Flight data from the US Department of Transportation, Bureau of Transportation Statistics. Downloaded on 2019-07-04. https://www.transtats.bts.gov Data are here for use in software tutorials.

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

Column name	Type	Description
time	date time	UTC time of landing or first GA attempt
icao24	string	Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned
callsign	string	Aircraft identifier in air-ground communications
airport	string	ICAO airport code where the aircraft is landing
runway	string	Runway designator on which the aircraft landed
has_ga	string	"True" if at least one GA was performed, otherwise "False"
n_approaches	integer	Number of approaches identified for this flight
n_rwy_approached	integer	Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

Column name	Type	Description
time	date time	UTC time of landing or first GA attempt
icao24	string	Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned
callsign	string	Aircraft identifier in air-ground communications
airport	string	ICAO airport code where the aircraft is landing
runway	string	Runway designator on which the aircraft landed
has_ga	string	"True" if at least one GA was performed, otherwise "False"
n_approaches	integer	Number of approaches identified for this flight
n_rwy_approached	integer	Number of unique runways approached by this flight
registration	string	Aircraft registration
typecode	string	Aircraft ICAO typecode
icaoaircrafttype	string	ICAO aircraft type
wtc	string	ICAO wake turbulence category
glide_slope_angle	float	Angle of the ILS glide slope in degrees
has_intersection	string	Boolean that is true if the runway has an other runway intersecting it, otherwise false
rwy_length	float	Length of the runway in kilometre
airport_country	string	ISO Alpha-3 country code of the airport
airport_region	string	Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)
operator_country	string	ISO Alpha-3 country code of the operator
operator_region	string	Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)
wind_speed_knts	integer	METAR, surface wind speed in knots
wind_dir_deg	integer	METAR, surface wind direction in degrees
wind_gust_knts	integer	METAR, surface wind gust speed in knots
visibility_m	float	METAR, visibility in m
temperature_deg	integer	METAR, temperature in degrees Celsius
press_sea_level_p	float	METAR, sea level pressure in hPa
press_p	float	METAR, QNH in hPA
weather_intensity	list	METAR, list of present weather codes: qualifier - intensity
weather_precipitation	list	METAR, list of present weather codes: weather phenomena - precipitation
weather_desc	list	METAR, list of present weather codes: qualifier - descriptor
weather_obscuration	list	METAR, list of present weather codes: weather phenomena - obscuration
weather_other	list	METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

Column name	Type	Description
airport	string	ICAO airport code where the aircraft is landing
runway	string	Runway designator on which the aircraft landed
n_landings	integer	Total number of landings observed on this runway in 2019
ga_rate	float	Go-around rate, per 1000 landings
glide_slope_angle	float	Angle of the ILS glide slope in degrees
has_intersection	string	Boolean that is true if the runway has an other runway intersecting it, otherwise false
rwy_length	float	Length of the runway in kilometres
airport_country	string	ISO Alpha-3 country code of the airport
airport_region	string	Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime
from tqdm.auto import tqdm
import pandas as pd
from traffic.data import opensky
from traffic.core import Traffic


load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False)
df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC"
start = datetime.datetime(year=2019, month=1, day=4).replace(
  tzinfo=datetime.timezone.utc
)
stop = datetime.datetime(year=2019, month=1, day=5).replace(
  tzinfo=datetime.timezone.utc
)

