The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

Motivation

The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data has been periodically included in the dataset until the end of the COVID-19 pandemic.

We stopped updating the dataset after December 2022. Previous files have been fixed after a thorough sanity check.

License

See LICENSE.txt

Disclaimer

The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.

Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted. Fields origin or destination are empty when no airport could be found.

Aircraft information come from the OpenSky aircraft database. Fields typecode and registration are empty when the aircraft is not present in the database.

Description of the dataset

One file per month is provided as a csv file with the following features:

callsign: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)

number: the commercial number of the flight, when available (the matching with the callsign comes from public open API); this field may not be very reliable;

icao24: the transponder unique identification number;

registration: the aircraft tail number (when available);

typecode: the aircraft model type (when available);

origin: a four letter code for the origin airport of the flight (when available);

destination: a four letter code for the destination airport of the flight (when available);

firstseen: the UTC timestamp of the first message received by the OpenSky Network;

lastseen: the UTC timestamp of the last message received by the OpenSky Network;

day: the UTC day of the last message received by the OpenSky Network;

latitude_1, longitude_1, altitude_1: the first detected position of the aircraft;

latitude_2, longitude_2, altitude_2: the last detected position of the aircraft.

Examples

Possible visualisations and a more detailed description of the data are available at the following page:

Credit

If you use this dataset, please cite:

Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders "Crowdsourced air traffic data from the OpenSky Network 2019–2020" Earth System Science Data 13(2), 2021 https://doi.org/10.5194/essd-13-357-2021

Russia Number of Flights: Domestic data was reported at 67,658.000 Number in Feb 2022. This records a decrease from the previous number of 71,658.000 Number for Jan 2022. Russia Number of Flights: Domestic data is updated monthly, averaging 55,400.000 Number from Jan 2010 (Median) to Feb 2022, with 146 observations. The data reached an all-time high of 127,409.000 Number in Jul 2021 and a record low of 27,413.000 Number in Feb 2010. Russia Number of Flights: Domestic data remains active status in CEIC and is reported by Federal Agency for Air Transport. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TE003: Airlines Statistics: Number of Airlines, Aircrafts, Airports and Flights. [COVID-19-IMPACT]

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately *** billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below **** billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for ********* of the global total.

Explore match day statistics of every game and every team during the 2021-2022 season of the English Premier League!

Data includes data, teams, referee, and stats by home and away side such as fouls, shots, cards, and more! Also included is a dataset of the weekly rankings for the season.

The 2021–22 Premier League was the 30th season of the Premier League, the top English professional league for association football clubs since its establishment in 1992, and the 123rd season of top-flight English football overall. The start and end dates for the season were released on 25 March 2021, and the fixtures were released on 16 June 2021.

Manchester City successfully defended their title, securing a sixth Premier League title and eighth English league title overall on the final day of the season; it was also the club's fourth title in the last five seasons.

The data was collected from the official website of the Premier League. I then cleaned the data using google sheets

This dataset contains georeferenced InSAR-derived water level change maps for Delta-X flight lines acquired during the spring (2021-03-27 to 2021-04-18) and fall (2021-09-03 to 2021-09-13) deployments. Water-level change observations are provided throughout wetlands of the Atchafalaya and Terrebonne Basins, in Southern Louisiana, USA, within the Mississippi River Delta (MRD). The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. Water surface elevations were measured on multiple flights at 30-minute intervals. There are three types of gridded products available: temporalcoherence (which provide an index measuring quality of phase unwrapping ranging from 0 (poor) to 1 (correctly unwrapped)), waterlevelchange in centimeters (which provide cumulative changes in water levels at approximately 30-minute intervals), and waterlevelchange_ramp in centimeters (which provide a 2-dimensional linear trend in water-level estimates not related to changing water levels). The water-level change maps were estimated using the phase unwrapping corrected interferograms generated for nearest-neighbor (NN), NN+1, and NN+2 pairs for data acquired within a single flight (one day). This analysis was done for all flight lines. Water level changes are relative to the first sampling flight for that study area. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. A series of quality assurance masks of troposphere-induced phase delay regions were generated for all SAR acquisition dates using a weather feature matching algorithm.

