All existing skyscrapers in the world. For this list a skyscraper is defined as a building with a height of 500 feet or more not including spires, antennas or elevator boxes. Churches are included.

Dataset

This dataset was created by Mussaddiq Nawaz

Contents

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

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Skyscraper Facade Bolt-Check Drone Market Outlook

According to the latest research conducted in 2025, the global skyscraper facade bolt-check drone market size is valued at USD 1.14 billion in 2024, reflecting robust adoption across high-rise construction and maintenance sectors. The market is projected to expand at a CAGR of 17.2% from 2025 to 2033, reaching a forecasted size of USD 4.07 billion by 2033. This impressive growth trajectory is driven by the increasing demand for safe, efficient, and cost-effective facade inspection solutions, particularly in urban environments where skyscraper construction continues to surge and safety regulations are becoming more stringent.

One of the primary growth factors propelling the skyscraper facade bolt-check drone market is the ongoing global urbanization trend, which is fueling the construction of taller and more complex buildings. As cities strive to accommodate growing populations and maximize limited land resources, the proliferation of skyscrapers has become a hallmark of modern urban landscapes. This vertical expansion brings heightened safety concerns, especially regarding the structural integrity of facades and their critical components such as bolts and fasteners. Traditional inspection methods, which are labor-intensive and often hazardous, are being rapidly replaced by drone-based solutions that offer unparalleled accessibility, precision, and data collection capabilities. This shift not only enhances worker safety but also enables more frequent and thorough inspections, thereby reducing the risk of catastrophic facade failures.

Technological advancements in drone hardware and software are also catalyzing market growth. The integration of high-resolution cameras, advanced sensors, and artificial intelligence (AI) algorithms allows drones to detect minute defects, corrosion, or loosening of bolts that might otherwise go unnoticed. Furthermore, the development of autonomous and semi-autonomous navigation systems is making it possible for drones to operate in challenging environments with minimal human intervention. These innovations are particularly valuable for skyscraper facade bolt-check applications, where precision and reliability are paramount. The continuous evolution of drone technology, coupled with falling hardware costs and improved data analytics, is expected to further accelerate the adoption of these solutions among construction, maintenance, and facility management stakeholders.

The regulatory landscape is another significant driver of the skyscraper facade bolt-check drone market. Governments and industry bodies across the globe are enacting stricter safety and maintenance standards for high-rise buildings, often mandating regular facade inspections. These regulations are creating a favorable environment for the deployment of drone-based inspection systems, which offer verifiable, auditable, and easily shareable inspection data. In addition, insurers and property owners are increasingly recognizing the value of drone inspections for risk mitigation, leading to wider acceptance and integration of these technologies into routine building management practices. As regulations continue to evolve in response to urban development and safety concerns, the demand for reliable and efficient bolt-check drone solutions is poised for sustained growth.

Regionally, the Asia Pacific market is emerging as a dominant force, driven by rapid urbanization, extensive infrastructure investments, and the proliferation of skyscrapers in cities like Shanghai, Singapore, and Dubai. North America and Europe are also significant contributors, benefiting from mature construction industries and early adoption of advanced inspection technologies. Meanwhile, the Middle East and Latin America are witnessing growing interest as urban centers expand and new high-rise projects are launched. Each region presents unique opportunities and challenges, shaped by local regulatory frameworks, technological readiness, and the pace of urban development. Overall, the global outlook for the skyscraper facade bolt-check drone market remains highly positive, with strong growth expected across all major regions.

This dataset includes many indexes of global cities. The variables of congestion level, skyscraper index, whether a city was bombed in WWII (World War II), and global cities’ population are key variables. (1) The congestion level data were collected from TOMTOM company. The congestion level data includes five indexes in 2004 which are “Congestion level”, “Morning peak Congestion level”, “Evening peak Congestion level”, “Highways Congestion level”, “Non-highways Congestion level”, and two indexes in 2020 which are “Time lost per year” and “Congestion level”. (2) The data of skyscraper index is calculated using the data of building height from the Council on Tall Buildings and Urban Habitat, from which we can obtain accurate data on the number of buildings taller than 150 m. With these data, we constructed an index of skyscrapers taller than 150 m in a city. A building receives a score of 1.5 if it is taller than 150 m and shorter than 200 m, 2.0 if it is between 200 m and 300 m, and so on. Then, we summed the scores for skyscrapers in the city as the “skyscraper index” of the city. (3) The data of whether a city was bombed in WWII is dummy variable, if the urban area of a city was bombed in WWII, it is 1, and 0 otherwise. The authors consulted various historical files and determined the value. (4) The data of global cities’ population, as well as the area and density of the city, are on the city-level, and were collected from the website of the cities or countries’ statistics department. These indicators are good measures of the level of congestion, urban spatial structure, instrumental variable (IV) for urban spatial structure, and urban population in global cities, and can be reused in other analysis.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

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The development plan (BPL) contains the legally binding determinations for the urban planning order. In principle, the development plan must be developed from the land use plan. The available data is the development plan “Beim Hochhaus” of the municipality of Warthausen from XPlanung 5.0. Description: WED.