df_selection = df.query("airport==@airport & has_ga

Bi-Annual Data Exposition: Every other year, at the Joint Statistical Meetings, the Graphics Section and the Computing Section join in sponsoring a special Poster Session called The Data Exposition , but more commonly known as The Data Expo. All of the papers presented in this Poster Session are reports of analyses of a common data set provided for the occasion. In addition, all papers presented in the session are encouraged to report the use of graphical methods employed during the developme nt of their analysis and to use graphics to convey their findings. Airline on-time performance: Have you ever been stuck in an airport because your flight was delayed or cancelled and wondered if you could have predicted it if you'd had more data? This is your chance to find out. The data: The data consists of flight arrival and departure details for all commercial flights within the USA, from October 1987 to April 2008. This is a large dataset: there are nearly 120 million records in total, and takes up 1.6 gigabytes of space compressed and 12 gigab ytes when uncompressed. The data comes originally from RITA where it is described in detail. You can download the data there, or from the bzipped csv files listed below. These files have derivable variables removed, are packaged in yearly chunks and have been more heavily compressed than the originals. Variable descriptions: Name Description 1 Year 1987-2008 2 Month 1-12 3 DayofMonth 1-31 4 DayOfWeek 1 (Monday) - 7 (Sunday) 5 DepTime actual departure time (local, hhm m) 6 CRSDepTime scheduled departure time (local, hhmm) 7 ArrTime actual arrival time (local, hhmm) 8 CRSArrTime scheduled arrival time (local, hhmm) 9 UniqueCarrier unique carrier code 10 FlightNum flight number 11 TailNum plane tail number 12 ActualElapsedTime in minutes 13 CRSElapsedTime in minutes 14 AirTime in minutes 15 ArrDelay arrival delay, in minutes 16 DepDelay departure delay, in minutes 17 Origin origin IATA airport code 18 Dest des tination IATA airport code 19 Distance in miles 20 TaxiIn taxi in time, in minutes 21 TaxiOut taxi out time in minutes 22 Cancelled was the flight cancelled? 23 CancellationCode reason for cancellation (A = carrier, B = weather, C = NAS, D = security) 24 Diverted 1 = yes, 0 = no 25 CarrierDelay in minutes 26 WeatherDelay in minutes 27 NASDelay in minutes 28 SecurityDelay in minutes 29 LateAircraftDelay in minutes

Graph and download economic data for Enplanements for U.S. Air Carrier Domestic, Scheduled Passenger Flights (ENPLANEDD11) from Jan 2000 to Nov 2024 about flight, passenger, air travel, travel, domestic, and USA.

NOAA AOC WP-3D Research Flight Data is digital data set DSI-6420, archived at the National Climatic Data Center (NCDC). This data set is meteorological data gathered by Lockheed WP-3D Orion aircraft, operated by the NOAA Aircraft Operations Center (AOC) at MacDill AFB, Florida. Data is provided by the Science and Engineering Division of AOC to the National Climatic Data Center (NCDC). The WP-3D aircraft perform many projects throughout the year. Examples of these projects would be hurricane research, atmospheric chemistry, thunderstorm investigations, and winter weather missions. Each of these projects consists of a series of individual flights. For instance, during hurricane projects, the P-3 may fly numerous flights through different tropical cyclones. For each archived project, there are multiple directories consisting of individual flights. The data in these flight directories contain the actual raw meteorological parameters obtained from sensors located in different positions on the aircraft. The data is initially written to a digital data tape on the aircraft and then later converted to a file for faster processing and archiving. Each flight folder also contains a scanned image of the actual flight manifest, the navigation log, and the mission observation logs. The flight-level data file contains measurements acquired in one second intervals. The following is a generalized list of these measured parameters: Time, GPS position data, inertial data, radar altimeter measurements, liquid water, total temperature, dewpoint temperature, attack pressure, slip pressure, differential attack and slip pressures, and static and dynamic pressure. Depending on the needs of each individual project, other sources of data will be added or subtracted from this list. As of publication this record consists of 5 projects: 1) NOAA-42 Aircraft-N42RF during the 2003 Hurricane season, the Tamdar project, and a wind calibration flight. 2) NOAA-43 Aircraft-N43RF-2003 Sar Pod, Hurricane, and Extratropical Season as well as the SFMR test flight missions. 3) NOAA-49 Aircraft-N49RF 2004 Winter Storms Experiment. 4) NOAA-43 Name Experiment 2004. 5) NOAA-49 Aircraft N49RF Hurricane Season 2004.

Air transport by airline licence (EU or non-EU) and reporting airports

SAR Aircraft Data

## Overview

SAR Aircraft Data is a dataset for object detection tasks - it contains Aircrafts annotations for 883 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Graph and download economic data for Revenue Passenger Miles for U.S. Air Carrier Domestic, Scheduled Passenger Flights (RPMDD11) from Jan 2000 to Nov 2024 about flight, miles, passenger, revenue, domestic, and USA.

