substantial variability in heating demand predictions: Mc001-2022 predicts up to 27.2% higher continuous heating demands and 21.0% higher intermittent demands compared to PHPP in one case study. In the second case study, the differences range from 8.1% higher to 6.9% lower for continuous heating and from 3.3% higher to 9.9% lower for intermittent heating, depending on the scenario. These findings underscore the importance of the methodological choice and localized climatic data in heating demand assessments, highlighting the need for a tailored, context-specific approach to energy performance assessment, integrating multiple energy efficiency measures suited to the unique characteristics of each building.

Towards a more realistic estimation of urban land take by combining cadastral parcels and building footprints

Despite its importance as a research issue, a widely accepted methodology of estimating land take resulting from urbanization has not yet been reached. Accurate geospatial datasets are currently available at a European and global level; however, different methods of quantifying urban land take could lead to diverse outputs, potentially resulting in underestimation. This can be alarming as encroaching urban sprawl is emerging as a major environmental challenge by destroying natural habitats, consuming productive agricultural land, and contributing to climate change by increasing energy demands. To address this knowledge gap, we propose an estimation of urban land take that combines cadastral parcels and building footprints. Land parcels can be considered as a suitable minimum mapping unit as they are directly related to the spatial level where economic decisions on land use conversion are made. The proposed geospatial method is compared to methods that depend on datasets of High Resolution Layer Imperviousness, Global Building Footprints (alone), Corine Land Cover, Urban Atlas, and Global Human Settlement Layer. The method is exemplified in case studies in Greece, specifically: (a) two islands of the South Aegean Region (Mykonos and Thera), that are heavily impacted by tourism development and sprawl and (b) a peri-urban zone (Thermaikos-Michaniona) within the metropolitan area of Thessaloniki, impacted by intense suburbanization. Results show that urban land consumes important shares of available land since the mid-20th century, this fact highlighting the dynamic encroachment of urban development. Calculation shows that other methods could underestimate urbanized areas by up to 80%–90%. In the discussion section, the advantages of shifting the focus from the pixel to the parcel are further justified, while explicit links to spatial planning policies for sprawl containment are drawn. Such policies could be informed by more realistic estimations of urban land take, in order to proceed with strategic and regulatory measures to support the ‘no net land take’ policy of Europe.

Investigating the influence of urban morphology on pluvial flooding: Insights from urban catchments in England (UK)

As climate change intensifies, cities globally are experiencing more severe rainfall and frequent pluvial floods. Urban expansion is altering the permeability of the land, thus increasing the risk of flooding. This study investigates the impact of urban morphology on pluvial floodwater distribution in 15 urban catchments across England, UK, to provide an analysis of how urban morphology influences flood magnitude. Using a cellular automata-based model, pluvial flood simulations were conducted for catchments characterized by diverse urban morphologies. Then a series of machine learning models were adopted to reveal the relationships between the morphological characteristics of urban configurations (e.g., building footprints, impervious surfaces, street network, topography) and pluvial flooding. These models were used to identify and quantify the effects of key urban morphological indicators on pluvial flooding. The results indicate that, although the total area of impervious surfaces plays the most significant role in floodwater distribution, the edge density (ED) of building footprints and impervious surfaces also influences this process. Synthetic experiments with an exemplary urban fabric show that decreasing “ED of building footprint” and increasing “ED of impervious surface” can mitigate flood volume by up to 6.3 % at 100 % drainage efficiency and 7.8 % at 50 % efficiency. The results of this study are anticipated to aid urban planners and policymakers in developing strategies for implementing flood-resilient cities.

Simplification and aggregation of vector building footprints using deep learning models

Simplification and aggregation of vector building footprints using deep learning models

High-resolution population maps derived from Sentinel-1 and Sentinel-2

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed Popcorn, a population mapping method whose only inputs are free, globally available satellite images from Sentinel-1 and Sentinel-2; and a small number of aggregate population counts over coarse census districts for calibration. Despite the minimal data requirements our approach surpasses the mapping accuracy of existing schemes, including several that rely on building footprints derived from high-resolution imagery. E.g., we were able to produce population maps for Rwanda with 100m GSD based on less than 400 regional census counts. In Kigali, those maps reach an R2 score of 66% w.r.t. a ground truth reference map, with an average error of only ±10 inhabitants/ha. Conveniently, Popcorn retrieves explicit maps of built-up areas and local building occupancy rates, making the mapping process interpretable and offering additional insights, for instance about the distribution of built-up, but unpopulated areas, e.g., industrial warehouses. With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns. Project page: https://popcorn-population.github.io/.

Automatic high-detailed building reconstruction workflow for urban microscale simulations

Reconstructing urban scenarios for computational fluid dynamics simulations typically requires significant manual effort, especially when higher geometrical details are required. To address this issue, we present a workflow to automatically reconstruct buildings in three levels of detail (LoDs): LoD1.2, LoD1.3, and LoD2.2, tailored to urban microscale simulations. The workflow uses a combination of building footprints and a point cloud to segment roof planes, create partitions, optimise planes, and finally assemble roof planes into 3D building models. Reconstructed buildings are seamlessly integrated into the terrain together with different surface layers such as water, low vegetation, and paved surfaces. Apart from three general LoDs, building footprints can be simplified as a part of the 2D generalisation; additionally, smaller surfaces such as chimneys and ventilation shafts can be removed using a graph-cut optimisation. The integrated geometry validator can report on validity of building models, such as watertightness, manifoldness, or occurrences of self-intersections. In the case of invalid geometries, we can generate an approximation: geometry repair the with alpha wrapping algorithm, or reconstruction in lower LoD. We tested our implementation on two different real-world datasets — one in The Netherlands, and another one in the USA. The results showed that 95% (Dutch dataset) and 90% (US dataset) buildings were valid according to the ISO 19107 standard. Generated grids showed satisfactory quality as we observed monotonous convergence in simulations with grid convergence indices up to 3.8% for pressure and velocity variables. These results indicate that the workflow is suitable for typical urban microscale simulations.

Correction: Kurian et al. Estimation of Carbon Footprint of Residential Building in Warm Humid Climate of India through BIM. Energies 2021, 14, 4237

There was an error in the original publication [...]

Inferring building height from footprint morphology data

As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications.