Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high-volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

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

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.

Passengers enplaned and deplaned at Canadian airports, annual.

Context

Have you taken a flight in the U.S. in the past 15 years? If so, then you are a part of monthly data that the U.S. Department of Transportation's TranStats service makes available on various metrics for 15 U.S. airlines and 30 major U.S airports. Their website unfortunately does not include a method for easily downloading and sharing files. Furthermore, the source is built in ASP.NET, so extracting the data is rather cumbersome. To allow easier community access to this rich source of information, I scraped the metrics for every airline / airport combination and stored them in separate CSV files.

Occasionally, an airline doesn't serve a certain airport, or it didn't serve it for the entire duration that the data collection period covers*. In those cases, the data either doesn't exist or is typically too sparse to be of much use. As such, I've only uploaded complete files for airports that an airline served for the entire uninterrupted duration of the collection period. For these files, there should be 174 time series points for one or more of the nine columns below. I recommend any of the files for American, Delta, or United Airlines for outstanding examples of complete and robust airline data.

* No data for Atlas Air exists, and Virgin America commenced service in 2007, so no folders for either airline are included.

Content

There are 13 airlines that have at least one complete dataset. Each airline's folder includes CSV file(s) for each airport that are complete as defined by the above criteria. I've double-checked the files, but if you find one that violates the criteria, please point it out. The file names have the format "AIRLINE-AIRPORT.csv", where both AIRLINE and AIRPORT are IATA codes. For a full listing of the airlines and airports that the codes correspond to, check out the airline_codes.csv or airport_codes.csv files that are included, or perform a lookup here. Note that the data in each airport file represents metrics for flights that originated at the airport.

Among the 13 airlines in data.zip, there are a total of 161 individual datasets. There are also two special folders included - airlines_all_airports.csv and airports_all_airlines.csv. The first contains datasets for each airline aggregated over all airports, while the second contains datasets for each airport aggregated over all airlines. To preview a sample dataset, check out all_airlines_all_airports.csv, which contains industry-wide data.

Each file includes the following metrics for each month from October 2002 to March 2017:

1. Date (YYYY-MM-DD): All dates are set to the first of the month. The day value is just a placeholder and has no significance.
2. ASM_Domestic: Available Seat-Miles in thousands (000s). Number of domestic flights * Number of seats on each flight
3. ASM_International*: Available Seat-Miles in thousands (000s). Number of international flights * Number of seats on each flight
4. Flights_Domestic
5. Flights_International*
6. Passengers_Domestic
7. Passengers_International*
8. RPM_Domestic: Revenue Passenger-Miles in thousands (000s). Number of domestic flights * Number of paying passengers
9. RPM_International*: Revenue Passenger-Miles in thousands (000s). Number of international flights * Number of paying passengers

* Frequently contains missing values

Acknowledgements

Thanks to the U.S. Department of Transportation for collecting this data every month and making it publicly available to us all.

Source: https://www.transtats.bts.gov/Data_Elements.aspx

Inspiration

The airline / airport datasets are perfect for practicing and/or testing time series forecasting with classic statistical models such as autoregressive integrated moving average (ARIMA), or modern deep learning techniques such as long short-term memory (LSTM) networks. The datasets typically show evidence of trends, seasonality, and noise, so modeling and accurate forecasting can be challenging, but still more tractable than time series problems possessing more stochastic elements, e.g. stocks, currencies, commodities, etc. The source releases new data each month, so feel free to check your models' performances against new data as it comes out. I will update the files here every 3 to 6 months depending on how things go.

A future plan is to build a SQLite database so a vast array of queries can be run against the data. The data in it its current time series format is not conducive for this, so coming up with a workable structure for the tables is the first step towards this goal. If you have any suggestions for how I can improve the data presentation, or anything that you would like me to add, please let me know. Looking forward to seeing the questions that we can answer together!