Spaceborne Imaging Radar-C (SIR-C) is part of an imaging radar system that was flown on board two Space Shuttle flights (9 - 20 April, 1994 and 30 September - 11 October, 1994). The USGS distributes the C-band (5.8 cm) and L-band (23.5 cm) data. All X-band (3 cm) data is distributed by DLR. There are several types of products that are derived from the SIR-C data: Survey Data is intended as a "quick look" browse for viewing the areas that were imaged by the SIR-C system. The data consists of a strip image of an entire data swath. Resolution is approximately 100 meters, processed to a 50-meter pixel spacing. Files are distributed via File Transfer Protocol (FTP) download. Precision (Standard) Data consists of a frame image of a data segment, which represents a processed subset of the data swath. It contains high-resolution multifrequency and multipolarization data. All precision data is in CEOS format. The following types of precision data products are available: Single-Look Complex (SLC) consists of one single-look file for each scene, per frequency. Each data segment will cover 50 kilometers along the flight track, and is broken into four processing runs (two L band, two C-band). Resolution and polarization will depend on the mode in which the data was collected. Available as calibrated or uncalibrated data. Multi-Look Complex (MLC) is based on an averaging of multiple looks, and consists of one file for each scene per frequency. Each data segment will cover 100 km along the flight track, and is broken into two processing runs (one L band and one C band). Polarization will depend on the modes in which the looks were collected. The data is available in 12.5- or 25-meter pixel spacing. Reformatted Signal Data (RSD) consists of the raw radar signal data only. Each data segment will cover 100 km along the flight track, and the segment will be broken into two processing runs (L-band and C-band). Interferometry Data consists of experimental multitemporal data that covers the same area. Most data takes were collected during repeat passes within the second flight (days 7, 8, 9, and/or 10). In addition, nine data takes were collected during the second flight that were repeat passes of the first flight. Most data takes were also single polarization, although dual and quad polarization data was also collected on some passes. A Digital Elevation Model (DEM) is not included with any of the SIR-C interferometric data. The following types of interferometry products are available: Interferometric Single-Look Complex (iSLC) consists of two or more uncalibrated SLC images that have been processed with the same Doppler centroid to allow interferometric processing. Each frame image covers 50 kilometers along the flight track. The data is available in CEOS format. Raw Interferogram product (RIn) involves the combination of two data takes over the same area to produce an interferogram for each frequency (L-band and C-band). The data is available in TAR format. Reformatted Signal Data (RSD) consists of radar signal data that has been processed from two or more data takes over the same area, but the data has not been combined. Although this is not technically an interferometric product, the RSD can then be used to generate an interferogram. Each frame will cover 100 km along the flight track. The data is available in CEOS format.

Made available for NPDC GeoHUB (GIS Hub Site and Open Data Portal) : A full description is available in the Metadata. See Terms of Use. Notes:The "Updated" date, noted here in the item, does not accurately reflect the currency of the data within the Feature Layer. The data available for download on NPDC GeoHUB is updated daily, this results in differences between what is available online and NPDC's databases.

Graph and download economic data for Producer Price Index by Industry: Travel Agencies: International Flight Bookings (PCU561510561510112) from Dec 1989 to Feb 2025 about flight, agency, travel, PPI, industry, inflation, price index, indexes, price, and USA.

