The UAS Facility Maps are designed to identify permissible altitudes (above ground level) at which UAS, operating under the Small UAS Rule (14 CFR 107), can be authorized to fly within the surface areas of controlled airspace. These altitude parameters, provided by the respective air traffic control facilities, are criteria used to evaluate airspace authorization requests (14 CFR 107.41), submitted via FAA.GOV/UAS. Airspace authorization requests for altitudes in excess of the predetermined map parameters will require a lengthy coordination process. This dataset will be continually updated and expanded to include UAS Facility Maps for all controlled airspace by Fall 2017. This map is not updated in real time. Neither the map nor the information provided herein is guaranteed to be current or accurate. Reliance on this map constitutes neither FAA authorization to operate nor evidence of compliance with applicable aviation regulations in or during enforcement proceedings before the National Transportation Safety Board or any other forum. Disclaimer of Liability. The United States government will not be liable to you in respect of any claim, demand, or action—irrespective of the nature or cause of the claim, demand, or action—alleging any loss, injury, or damages, direct or indirect, that may result from the use or possession of any of the information in this draft map or any loss of profit, revenue, contracts, or savings or any other direct, indirect, incidental, special, or consequential damages arising out of any use of or reliance upon any of the information in this draft map, whether in an action in contract or tort or based on a warranty, even if the FAA has been advised of the possibility of such damages. The FAA’s total aggregate liability with respect to its obligations under this agreement or otherwise with respect to the use of this draft map or any information herein will not exceed $0. Some States, Territories, and Countries do not allow certain liability exclusions or damages limitations; to the extent of such disallowance and only to that extent, the paragraph above may not apply to you. In the event that you reside in a State, Territory, or Country that does not allow certain liability exclusions or damages limitations, you assume all risks attendant to the use of any of the information in this draft map in consideration for the provision of such information. Export Control. You agree not to export from anywhere any of the information in this draft map except in compliance with, and with all licenses and approvals required under, applicable export laws, rules, and regulations. Indemnity. You agree to indemnify, defend, and hold free and harmless the United States government from and against any liability, loss, injury (including injuries resulting in death), demand, action, cost, expense, or claim of any kind or character, including but not limited to attorney’s fees, arising out of or in connection with any use or possession by you of this draft map or the information herein. Governing Law. The above terms and conditions will be governed by the laws of each and every state within the United States, without giving effect to that state’s conflict-of-laws provisions. You agree to submit to the jurisdiction of the state or territory in which the relevant use of any of the information in this draft map occurred for any and all disputes, claims, and actions arising from or in connection with this draft map or the information herein.

The Aviation Facilities dataset is updated every 28 days from the Federal Aviation Administration (FAA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Aviation Facilities dataset is a geographic point database of all official and operational aerodromes in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the aerodrome, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. For more information about these data, please visit: https://www.faa.gov/air_traffic/flight_info/aeronav/Aero_Data/NASR_Subscription. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529011