This page provides data on the performance of the Register a high-rise residential building service. The data is updated every 3 months.

WFS service of the development plan “Beim Hochhaus” of the municipality of Warthausen from XPlanung 5.0. Description: WED.

Contains demographic profile information from the Australian Bureau of Statistics (ABS) 2016 Census of Population and Housing. Data has been aggregated based on residents living in buildings with four or more storeys.

This data has been derived from the ABS Census TableBuilder online data tool (http://www.abs.gov.au/websitedbs/D3310114.nsf/Home/2016%20TableBuilder) by Australian Bureau of Statistics, used under CC 4.0.

Uruguay Housing Construction Cost Index: High-Rise Towers with Public Contribution data was reported at 109.959 Jun2023=100 in Mar 2025. This records a decrease from the previous number of 110.061 Jun2023=100 for Feb 2025. Uruguay Housing Construction Cost Index: High-Rise Towers with Public Contribution data is updated monthly, averaging 107.287 Jun2023=100 from Jun 2023 (Median) to Mar 2025, with 22 observations. The data reached an all-time high of 110.061 Jun2023=100 in Feb 2025 and a record low of 100.000 Jun2023=100 in Jun 2023. Uruguay Housing Construction Cost Index: High-Rise Towers with Public Contribution data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Uruguay – Table UY.EA004: Housing Construction Cost Index.

Canada Construction Price Index: Residential: Apartment: High Rise data was reported at 106.100 2023=100 in Mar 2025. This records an increase from the previous number of 105.700 2023=100 for Dec 2024. Canada Construction Price Index: Residential: Apartment: High Rise data is updated quarterly, averaging 46.200 2023=100 from Mar 1986 (Median) to Mar 2025, with 157 observations. The data reached an all-time high of 106.100 2023=100 in Mar 2025 and a record low of 26.700 2023=100 in Mar 1986. Canada Construction Price Index: Residential: Apartment: High Rise data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.EA011: Construction Price Index: 2023=100.

This dataset provides information about the number of properties, residents, and average property values for High Rise Drive cross streets in Lyles, TN.

Abstract

During six consecutive autumn seasons we registered birds that were attracted to an illuminated 41-storey building in Bonn, Germany. Casualties on the ground were disoriented by the light and in most cases collided with the building. All-night observations with numbers of casualties, effective light sources, moon, and weather parameters registered hourly allowed for analyses of the role of these factors for the attraction and disorientation of numerous migratory birds. As expected, the conspicuous façade illumination was responsible for many casualties (fatal or non-fatal). Additionally, the illuminated roof logos and even faint light sources like the emergency lights were attractive and led to casualties. Moon and rain were negatively correlated with casualties, but there was no clear correlation with other weather parameters. Turning off lights was key, but effects of other ex post mitigation measures were limited: shutters were not originally intended for the attenuation of light emissions, control technology was insufficient, and there was a lack of willingness of the building owner to reduce light emissions consistently, even during core bird migration periods. Conservation recommendations are derived from this case study.