This dataset contains the records of all the flights in the Northern California TRACON. The data was provided by the aircraft noise abatement office (http://www.flyquietsfo.com/) of San Francisco International Airport. The data cover Jan-Mar 2006. It is organized by day and flight. Each record contains some information about the flight and a sequence of 3D position and estimated speed. This data contains thousands of trajectories that can be used for trajectory clustering. The data is used by the Aircraft Noise Abatement Office to analyze the trajectories of aircraft flying in and out SFO. The objective is to minimize the noise pollution due to aircraft in the San Francisco Bay Area The files have the extension "lt6" and are organized as follow, one file per day. line number & explaination 1 TRACK OPNUM (TRACK header word and operation number) 2 eventid (Corralation number) 3 trackstart date (in time since 1900, A8 version four year digit) 4 trackstart time HH:MM:SS 5 trackend time HH:MM:SS 6 airportid 7 ACID (FLIGHTNUM/TAILNUMBER) 8 owner name 9 aircrafttype 10 aircraft category 11 beacon 12 adflag 13 waypoint 14 other_port (dest/origin) 15 runwayname 16 min alt 17 max alt 18 min range 19 max range 20 Count of trackpoints (to follow) 21 x,y,z,v,t (all points is meters relative to MRP, velocity and time from start of track)

This project is about predicting if a flight will be delayed by over 15 minutes upon arrival, with Scikit-learn Decision Tree Classifier, using US flight data in 2022. Here is the URL of the dataset and variables description: https://www.transtats.bts.gov/DL_SelectFields.aspx?gnoyr_VQ=FGK&QO_fu146_anzr=b0-gvzr

Context The U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics tracks the on-time performance of domestic flights operated by large air carriers. This dataset is collected from the Bureau of Transportation Statistics, Govt. of the USA. This data is open-sourced under U.S. Govt. Works. I dowload 12 csv file that represents each month of 2022. This dataset contains all US domestic flights in 2022.

Description of Columns • Quarter Quarter (1-4) • Month Month • DayofMonth Day of Month • DayOfWeek Day of Week • FlightDate Date of the Flight • Marketing_Airline_Network Airline Identifier • OriginCityName Origin Airport, City Name • DestCityName Destination Airport, City Name • DepDelay Difference in minutes between scheduled and actual departure time. Early departures show negative numbers • ArrDelay Difference in minutes between scheduled and actual arrival time. Early arrivals show negative numbers • Cancelled Cancelled Flight (1=Yes) • Diverted Diverted Flight (1=Yes) • AirTime Flight Time, in Minutes • Distance Distance between airports (miles) • CarrierDelay Delay caused by the airline in minutes • WeatherDelay Delay caused by weather • NASDelay Delay caused by air system • SecurityDelay Delay caused by security reasons • LateAircraftDelay Delay caused as a result of another flight on the same aircraft delayed

Analysis of ‘New York City Airport Activity’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sveneschlbeck/new-york-city-airport-activity on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains randomly compiled data with detailed information about flights from New York City Airports in 2013 with parameters concerning the airports, the flights and the airlines involved.

Content

The table contains the following parameters:

- year: Year.
- month: Month.
- day: Day.
- dep_time: Departure time, in Eastern time zone.
- dep_delay: Departure delay, in minutes.
- arr_time: Arrival time, in the local time zone.
- arr_delay: Arrival delay, in minutes.
- carrier: Carrier, abbreviated.
- tailnum: Tail number of the airplane.
- flight: Flight number.
- origin: Flight origin, airport code.
- dest: Flight destination, airport code.
- air_time: Time in the air, in minutes.
- distance: Distance between the departure and arrival airports, in miles.
- hour: Scheduled departure hour.
- minute: Scheduled departure minute.

Analysis

Take a look at the notebook "nyc-flights" to get started on how to transform, analyse or visualize the data.

Data Source

Wickham H. 2014. nycflights13: Data about flights departing NYC in 2013. R package version 0.1.

--- Original source retains full ownership of the source dataset ---

This dataset includes externalities: weather, number of arrival flights, and day of the week. Weather data are collected from the National Weather Service’s NY-Central Park Area record, and flight arrival data are collected from the Bureau of Transportation Statistics.

Geospatial Dataset of GNSS Anomalies and Political Violence Events

Overview

The Geospatial Dataset of GNSS Anomalies and Political Violence Events is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database.

Dataset Files

The dataset consists of three CSV files:

1. Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv
• Description: Contains all grids and dates that had aircraft traffic during 2023.
• Number of Records: 6,777,228
• Purpose: Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.
2. Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv
• Description: A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.
• Number of Records: 718,237
• Purpose: Focuses on areas and times with GNSS anomalies for targeted analysis.
3. Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv
• Description: Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution.
• Number of Records: 25,770
• Purpose: Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization.