This dataset represents active recreational flyer fixed sites (commonly referred to as flying fields) that are established by an agreement with the FAA. The fixed sites depicted here are located in controlled airspace two or more miles from an airport. At these sites, recreational UAS operations are authorized up to the unmanned aircraft system (UAS) facility map (UASFM) altitudes. If you fly at the fixed sites depicted in this dataset within controlled airspace, you must adhere to the operating limitations of the agreement, which is available from the fixed site sponsor.The FAA currently is upgrading LAANC (Low Altitude Authorization and Notification Capability) to enable recreational flyers to obtain automated authorization to controlled airspace. The FAA is committed to quickly implementing LAANC for recreational flyers. The FAA also is exploring upgrades to DroneZone to enable access for recreational flyers. Until LAANC is available for recreational operations, the FAA is granting temporary airspace authorizations to operate at certain fixed sites (commonly referred to as flying fields) that are established by an agreement with the FAA. For fixed sites that are located in controlled airspace two or more miles from an airport, operations are authorized up to the unmanned aircraft system (UAS) facility map (UASFM) altitudes. The FAA is reviewing fixed sites located within two miles of an airport and will make individualized determinations of what airspace authorization is appropriate. Aeromodelling organizations that sponsor fixed sites, regardless of their location within controlled airspace, can obtain additional information about requesting airspace authorization by email at UAShelp@faa.gov. During this interim period, you may fly in controlled airspace only at authorized fixed sites. The list of authorized fixed sites is available on the FAA’s website at www.faa.gov/uas and will be depicted on the maps on the FAA’s UAS Data Delivery System, which is available at https://udds-faa.opendata.arcgis.com. Agreements establishing fixed sites may contain additional operating limitations. If you fly at a fixed site in controlled airspace, you must adhere to the operating limitations of the agreement, which is available from the fixed site sponsor.As a reminder, existing FAA rules provide that you may not operate in any designated restricted or prohibited airspace. This includes airspace restricted for national security reasons or to safeguard emergency operations, including law enforcement activities. The easiest way to determine whether any restrictions or special requirements are in effect as well as the authorized altitudes where you want to fly is to use the maps on the FAA’s UAS Data Delivery System, which is available at https://udds-faa.opendata.arcgis.com, and to check for the latest FAA Notices to Airmen (NOTAMs). This information may also be available from third-party applications.The FAA will provide notice when LAANC is available for use by recreational flyers.Alternatively, during this interim period, the FAA directs recreational flyers to existing basic safety guidelines, which are based on industry best practices, on its website (faa.gov/uas): • Fly only for recreational purposes • Keep your unmanned aircraft within your visual line-of-sight or within the visual line of sight of a visual observer who is co-located and in direct communication with you • Do not fly above 400 feet in uncontrolled (Class G) airspace • Do not fly in controlled airspace without an FAA authorization • Follow all FAA airspace restrictions, including special security instructions and temporary flight restrictions • Never fly near other aircraft • Always give way to all other aircraft • Never fly over groups of people, public events, or stadiums full of people • Never fly near emergency response activities • Never fly under the influence of drugs or alcoholYou also should be able to explain to an FAA inspector or law enforcement official which safety guidelines you are following if you are flying under the exception for limited recreational unmanned aircraft operations.Please do not contact FAA Air Traffic facilities for airspace authorization because these facilities will no longer accept requests to operate recreational unmanned aircraft in controlled airspace.Please continue to check faa.gov/uas on a regular basis for the most current directions and guidance.

Airports Polygon is a 1:24,000-scale, feature-based layer that includes all airport features depicted on all of the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps that cover the State of Connecticut and are listed on the Federal Aviation Administration (FAA) "Airport Data (5010) & Contact Information" June 5, 2008 report. Airports in New York, Massachusetts and Rhode Island that are near the Connecticut state boundary are included. Airports that are listed by FAA and are visible on aerial photography (Connecticut 2004 Orthophotos and Connecticut 2006 NAIP Color Orthophotos from National Agriculture Imagery Program) are included. Airports that are listed by FAA but are not visible on aerial photography are not included. All airports listed by FAA are included in a separate point feature-based layer, Airport FAA CT. The airport point locations were generated from latitude and longitude coordinates contained in the FAA report and all the attribute information in the report was included. The airport layer is based partly on information from USGS topographic quadrangle maps published between 1969 and 1984 which does not represent airports in Connecticut at any one particular point in time. The layer does depict current conditions as to airports listed by FAA and having location identification codes and visible on aerial photography of 2004 and 2006. The layer delineates airports and heliports. It includes airport name, airport location code, type of facility, public or private use of facility and state the airport is located in. It does not include airport elevation, flight schedule, runway capacity, or ownership information. Features are polygonal and generally depict landing strips and perimeters for large and small airports and helicopter landing pads. Attribute information allows to cartographic representation (symbolize) and labeling of these features on a map. This layer was originally published in 1994 and slightly updated in 2005.