The recent growth in the use of Autonomous Aerial Vehicles (AAVs) has increased concerns about the safety of the autonomous vehicles, the people, and the properties around the flight path and onboard the vehicle. Much research is being done on new regulations, more robust systems are designed to address the concerns, and new methods and algorithms are introduced to detect the potential hardware and software issues. This dataset presents several fault types in control surfaces of a fixed-wing Unmanned Aerial Vehicle (UAV) for use in Fault Detection and Isolation (FDI) and Anomaly Detection (AD) research. Currently, the dataset includes processed data for 47 autonomous flights with 23 sudden full engine failure scenarios and 24 scenarios for seven other types of sudden control surface (actuator) faults, with a total of 66 minutes of flight in normal conditions and 13 minutes of post-fault flight time. It additionally includes many hours of raw data of fully-autonomous, autopilot-assisted and manual flights with tens of fault scenarios. The ground truth of the time and type of faults is provided in each scenario to enable the evaluation of new methods using the dataset. We have also provided the helper tools in several programming languages to load and work with the data and to help the evaluation of a detection method using the dataset. A set of metrics is proposed to help to compare different methods using the dataset. Most of the current fault detection methods are evaluated in simulation and as far as we know, this dataset is the only one providing the real flight data with faults in such capacity. We hope it will help advance the state-of-the-art in Anomaly Detection or FDI research for Autonomous Aerial Vehicles and mobile robots to enhance the safety of autonomous and remote flight operations further. Hardware: The platform used for collecting the dataset is a custom modification of the Carbon Z T-28 model plane. The plane has 2 meters of wingspan, a single electric engine in the front, ailerons, flaperons, an elevator, and a rudder. We equipped the aircraft with a Holybro PX4 2.4.6 autopilot, a Pitot Tube, a GPS module, and an Nvidia Jetson TX2 onboard computer. In addition to the receiver, we also equipped it with a radio for communication with the ground station.Software: The Pixhawk autopilot uses a custom version of Ardupilot/ArduPlane firmware to control the plane in both manual and autonomous modes and to create the simulations. The original firmware is modified from ArduPlane v3.9.0beta1 to allow disabling control surfaces during the flight. The onboard computer uses Robot Operating System(ROS) Kinetic Kame on Linux Ubuntu 16.04 (Xenial) to read the flight and state information from the Pixhawk using MAVROS package (the MAVLink node for ROS). More Information and Supplemental ToolsPlease visit http://theairlab.org/alfa-dataset for more information. It includes the description of each flight sequence, alternative download locations to view and download each individual flight sequence, correct citations to the relevant publications, supplemental code, and an open-source published method using the dataset.The corresponding paper explaining the dataset in more detail is currently under review in the International Journal of Robotics Research (IJRR). The pre-print (arXiv) of the paper can be accessed from our website at http://theairlab.org/alfa-dataset .The supplemental tools for reading and working with the dataset in C++, MATLAB and Python languages can be accessed from https://github.com/castacks/alfa-dataset. The repository also includes a C++ ROS-based tool for evaluating the new methods and all the ROS message type definitions for working directly with the ROS bags. Citing the WorkPlease refer to our website at http://theairlab.org/alfa-dataset to find the correct citation(s) if you are using this dataset.

This map provides the locations of airports, which the FAA defines as areas on land or water intended to be used either wholly or in part for the arrival, departure, and surface movement of aircraft/helicopters. Thus, places such as hospitals with helicopter pads are depicted as airports in this dataset. The data is provided as a vector geospatial-enabled file format.

This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.

This dataset contains 1 second files and 10 second files N43RF P-3 flight level data collected during Katrina, Ophelia, and Rita. To use NetCDF (.nc) files, you will need to employ the software available on the UCAR NetCDF website. To use the Standard Tape image (.std) files, you can read the format description and download code to read the files at the NOAA Standard tape website. There are columnar ascii files for some flights as well - these are self-expanatory.