Data Fields: Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv

1. grid_id
• Description: Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.
• Format: String combining latitude and longitude (e.g., -10.0_-36.0).
2. day
• Description: Date of the recorded data.
• Format: YYYY-MM-DD (e.g., 2023-03-28).
3. geometry
• Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.
• Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).
4. flights
• Description: Number of aircraft flights that passed through the grid on that day.
• Format: Integer (e.g., 28).
5. GPS_jumps
• Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.
• Format: Integer (e.g., 1).
6. GPS_gaps
• Description: Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.
• Format: Integer (e.g., 0).
7. gaps_density
• Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.
• Format: Decimal (e.g., 0).
8. jumps_density
• Description: Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.
• Format: Decimal (e.g., 0.035714286).
9. event_id_cnty
• Description: ACLED event ID corresponding to political violence events in the grid on that day.
• Format: String (e.g., BRA69267).
10. disorder_type
• Description: Type of disorder as classified by ACLED (e.g., "Political violence").
• Format: String.
11. event_type
• Description: General category of the event according to ACLED (e.g., "Violence against civilians").
• Format: String.
12. sub_event_type
• Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").
• Format: String.
13. acled_count
• Description: Number of ACLED events in the grid on that day.
• Format: Integer (e.g., 1).
14. acled_flag
• Description: Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).
• Format: Integer (0 or 1).

Data Fields: Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv

The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below:

1. grid_id
• Description: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude.
• Format: String combining latitude and longitude (e.g., -0.5_-79.0).
2. year_month
• Description: Month and year of the aggregated data.
• Format: String in Mon-YY format (e.g., Jan-23).
3. geometry
• Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.
• Format: POLYGON((longitude latitude, ...))
  (e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))).
4. flights
• Description: Total number of aircraft flights that passed through the grid cell during the month.
• Format: Integer (e.g., 1230).
5. GPS_jumps
• Description: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month.
• Format: Integer (e.g., 13).
6. GPS_gaps
• Description: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month.
• Format: Integer (e.g., 0).
7. event_id_cnty
• Description: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month.
• Format: String (e.g., ECU3151;ECU3158;ECU3150).
8. disorder_type
• Description: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month.
• Format: String.
9. event_type
• Description: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests").
• Format: String.
10. sub_event_type

• Description: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash").
• Format: String.

1. acled_count

• Description: Total number of ACLED conflict events in the grid cell during the month.
• Format: Integer (e.g., 2).

1. acled_flag

• Description: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0.
• Format: Integer (0 or 1).

1. gaps_density

• Description: Monthly density of GNSS gaps, calculated as GPS_gaps / flights.
• Format: Decimal (e.g., 0.0).

1. jumps_density

• Description: Monthly density of GNSS jumps, calculated as GPS_jumps / flights.
• Format: Decimal (e.g., 0.0106).

Data Sources

• GNSS Anomalies Data:
• Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.
• GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).
• Political Violence Events Data:
• Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.

Temporal and Spatial Coverage

• Temporal Coverage:
• From January 1, 2023, to December 31, 2023.
• Daily records provide temporal granularity for time-series analysis.
• Spatial Coverage:
• Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.
• Each grid cell represents an area on Earth's surface, facilitating spatial

India All Scheduled Airlines: Domestic: Number of Flight data was reported at 102,319.000 Unit in Mar 2025. This records an increase from the previous number of 92,291.000 Unit for Feb 2025. India All Scheduled Airlines: Domestic: Number of Flight data is updated monthly, averaging 48,100.000 Unit from Apr 2001 (Median) to Mar 2025, with 288 observations. The data reached an all-time high of 102,319.000 Unit in Mar 2025 and a record low of 188.000 Unit in Apr 2020. India All Scheduled Airlines: Domestic: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

Description

SkySoft ATM created this dataset as a part of the Innosuisse MALAT project to enhance the quality of high-altitude wind nowcasting. The dataset contains the flight's track points that passed over European airspace over five weeks. In detail, it contains Mode-S data recordings for 35 separate days. Mode-S data is exchanged between Secondary Surveillance Radars (SSR) and the aircraft radar transponders and consists of position and wind information. SSRs rotate with a period of 4 seconds, setting the sampling time for these variables.

The total dataset contains over 33 million measurement points, corresponding to around 6.7 million points per week or roughly a million points per day. Planes record these points along their trajectory, and there were 61929 different flights recorded over the airspace during the whole period, which corresponds to around 1770 flights per day.

Total monthly number of passengers arriving to and departing from Heathrow Airport, including both international and domestic flights.