The Federal Aviation Administration (FAA) Regional Offices dataset is as of July 12, 2023 from the Federal Aviation Administration’s (FAA's) Office of National Engagement and Regional Administration (ARA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset shows the location and associated information for each of the nine FAA Regional Offices, which fall under the FAA’s Office of National Engagement and Regional Administration (ARA). More information about the FAA Regions and Regional Offices can be found at: https://www.faa.gov/about/office_org/headquarters_offices/ara A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529297

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 is a point layer of Military Airports currently permitted by the State of California, Department of Transportation (Caltrans), Division of Transportation Planning Aeronautics Program. The attributes include the airport location, function class, ownership, and the link of Federal Aviation Administration (FAA) site. FAA website has airport detail information and master records and reports.

content

Aviation data is information about the mechanical flight and aircraft industries. This information includes entry date,entry location date,event location time,location city name etc.

http://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/categories/

© FAA This layer is a component of Airports.

United States Landing Facilities including Heliport, Seaplane Base, Gliderport, Ultralight, and Balloonports

© FAA, BTS, Derald Dudley

The Airport Points and Estimated Pavement map contains basic information about North Carolina airports included in the National Plan of Integrated Airport Systems (NPIAS). The location information also known as the airport reference point (ARP) is from the Federal Aviation Administration’s (FAA) Airport Master Record (https://www.faa.gov/airports/airport_safety/airportdata_5010/#5010) and is accurate as of 06/21/2019. The airport class data is from the NCDOT Division of Aviation’s Airport System Plan Update (https://connect.ncdot.gov/municipalities/State-Airport-Aid/State%20Airport%20Aid%20Documents/NC%20Airport%20System%20Plan%20(2015).pdf) dated December 2015. Any of the airports that have an Automated Weather Observing System (AWOS) or Automated Surface Observing System (ASOS) has the type, owner, reporting frequency, and reporting phone number (https://www.faa.gov/air_traffic/weather/asos/). A link to has been provided in the data to retrieve the most recently disseminated weather report from the systems. These data were last updated July 2024. Please contact the Division of Aviation with any questions about the data.Contact:Jaimie Nevinsext-janevins@ncdot.govNC Department of Information Technology - Transportation

This dataset consists of UAS flight images from three sites along an elevation and precipitation gradient within Reynolds Creek Experimental Watershed collected between June 4 and July 9, 2019. The lowest elevation site ('wbs1', 1,425 m) was vegetated by shrub steppe dominated Wyoming big sage (Artemisia tridentata ssp. wyomingensis). Vegetation at the middle elevation site ('los1', 1,680 m) was shrub steppe dominated by low sage (Artemisia arbuscula). Shrub steppe at the highest elevation site ('mbs1', 2,110 m) was dominated by mountain big sage (Artemisia tridentata ssp. vaseyana) and Utah snowberry (Symphoricarpos oreophilus utahensis). A MicaSense RedEdge 3 sensor mounted on a DJI Matrice 600 Pro UAS platform was used to collect multispectral imagery of each site. The drone was flown by a Federal Aviation Administration (FAA) Part 107 certified remote pilot between June 5 and July 9 2019. All flights were completed within two hours of solar noon. The RedEdge is a broadband multispectral sensor: blue (475nm), green (560nm), red (668nm), red edge (717nm), and near-infrared (840nm). The RedEdge sensor was radiometrically calibrated using a reflectance panel before and after each flight. A DJI Phantom 4 with the stock FC330 Red Green Blue (sRGB) camera was flown over each site to collect imagery at a finer spatial resolution to assist with training and test data for vegetation type classification.Resources in this dataset:Resource Title: UAS Imagery and Location Data - SCINet.File Name: Web Page, url: https://app.globus.org/file-manager?origin_id=904c2108-90cf-11e8-9672-0a6d4e044368&origin_path=/LTS/ADCdatastorage/NAL/published/node424632/Folder containing imagery (.zip) and location (.csv) data. The .zip files contain unprocessed visual (RGB) imagery in .jpg format acquired with a 12-MP DJI (Sony) FC330 camera and unprocessed multispectral, 5-band imagery in .tif format acquired with a MicaSense RedEdge-M sensor. Camera settings and EXIF information are embedded in the imagery files. The .csv files contain ground control point (GCP) labels and coordinate information recorded with an RTK instrument for GCP target (black/white cross) locations at the relevant study areas.SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node424632/ See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

sUAS Facility Maps (UASFM) that indicate “pre-approved fly altitudes.” Within each grid on the map, FAA would identify maximum altitudes at which flight is permitted without further coordination.Airspace at or below the maximum altitudes would be “pre-approved fly zones” and airspace above the maximum altitudes would require further ATC coordination.