This portion of the data release presents the raw aerial imagery collected during the unoccupied aerial system (UAS) survey of the Los Padres Reservoir delta, Carmel River valley, CA, 2017-11-01. The imagery was acquired using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. The camera was mounted using a fixed mount on the bottom of the UAS and oriented in an approximately nadir orientation. A total of six flights were conducted for the survey between 19:35 and 22:15 UTC (12:35 and 15:15 PDT). Only the imagery from fights F03 and F06 were used for data processing and are included in this data release. All flights were conducted at an approximate altitude of 100 meters above ground level resulting in a nominal ground-sample-distance (GSD) of 2.6 centimeters per pixel. Before each flight, the camera's digital ISO, aperture, and shutter speed were adjusted for ambient light conditions. For all flights the camera was triggered at 1 Hz using a built-in intervalometer. After acquisition, the images were renamed to include flight number and acquisition time in the file name. The coordinates of the approximate image acquisition locations were added ('geotagged') to the image metadata (EXIF) using the telemetry log from the UAS onboard single-frequency autonomous GPS. The image EXIF were also updated to include additional information related to the acquisition. The data release includes a total of 1,033 JPG images. Images from takeoff and landing sequences were not used for processing and have been omitted from the data release. To facilitate bulk download, the images from each flight are provided in a zip file named with the flight number. In addition to the provided zip files, the images are also available for browsing and individual download on the USGS Coastal and Marine Hazards and Resources Program Imagery Data System at the following URL: https://cmgds.marine.usgs.gov/idsviewer/data_release/10.5066-P9J9CHOH.

SPECIAL NOTE: C-MAPSS and C-MAPSS40K ARE CURRENTLY UNAVAILABLE FOR DOWNLOAD. Glenn Research Center management is reviewing the availability requirements for these software packages. We are working with Center management to get the review completed and issues resolved in a timely manner. We will post updates on this website when the issues are resolved. We apologize for any inconvenience. Please contact Jonathan Litt, jonathan.s.litt@nasa.gov, if you have any questions in the meantime. Subject Area: Engine Health Description: This data set was generated with the C-MAPSS simulator. C-MAPSS stands for 'Commercial Modular Aero-Propulsion System Simulation' and it is a tool for the simulation of realistic large commercial turbofan engine data. Each flight is a combination of a series of flight conditions with a reasonable linear transition period to allow the engine to change from one flight condition to the next. The flight conditions are arranged to cover a typical ascent from sea level to 35K ft and descent back down to sea level. The fault was injected at a given time in one of the flights and persists throughout the remaining flights, effectively increasing the age of the engine. The intent is to identify which flight and when in the flight the fault occurred. How Data Was Acquired: The data provided is from a high fidelity system level engine simulation designed to simulate nominal and fault engine degradation over a series of flights. The simulated data was created with a Matlab Simulink tool called C-MAPSS. Sample Rates and Parameter Description: The flights are full flight recordings sampled at 1 Hz and consist of 30 engine and flight condition parameters. Each flight contains 7 unique flight conditions for an approximately 90 min flight including ascent to cruise at 35K ft and descent back to sea level. The parameters for each flight are the flight conditions, health indicators, measurement temperatures and pressure measurements. Faults/Anomalies: Faults arose from the inlet engine fan, the low pressure compressor, the high pressure compressor, the high pressure turbine and the low pressure turbine.

Graph and download economic data for Load Factor for U.S. Air Carrier International, Scheduled Passenger Flights (LOADFACTORID11) from Jan 2000 to Nov 2024 about flight, passenger, and USA.

This dataset provides information to access NASA Earthdata published flight data and flight information collected by the Alpha Jet Atmospheric eXperiment (AJAX) and associated with the COMEX project in 2014-2015. The file lists information for COMEX-related datasets that has been subsetted from AJAX collections archived through NASA's Atmospheric Science Data Center. AJAX data are not otherwise replicated in this dataset. AJAX is a partnership between NASA's Ames Research Center and H211, L.L.C., which conducted in-situ measurements over California, Nevada, and the coastal Pacific in support of satellite validation. During COMEX data collection, a Picarro greenhouse gas (GHG) sensor was mounted on an Alpha Jet, a tactical strike fighter developed by Dassault-Breguet and Dornier through a German-French NATO collaboration. The GHG sensor made repeat measurements in California and Nevada. In situ data included measurements of CO2, CH4, and H2O at 2 Hz or CH4 and H2O at 10 Hz with a strategy of characterizing atmospheric structure over ocean and land, and vertical profiles to at least 5000 m. Ancillary data, including O3, formaldehyde, and meteorological profiles, were also collected. This dataset provides filenames, spatiotemporal bounds, and download URLs for accessing these in situ data. This information is provided in comma separated values (CSV) format.