India All Scheduled Airlines: International: Number of Flight data was reported at 18,502.000 Unit in Mar 2025. This records an increase from the previous number of 16,668.000 Unit for Feb 2025. India All Scheduled Airlines: International: Number of Flight data is updated monthly, averaging 7,797.000 Unit from Apr 2001 (Median) to Mar 2025, with 283 observations. The data reached an all-time high of 18,574.000 Unit in Jan 2025 and a record low of 273.000 Unit in May 2020. India All Scheduled Airlines: International: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

The BACSAM II Arctic airborne campaign took place in spring 2024. It involved the Polar 6 research aircraft stationed in Longyearbyen on Svalbard. Polar 6 was equipped with a wide range of in-situ instruments to measure meteorological and aerosol properties in the Arctic atmosphere around Svalbard and the Fram Strait. In order to understand the origin of air masses sampled by Polar 6, this dataset here was created as follows. For all research flights, every one minute along the flight track and at the coordinates and pressure altitude of Polar 6, one air mass was initialized using the the trajectory calculation tool Lagranto in conjunction with wind fields from the ERA5 reanalysis. Latter has an output resolution of around 30 km and one hour. The hourly data was bi-linearly interpolated to one minute resolution. Trajectories were then calculated five days backwards in one minute steps. Several parameters were traced along the trajectories, based on ERA5 output: Surface pressure, geopotential altitude, atmospheric boundary-layer height, air temperature, specific humidity, sea-ice concentration, and cloud liquid/ice/rain/snow water contents. Thus, it is possible to investigate the origin of the sampled air masses, as well as to estimate the relevant air-mass transformations and cloud processes they underwent during transport towards Polar 6.

The Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency. The data collection process is conducted using the Next-generation receiver (NgRx) mounted on the Air New Zealand domestic aircraft Q300.This Level 1 (L1) dataset contains the Version 1.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument onboard an Air New Zealand domestic Q300 (tail number ZK-NFA). 20 DDMs are contained within a typical L1 netcdf corresponding to 10 Left-Hand-Circularly Polarized (LHCP) and 10 Right-Hand-Circularly Polarized (RHCP) channels. Other useful scientific and engineering measurement parameters include the co- and cross-polarized Normalized Bistatic Radar Cross Section (NBRCS) of the specular point, the Leading Edge Slope (LES) of the integrated delay waveform and the normalized waveforms. The L1 dataset contains a number of other engineering and science measurement parameters, including coherence detection and a coherence state metric, sets of quality flags/indicators, error estimates, Fresnel-zone geometry estimates (and thereby the estimated per-sample spatial resolution) as well as a variety of timekeeping, and geolocation parameters.Each netCDF data files corresponds to a single flight between airports within New Zealand (flight durations typically range between 45 min and 1hr 30min with a median of 7 flights/day) and measurements are reported at 1 second increments. Latency is approximately 1 days (or better) from the last recorded measurement time.

This dataset was established in the context of the Master Thesis of Arthur Garreau and Kilian Meier in 2020 and 2021. And was published with the resulting paper: Kilian Meier, Richard Hann, Jan Skaloud, and Arthur Garreau: "Wind Estimation with Multirotor UAVs", Atmosphere 2022, 13(4), 551; https://doi.org/10.3390/atmos13040551 The dataset contains flight logs from DJI Phantom 4 drones flown in Switzerland and Norway, as well as corresponding weather data: wind, temperature, pressure and humidity. There are a total of 75 flights spread over 13 days (56 flights in Switzerland and 19 flights in Norway). Flight locations are in Adventadlen at 78°12’10.0"N15°49’41.0"E and on the EPFL campus 46°31’17.0"N 6°34’02.5"E. Flight logs are labeled by location (EPFL and Svalbard) and date. Each folder represents a given flight day and is name respecting the following naming convention "LocationYYYYMMDD". Each folder contains two subfolders: "FLIGHT" and "WEATHER". They contain respectively, flight data and weather data. The "FLIGHT" folder contains the original raw DJI flight log (.DAT, proprietary) and the extracted human readable data as a CSV file. This data was extracted using DatCon. The "WEATHER" folder contains weather sensor data in human readable text form (.txt, .dat). The exact content depends on the available sensors. The "DATA_OVERVIEW.xlsx" file contains a table describing each flight (start time, end time, path to data, etc). For more details, please consult the paper written by Meier et al. https://doi.org/10.3390/atmos13040551. More information and related thesis to be found on https://folk.ntnu.no/richahan/. GitHub repository https://github.com/meierkilian/WEMUAV

Each dataset consists of flock flights of 10 pigeons in multiple releases. A maximum of two releases were conducted per day. Age of the flock members is expressed as the average and standard deviation. Age range is shown in brackets. The number of airborne pigeons per release (mean ± S.D.) shows how many individuals actually flew together in a flock. There was no overlap in membership between the flocks in different datasets.Differences between D1 and D2 datasets.