Vögel und der „Postturm“ in Bonn: Eine Fallstudie zur Lichtverschmutzung

In sechs aufeinanderfolgenden Herbstsaisons erfassten wir die Vögel, die an ein beleuchtetes, 41stöckiges Hochhaus in Bonn (Deutschland), den sog. „Postturm “ angelockt wurden. Die Opfer am Boden waren aufgrund der Beleuchtung desorientiert und in den meisten Fällen mit dem Gebäude kollidiert. Basierend auf Beobachtungen während des gesamten Nachtverlaufes wurden die registrierten Opfer, die in Betrieb befindlichen Lichtquellen, Mond und Wettervariablen stundenweise dargestellt, um die Bedeutung dieser Faktoren für die Anlockung und Desorientierung zahlreicher Zugvögel zu analysieren. Die auffällige Fassadenbeleuchtung war erwartungsgemäß für die meisten der Todesfälle und Verletzungen verantwortlich. Zusätzlich führten die beleuchteten Firmenlogos auf dem Dach und sogar schwache Lichtquellen wie die Notbeleuchtung zu Opfern durch Anlockung, auch bei ausgeschalteter Fassadenbeleuchtung. Mond und Regen korrelierten negativ mit den Opferzahlen, aber mit anderen Wettervariablen fehlten klare Korrelationen. Das Abschalten der Beleuchtung war ausschlaggebend, während andere nachträgliche Abhilfemaßnahmen wenig wirksam waren: Sonnenschutzlamellen waren ursprünglich beim Einbau nicht dafür konzipiert, Lichtabstrahlung zu reduzieren, die Steuerungstechnik war fehleranfällig und die Bereitschaft der Gebäudeeigentümerin, Lichtemissionen selbst während der Kernzeiten des Vogelzugs konsequent zu reduzieren, war begrenzt. Aus dieser Fallstudie werden Empfehlungen für Schutzmaßnahmen abgeleitet.

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This is the official repository of the CIM-WV dataset. For technical details, please refer to:Li, M., Yeh, A. G. & Xue, F. (2023). CIM-WV: A 2D semantic segmentation dataset of rich window view contents in high-rise, high-density Hong Kong based on photorealistic City Information Models. Urban Informatics, 1-24.This study was supported in part by the Department of Science and Technology of Guangdong Province (GDST) (2020B1212030009, 2023A1515010757) and the University of Hong Kong (203720465).Overview of CIM-WVThis paper presents a City Information Model (CIM)-generated Window View (CIM-WV) dataset comprising 2,000 annotated images collected in the high-rise, high-density urban areas of Hong Kong. 1) Window view images of CIM-WV depict diversified urban scenes of Hong Kong at different locations, elevations, and orientations2) The CIM-WV includes seven semantic labels, i.e., building, sky, vegetation, road, waterbody, vehicle, and terrain.In addition, we provide variants of DeepLab V3+ models trained on CIM-WV, real window view images, Google Earth CIM-generated window view images from New York, and Google Earth CIM-generated window view images from Singapore, respectively.You can modify the source code here to use the trained DeepLab V3+ models. Contribution1) CIM-WV is the first public CIM-generated photorealistic window view dataset with rich semantics. 2) Comparative analysis shows a more accurate window view assessment using deep learning from CIM-WV than deep transfer learning from ground-level views.3) For urban researchers and practitioners, our publicly accessible deep learning models trained on CIM-WV enable novel multi-source window view-based urban applications including precise real estate valuation, improvement of built environment, and window view-related urban analytics.Please cite our paper and dataset, if you find our work useful for your research and practices. Many thanks.For any inquiries, please feel free to contact Maosu at maosulee@connect.hku.hk or Dr. Frank at xuef@hku.hk.

HRHD-HK: A Benchmark Dataset of High-Rise and High-Density Urban Scenes for 3D Semantic Segmentation of Photogrammetric Point CloudsThis is the official repository of the HRHD-HK dataset. For technical details, please refer to:Li, M., Wu, Y., Yeh, A. G. O., & Xue, F. (2023). HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point cloud. Proceedings of 2023 IEEE International Conference on Image Processing Challenges and Workshops, 3714-3718. IEEE. https://doi.org/10.1109/ICIPC59416.2023.10328383Overview of HRHD-HKThis paper presents a benchmark dataset of high-rise high-density urban point clouds, namely High-Rise, High-Density urban scenes of Hong Kong (HRHD-HK) for 3D semantic segmentation.The semantic labels of HRHD-HK include 1) building, 2) vegetation, 3) road, 4) waterbody, 5) facility, 6) terrain, and 7) vehicle.Point clouds of HRHD-HK were collected in HK with two features, i.e., color and coordinates in the HK 1980 Grid system (EPSG:2326).HRHD-HK arranged in 150 tiles, contains approximately 273 million points, covering 9.375 km2.Each tile of point clouds was saved in the "ply" format with seven channels, i.e., x, y, z, red, green, blue, and label.HRHD-HK aims to supplement the existing benchmark datasets with Asian HRHD urban scenes as well as subtropical natural landscapes, such as sea, vegetation, and mountains.For any inquiries, please feel free to contact Maosu at maosulee@connect.hku.hk or Dr. Frank at xuef@hku.hk.Please cite our paper, if you find our work useful for your research.