Dataset: 2023 GPS Anomalies, NOTAMs, and Aircraft Traffic

The dataset "2023 GPS Anomalies, NOTAMs, and Aircraft Traffic" was collected and generated for the paper "Detecting GPS Anomalies in Aviation Using ADS-B: Correlating Coordinate Gaps and GPS Deviations with NOTAM Warnings."

This dataset provides a collection of geospatial and temporal data necessary for analyzing potential GPS anomalies in aviation. The data sources include NOTAMs received from the FAA, and the aircraft traffic and GPS information calculated and extracted from the OpenSky Trino ADS-B database.

The FAA_and_ICAO_locations file includes 21,382 records with identifiers, coordinates, and detailed facility information. This dataset serves as a reference for analyzing the geographical distribution of aviation facilities. The Flights_per_Hour_per_Grid file, with 74,219,036 records, provides hourly flight movement counts within specified grids, offering insights into air traffic patterns and potential disruptions. The GPS_Jumps_from_Routes file, comprising 5,878,275 records, documents deviations in flight paths, capturing metrics such as distances, speeds, and timestamps. This data is crucial for identifying potential GPS spoofing incidents by analyzing unusual jumps between consecutive data points.

The GPS_Missing_Coordinates file, with 53,232 records, highlights periods of missing GPS signals, indicating possible GPS jamming events. This file includes start and end times, distances between known coordinates, and Navigation Integrity Category (NIC) values to assess data quality during null periods. The NOTAM_ICAO_GPS and NOTAM_USA files, with 30,160 and 234,205 records respectively, provide detailed information on NOTAM areas, including geographic areas, active periods, and categories. This allows for an analysis of the spatial and temporal correlation between NOTAM warnings and GPS anomalies, facilitating a better understanding of the impact of GPS disruptions on aviation safety and operations.

Summary Table

Details

1. FAA_and_ICAO_locations.csv and FAA_and_ICAO_locations.dpkg

• Total Records: 21,382
• Columns:
• WKT: Well-Known Text representation of a point in the CSV file, or a geometry field in the DPKG file.
• id: Unique identifier for each record.
• fid: Feature identifier.
• Location_ID: Identifier for the location.
• ICAO_ID: ICAO (International Civil Aviation Organization) identifier.
• IATA_ID: IATA (International Air Transport Association) identifier.
• FAA_Location_Code: FAA location code.
• Facility_Type: Type of facility (e.g., airport, heliport).
• Facility_Name: Name of the facility.
• FAA_New_Location_Code: New location code by FAA.
• Coordinates: Coordinates of the location.
• lat: Latitude of the location.
• lon: Longitude of the location.
• Region: Geographical region of the location.
• Country_Code: Country code of the location.
• Country: Country name of the location.
• State_Id: State identifier.
• State_Name: Name of the state.
• City: City name.
• Location: General location information.
• Effective_Date: Effective date of the record.
• Site_Id: Site identifier.
• ADO: Airport District Office.
• ARTCC_Id: ARTCC (Air Route Traffic Control Center) identifier.
• ARTCC_Computer_ID: ARTCC computer identifier.
• ARTCC_Name: Name of the ARTCC.
• Tie_In_FSS_Id: Tie-in Flight Service Station identifier.
• Tie_In_FSS_Name: Name of the Tie-in Flight Service Station.
• NOTAM_Facility_Id: NOTAM (Notice to Airmen) facility identifier.
• NOTAM_Service: Indicates if NOTAM service is available (Y/N).

2. Flights_per_Hour_per_Grid-2023.csv and Flights_per_Hour_per_Grid-2023.dpkg

• Total Records: 74,219,036
• Columns:
• grid_id: Identifier for the grid.
• hour: Timestamp for the hour.
• movement_count: Number of flights in each 0.5x0.5 degree grid during each hour of year 2023.
• geometry: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.