The National Security UAS Flight Restrictions in this dataset are currently pending and will become effective on November 07, 2019. The FAA, pursuant to Title 14 of the Code of Federal Regulations (CFR) § 99.7, Special security instructions (SSI), has prohibited all UAS flight operations within the airspace defined under NOTAM FDC 7/7282 . Specific locations are described in the table and on the interactive map provided on this website. The TFRs extend from the surface up to 400 feet Above Ground Level (AGL), apply to all types and purposes of UAS flight operations, and remain in effect 24 hours a day, 7 days a week.

WHAT UAS FLIGHT RESTRICTIONS HAVE BEEN PUT INTO PLACE?

At the request of and pursuant to agreements with the Department of Defense and U.S. Federal security and intelligence agencies (“sponsoring Federal agencies”), the Federal Aviation Administration (FAA) has implemented Special Security Instructions for Unmanned Aircraft System (UAS), issued as temporary flight restrictions (TFR) over select national security sensitive facilities located throughout the U.S. These TFRs are established within the lateral boundaries of these facilities and extend from surface to 400 feet Above Ground Level (AGL). These TFRs apply to all UAS operations specifically including:

· Public aircraft operations conducted in accordance with a Certificate of Authorization or Waiver (COA).

· Civil aircraft operations (other than model aircraft), including those conducted in accordance with a COA and those conducted in accordance with the FAA’s small UAS Rule, 14 CFR Part 107.

· Model Aircraft operations conducted in accordance with 14 CFR Part 101, Subpart E.

UAS operators must comply with these flight restrictions in addition to all other applicable Federal Aviation Regulations, including but not limited to, requirements to secure an FAA airspace authorization and/or waiver prior to flying in the airspace where a TFR is in effect.

The information on this website complements Notice to Airmen (NOTAM) NOTAM FDC 7/7282, which generally notifies the public about these temporary flight restrictions (TFR). This website provides UAS operators with more detailed information about these TFRs, including:

· An explanation of what is restricted

· A table listing the selected facilities over which a TFR has been established

· An interactive map providing visual depictions and information about specific TFRs and geospatial (GIS) data that can be downloaded

· An explanation of which UAS operations may be able to access the airspace within a TFR, including instructions for submitting a request

· Reminders on other requirements for UAS operations

WHAT HAPPENS IF I VIOLATE A TEMPORARY FLIGHT RESTRICTION (TFR)?

The FAA classifies the airspace encompassed by these temporary flight restrictions (TFRs) as “national defense airspace” in accordance with Title 49 of the United States Code (USC) § 40103(b)(3). Violations of these TFRs may prompt the following enforcement actions:

A. The U.S. Government may pursue criminal charges, including charges under Title 49 U.S.C § 46307.

B. The FAA may take administrative action, including imposing civil penalties and the revoking FAA certificates and authorizations to operate UAS under Title 49 U.S.C. §§ 44709 and 46301.

WHAT ARE THE BASIC FLIGHT RESTRICTIONS?

The FAA, pursuant to Title 14 of the Code of Federal Regulations (CFR) § 99.7, Special security instructions (SSI), has prohibited all UAS flight operations within the airspace defined under NOTAM FDC 7/7282. Specific locations are described in the table and on the interactive map provided on this website. The TFRs extend from the surface up to 400 feet Above Ground Level (AGL), apply to all types and purposes of UAS flight operations, and remain in effect 24 hours a day, 7 days a week.

See the full text of NOTAM FDC 7/7282 here.

ARE THERE EXCEPTIONS FOR UAS OPERATIONS TO ACCESS A TFR?

