Here is the list of Top 100 Skyscrapers of New York based on their Height. I scraped this data from wikipedia.

About Dataset:

The dataset contains Top 100 highest skyscrapers of New York along with their names, height, floors, year and address.

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.

The current dataset is prepared to feed ML algorithms for finding emergency exits in buildings. Emergency exits are the two doors on each floor that connect an air-lock space to the stairwell, and the pattern is quite similar in most high-rise buildings. This led to the selection of the following two attributes: 'the distance to the nearest door' and 'the distance to the nearest stairwell'. The datasets comprise manually labeled data with 1732 door samples from four high-rise building models, containing 1517 'non-Emergency Exit' and 215 'Emergency Exit', each with the two mentioned attributes.

There are thousands of pollution monitoring stations which are recording the data 24×7, the present research question is using this data to solve bring out a relationship between natural ventilation and air conditioning. Recently, WHO reported that 14 out of the top 15 most polluted cities are in India. Every year there is a loss of 6.2% to the global economy due to air pollution. The recent urban PM2.5 smog spread over the whole of north India covering about 50% of the country’s population. This event has been increasing the use of air purifiers and affecting the building energy performance. Most air purifiers (PM 10 and PM 2.5) are energy-intensive but are not always equipped with sensors. In commercial buildings, air purifiers are operated based on publicly relayed pollution information. The air pollutants that infiltrate into buildings are based on leaks, cracks, quality of building construction and pressure differences. Since indoor pollution levels are less than outdoor pollution levels, usage of air purifiers based on outdoor information leads to overperformance and hence energy wastage. Therefore, there is a need for optimization in sensitizing the performance of air purifiers at the building level. This study intends to assess the role of building airtightness and air purifier automation in lessening the air purifiers’ electricity consumption in urban areas. Transient building simulation tools do not account for infiltrated pollution levels directly. Virtually evaluating the energy savings through air purifier automation and the building’s airtightness would not be a straightforward assessment. The following paper uses EnergyPlus Energy Management System Class along with air pollution data monitored to model and simulate the Business-as-usual (BAU) and proposed Automation scenarios.