3. GPS_Jumps_from_Routes-2023.csv and GPS_Jumps_from_Routes-2023.dpkg

• Total Records: 5,878,275
• Columns:
• WKT: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.
• id: Unique identifier for each record.
• fid: Feature identifier.
• icao24: ICAO 24-bit aircraft address.
• callsign: Callsign of the aircraft.
• time_before_spoofing: Timestamp before the spoofing event.
• time_of_spoofing: Timestamp of the spoofing event.
• distance: Distance of the jump in meters.
• time_difference: Time difference between two coordinates in seconds.
• speed_m_s: Speed in meters per second.
• time_start: Start time of the record.
• time_end: End time of the record.

4. GPS_Missing_Coordinates-2023.csv and GPS_Missing_Coordinates-2023.dpkg

• Total Records: 53,232
• Columns:
• WKT: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.
• id: Unique identifier for each record.
• icao24: ICAO 24-bit aircraft address.
• callsign: Callsign of the aircraft.
• null_start_time: Start time of missing GPS coordinates.
• null_end_time: End time of missing GPS coordinates.
• time_of_previous_not_null_coords: Time of the last known good GPS coordinates before the null period.
• time_of_next_not_null_coords: Time of the first known good GPS coordinates after the null

The dataset "2023 GPS Anomalies, NOTAMs, and Aircraft Traffic" was collected and generated for the paper "Detecting GPS Anomalies in Aviation Using ADS-B: Correlating Coordinate Gaps and GPS Deviations with NOTAM Warnings."

This dataset provides a comprehensive collection of geospatial and temporal data necessary for analyzing potential GPS anomalies in aviation. The data sources include NOTAMs received from the FAA, and GPS information calculated and extracted from the OpenSky Trino ADS-B database. The dataset contains files such as FAA_and_ICAO_locations, Flights_per_Hour_per_Grid, GPS_Jumps_from_Routes (possible spoofing), GPS_Missing_Coordinates (possible jamming), and two sets of NOTAMs (ICAO and USA). Each file includes detailed columns that capture essential attributes and metrics, enabling thorough examination and correlation of GPS anomalies with NOTAM warnings.

The FAA_and_ICAO_locations files include 21,383 records with identifiers, coordinates, and detailed facility information. This dataset serves as a reference for analyzing the geographical distribution of aviation facilities. The Flights_per_Hour_per_Grid file, with 74,219,036 records, provides hourly flight movement counts within specified grids, offering insights into air traffic patterns and potential disruptions. The GPS_Jumps_from_Routes data, comprising 5,878,276 records, documents deviations in flight paths, capturing metrics such as distances, speeds, and timestamps. This data is crucial for identifying potential GPS spoofing incidents by analyzing unusual jumps between consecutive data points.

The GPS_Missing_Coordinates file, with 53,227 records, highlights periods of missing GPS signals, indicating possible GPS jamming events. This file includes start and end times, distances between known coordinates, and Navigation Integrity Category (NIC) values to assess data quality during null periods. The NOTAM_ICAO_GPS and NOTAM_USA files, with 30,161 and 234,206 records respectively, provide detailed information on NOTAM areas, including geographic extents, active periods, and categories. This allows for a comprehensive analysis of the spatial and temporal correlation between NOTAM warnings and GPS anomalies, facilitating a better understanding of the impact of GPS disruptions on aviation safety and operations.

Summary Table

Details

Note that all the files are zipped as CSV. The GPKG version is also available where geographical information is present.