The FAA has authorized UAS operations within the TFRs if those flights are in compliance with the applicable requirements listed below:

1) The UAS flight operation has been pre-approved by the designated facility contact based on criteria established by the sponsoring federal agency in coordination with the FAA. Note: UAS operators seeking approval to operate in one of the TFRs defined in this website under this provision must contact the facility’s designated point of contact identified in the table or interactive map, and secure permission to operate within the airspace prior to entry. Pre-approval from the facility or sponsoring agency does not substitute for compliance with FAA requirements. Depending on the nature of the proposed operation and Class of airspace, waiver or authorization may be needed from the FAA before flight. For more information visit our website at www.faa.gov/uas

2). The UAS flight operation is conducted in direct support of an active national defense, homeland security, law enforcement, firefighting, search and rescue, or disaster response mission, and prior notification has been provided to the designated facility contact. Note: UAS operators seeking approval to operate in one of the TFRs defined in this website under this provision must contact the facility’s designated point of contact identified in the table or interactive map, and provide notification prior to entering the airspace. These operators must make every effort to coordinate with the designated facility to deconflict the UAS flight operation with any safety or security concerns stated by the facility and/or sponsoring Federal agency.

3). The UAS flight operation is conducted in direct support of a significant and urgent governmental interest and is approved by the FAA’s System Operations Support Center (SOSC) in advance of entering the TFR. Note: UAS operators, that meet the criteria for thisprovision , may also qualify for access under provision 2 outlined above and are encouraged to coordinate directly with the facility’s designated point of contact identified in the table or interactive map, by providing notification prior to entering the airspace and taking into consideration any safety or security concerns stated by the facility and/or sponsoring Federal agency.

For urgent and time sensitive requests, contact the FAA’s SOSC at (202) 267-8276 for expedited assistance. The FAA’s SOSC will coordinate with the facility and/or sponsoring Federal agency as appropriate.

ARE THERE OTHER REQUIREMENTS TO OPERATE IN A TFR IN ADDITION TO THE EXCEPTIONS?

Separate and distinct from any of the conditions cited above used to gain access to a TFR defined by NOTAM FDC 7/7282 and described in this website, UAS operators must comply with all applicable Federal Aviation Regulations. For example:

For Model Aircraft:

· Comply with 14 CFR Part 101, Subpart E

NOTE: These provisions require model aircraft operators to notify any airport operator and air traffic control tower within 5 miles of the intended area of flight.

For All Other UAS Operators:

· Comply with a Public Aircraft Certificate of Authorization or Waiver (COA), or

· Comply with 14 CFR Part107, Small Unmanned Aircraft Systems, or

· Comply with Section 333 Exemption and a Certificate of Authorization or Waiver (COA)

NOTE: Public and civil UAS operators flying under the provisions of a COA or 14 CFR Part 107 may need to secure further airspace authorizations or waivers in order to conduct the proposed flight operation in controlled airspace, which may overlap with one of the TFRs defined by NOTAM FDC 7/7282 and this website. In those cases, these operators should follow the pre-existing procedures outlined below.

A. Non-emergency requests for UAS airspace authorizations and waivers must be submitted using the regular process as follows:

· 14 CFR Part 107 requests for airspace authorizations and waivers must be submitted to the FAA athttps://www.faa.gov/uas/request_waiver/

·
Section 333 Exemption holders may request a site specific COA at https://oeaaa.faa.gov/oeaaa/external/uas/portal.jsp

· Public aircraft operators without an existing authorization to operate must secure a public COA athttps://ioeaaa.faa.gov/oeaaa/Welcome.jsp

B. Emergency requests for UAS authorizations/waivers for missions that directly support significant and urgent governmental interests (e.g., active national defense, homeland security, law enforcement, and emergency operations missions), which cannot be supported by the FAA’s routine authorization/waiver processes should be referred to the SOSC at (202) 267-8276

ADDITIONAL QUESTIONS?

If you have any general questions regarding UAS operations, please refer to the following FAA webpage: https://www.faa.gov/uas/ , or contact the FAA by email at uashelp@faa.gov or by phone at (844) FLY-MY-UA.

If you have any additional questions regarding the TFRs defined by NOTAM FDC 7/7282 and this website, please contact the FAA SOSC at (202) 267-8276.

Disclaimers

The restrictions depicted on this site reflect temporary flight restrictions issued for national security reasons at select U.S. Federal facilities. There may be additional temporary flight restrictions that prohibit UAS and manned flight in effect in your area. Seehttp://tfr.faa.gov/tfr2/list.html for additional information on flight restrictions that may be in effect in your area before operating your UAS.