The data are UAV (Unmanned Aerial Vehicle) and individual tree ground measurements collected from 2 citrus rootstock trials at the U.S. Horticultural Research Laboratory Picos Road farm site, Ft. Pierce, Florida, USA located at 27.437115254946757, -80.42786069428246. The trees in both trials were Valencia sweet orange scion grafted onto various rootstock selections and varieties. The trials are designated as Valencia 5-16 and Valencia 17-28, which indicate the row numbers used for each trial. Valencia 5-16 includes 648 trees and Valencia 17-28 includes 643 trees. The ground data was taken for the 5-16 and 17-28 trials in 2020 and 2021, respectively. The UAV images were taken twice the same day, 5/12/2021, once each under partially sunny (images 27-176) and overcast conditions (images 177-327). A single flight of rows 5-28 for each condition captured both trials. Some of the images under the partially sunny condition show tree shadows when the sun was not obscured behind a cloud, whereas the images under the overcast condition flight have uniform lighting and no sun shadows. Each image is notated to designate the flight condition. For example, the image labeled DJI_0033_R5-R28_Valencia_sunny.JPG was taken from the partially sunny flight and the image labeled DJI_0183_R5-R28_Valencia_overcast.JPG was taken from the overcast flight. The UAV images were taken using a DJI Phantom 4 Pro drone using a side-overlap of 80% and a forward-overlap of 80% of the flight lines. The images are suitable for orthorectification. The images were red-green-blue (sRGB) in a 3:2 format with 5472 x 3648 pixels. The dataset is included in one folder that contains 305 files – 301 image files, 2 Excel spreadsheets (one for each trial) that contain the planting plan and ground measures, and 2 images with the rows and tree spaces labeled. The 2 labeled images are composite images constructed from the 150 images from the overcast set and were created to label rows and tree space numbers. The composite image is useful for general orientation and matching the individual trees to the ground data and other post-processing image analyses.

Passengers enplaned and deplaned at Canadian airports, annual.

This dataset contains data related to Air Traffic Management hotspots. Hotspots are created in the European airspaces when capacity for some pieces of airspace are foreseen to be infringed due to weather, congestion, strikes, etc. This anonymised dataset records around 5900 hotspots happening at 22 major European airports. These hotspots are generated through a simulator called Mercury that is fed with real data (in particular, real capacity reduction that happened in Europe for over a year, schedules etc) and simulates a day of operation, randomising events like delays, cancellation etc. More details on mercury can be found here [1] and [2]. The data, anonymised in terms of airports and airlines, is a dictionary which is structured as follows: - the top level key is the id of the airport, the value is list a of all regulations available for this airport. - each item of the list is a dictionary, with keys: -- 'slot_times': list of all slots available to flights for this hotspot/regulation, in minutes since midnight. -- 'etas': list of initial estimated arrival times of flights involved in the regulation, in minutes since midnight. -- 'flight_ids': list of flight ids (in the same order than etas) -- 'cost_vectors': list of cost vectors. Each item is a list itself, of length equal to the slot_times list. Each element of that list is the estimated cost that the airline owning the flight would incur, were the flight be assigned to this slot, in terms of: maintenance, crew, rebooking fees, market value loss, and curfew infringement, in 2014 euros. This cost is computed within the Mercury model and is based on [3]. -- 'airlines_flights': dictionary whose keys are airline ids and values are lists of ids of flights owned by the airline. [1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600 [2] G. Gurtner, L. Delgado, and D.Valput, “An agent-based model for air transportation to capture network effects in assessing delay management mechanisms”, Transportation Research Part C: emerging Technologies, 2021. Pre-print available here: https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms [3] A. J. Cook and G. Tanner, “European airline delay cost reference values - updated and extended values (Version 4.1),” University of Westminster, London, 2015a

The University of Connecticut and the U.S. Geological Survey (USGS) collected low-altitude (30-50 m above ground level) airborne visible-light imagery data via a quadcopter, small unoccupied aircraft system (UAS or ‘drone’) deployed along two tributary confluence locations within the Housatonic River: Mill Brook (latitude: 42°52’18” N, longitude: 73°21’48” W) and Furnace Brook (latitude: 41°49’16” N, longitude: 73°22’17” W). Both tributary confluence sites serve as critical summer thermal refuge for cold water-adapted poikilotherms. The objectives for this data collection included the creation of high-resolution orthomosaic images of the two tributary confluences to infer bank and instream structures and mixing processes at the tributary confluences. Detailed site-scale maps such as these are important tools for managers and researchers aiming to protect and conserve populations at risk. The UAS (Mavic 2 Zoom, DJI Enterprises) was flown several times per day, at wind speeds below 10 mph, capturing RGB imagery from March 24-25, 2021. The UAV flights collected single RGB JPG images at 30-50m above ground level using the double-grid flight pattern on the third-party app Pix4D Capture (https://www.pix4d.com/product/pix4dcapture). The images were stitched automatically into several orthomosaic images using Agisoft Metashape (Agisoft LLC, St. Petersburg, Russia) software as described in the ‘processed_data’ subfolders of this data release. Structure from Motion techniques were also applied to the visual imagery to derive time-specific, digital surface models (DSM) of the exposed banks and some subsurface features.