1. FAA_and_ICAO_locations.csv.zip and FAA_and_ICAO_locations.dpkg.zip

• Total Records: 21,383
• Columns:
  • WKT: Well-Known Text representation of a point in the CSV file, or a geometry field in the DPKG file.
  • id: Unique identifier for each record.
  • fid: Feature identifier.
  • Location_ID: Identifier for the location.
  • ICAO_ID: ICAO (International Civil Aviation Organization) identifier.
  • IATA_ID: IATA (International Air Transport Association) identifier.
  • FAA_Location_Code: FAA location code.
  • Facility_Type: Type of facility (e.g., airport, heliport).
  • Facility_Name: Name of the facility.
  • FAA_New_Location_Code: New location code by FAA.
  • Coordinates: Coordinates of the location.
  • lat: Latitude of the location.
  • lon: Longitude of the location.
  • Region: Geographical region of the location.
  • Country_Code: Country code of the location.
  • Country: Country name of the location.
  • State_Id: State identifier.
  • State_Name: Name of the state.
  • City: City name.
  • Location: General location information.
  • Effective_Date: Effective date of the record.
  • Site_Id: Site identifier.
  • ADO: Airport District Office.
  • ARTCC_Id: ARTCC (Air Route Traffic Control Center) identifier.
  • ARTCC_Computer_ID: ARTCC computer identifier.
  • ARTCC_Name: Name of the ARTCC.
  • Tie_In_FSS_Id: Tie-in Flight Service Station identifier.
  • Tie_In_FSS_Name: Name of the Tie-in Flight Service Station.
  • NOTAM_Facility_Id: NOTAM (Notice to Airmen) facility identifier.
  • NOTAM_Service: Indicates if NOTAM service is available (Y/N).

2. Flights_per_Hour_per_Grid-WKT.csv.zip and Flights_per_Hour_per_Grid-WKT.dpkg.zip

• Total Records: 74,219,036
• Columns:
  • grid_id: Identifier for the grid.
  • hour: Timestamp for the hour.
  • movement_count: Number of flights in each 0.5x0.5 degree grid during each hour of year 2023.
  • geometry: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.

3. GPS_Jumps_from_Routes-2023.csv.zip and GPS_Jumps_from_Routes-2023.dpkg.zip

• Total Records: 5,878,276
• Columns:
  • WKT: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.
  • id: Unique identifier for each record.
  • fid: Feature identifier.
  • icao24: ICAO 24-bit aircraft address.
  • callsign: Callsign of the aircraft.
  • time_before_spoofing: Timestamp before the spoofing event.
  • time_of_spoofing: Timestamp of the spoofing event.
  • distance: Distance of the jump in meters.
  • time_difference: Time difference between two coordinates in seconds.
  • speed_m_s: Speed in meters per second.
  • time_start: Start time of the record.
  • time_end: End time of the record.

4. GPS_Missing_Coordinates-2023.csv.zip and GPS_Missing_Coordinates-2023.dpkg.zip

• Total Records: 53,227
• Columns:
  • WKT: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.
  • id: Unique identifier for each record.
  • icao24: ICAO 24-bit aircraft address.
  • callsign: Callsign of the aircraft.
  • null_start_time: Start time of missing GPS

Public use airports in the state of Alaska. Includes public airports, heliports, and seaplane bases. Points represent the actual location of each airport as provided by the FAA. For Ownership and Use attributes: PU - PublicPR - PrivateSource: Federal Aviation Administration, 2018, Alaska Department of Transportation & Public FacilitiesThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Transportation GIS and FAA Airport Data & Contact Information

Antenna Structure Registration Dataset

Antenna Structure Registration Data

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset provides comprehensive registration information for antenna structures, specifically the Antenna Structure Registrate (ASR). The ASR is a database that contains data on antenna structures that meet certain criteria, such as being taller than 60.96 meters (200 feet) or being located in close proximity to an airport.

The dataset includes various attributes related to the antenna structures and their registration. These attributes cover details about the ownership and operation of the antenna structure, including the entity or organization responsible for it. Additionally, information about contact persons or organizations associated with the structure is provided, including their names, addresses, and city of residence.

Geographical details of each structure are also included in this dataset. This includes both latitude and longitude coordinates represented in different formats - decimal degrees and degrees/minutes/seconds. The direction (North/South for latitude and East/West for longitude) is provided alongside these coordinates.

To aid in identifying specific structures within this dataset, registration numbers are assigned to each entry. Additionally, file numbers associated with each registration provide unique identifiers for tracking purposes.

Furthermore, licensing details associated with each antenna structure are included. FAA study numbers and circular numbers are provided to reference studies conducted by the Federal Aviation Administration (FAA) regarding these structures.

It's important to note that there may be errors present within some of the licensing information contained in this dataset. Specifically mentioned are potential inaccuracies related to latitude, longitude, ground elevation data as well as frequency assignment data used during generation of these files.

Overall, this dataset serves as a valuable resource for understanding registered antenna structures by providing detailed location information along with contact details and licensing specifics. It can be utilized by researchers studying communication infrastructure or individuals interested in exploring regulatory aspects surrounding antenna installations

How to use the dataset

Introduction

Understanding the Columns

• X and Y: These columns represent the X-coordinate and Y-coordinate of the antenna structure's location in numeric format.

• REGNUM: The registration number of the antenna structure is recorded in this column. It uniquely identifies each structure.

• FILENUM: The file number associated with the antenna structure registration is provided in this column. It helps identify relevant documentation related to a specific structure.

• ENTITY: This column contains information about the organization or entity that owns or operates the antenna structure.

• LAT_DMS and LON_DMS: These columns provide latitude (in degrees, minutes, and seconds) and longitude (in degrees, minutes, and seconds) respectively for locating an antenna structure precisely.

• LAT_DIR and LON_DIR: These columns indicate whether latitude is North or South direction (N/S), while longitude is East or West direction (E/W).

• DD_TEMP and DD_TEMP0: The decimal degree representation of latitude and longitude is mentioned in these columns as numeric values.

• STRUCHT: This column describes the type or category of each antenna structure such as tower, mast, etc.

• STRUCADD, STRUCCITY, and STRUCSTATE: These columns provide information about address including street name/city/state where a particular structure is located.

• • Column: FAASTUDY Description: This column contains FAA study numbers related to specific antenna structures.

  • Column: FAACIRC Description: The FAA circular number associated with the antenna structure is mentioned in this column.

1. CONTNAME: This column represents the name of the contact person or organization responsible for the antenna structure.

2. CONTADD, CONTCITY, CONTSTATE, and CONTZIP: These columns display the contact address, city, state, and ZIP code respectively for point-of-contact related to a particular antenna structure.

Tips for Utilizing the Dataset

• Location Analysis: Use latitude and longitude information to analyze clusters or patterns of registered antenna structures in specific areas or regions.

• Antenna Structure Types...

Remote ID Broadcast Module for Drones Market Outlook

According to our latest research, the global Remote ID Broadcast Module for Drones market size reached USD 1.37 billion in 2024. The market is projected to grow at a CAGR of 17.8% during the forecast period, reaching USD 6.06 billion by 2033. This robust growth is primarily driven by stringent regulatory mandates for drone identification, rapid adoption of unmanned aerial vehicles (UAVs) across diverse sectors, and increasing focus on airspace safety and security. The market’s momentum is further supported by technological innovations and expanding commercial drone applications, making Remote ID compliance an essential aspect of the evolving drone ecosystem.

One of the key growth factors fueling the Remote ID Broadcast Module for Drones market is the global implementation of regulatory frameworks mandating Remote ID capabilities. Aviation authorities such as the Federal Aviation Administration (FAA) in the United States and the European Union Aviation Safety Agency (EASA) have introduced comprehensive regulations that require most drones operating in regulated airspace to broadcast identification and location information. This regulatory push has compelled drone manufacturers, operators, and service providers to integrate Remote ID modules into both new and existing fleets, driving substantial demand for compliant broadcast solutions. The increasing frequency of drone-related incidents and the need for effective air traffic management further underscore the critical role of Remote ID in enabling safe and responsible drone operations.

Another significant driver is the proliferation of commercial drone use cases across industries such as agriculture, logistics, infrastructure inspection, mapping, and emergency response. As drones become indispensable tools for data collection, delivery, and surveillance, stakeholders are prioritizing investments in technologies that ensure operational transparency and accountability. Remote ID broadcast modules not only facilitate regulatory compliance but also enhance situational awareness and safety for both manned and unmanned aircraft. The rapid expansion of drone-enabled services, coupled with the growing sophistication of UAV platforms, is expected to sustain high demand for advanced Remote ID solutions, particularly those offering seamless integration and interoperability.

Technological advancements are also shaping the trajectory of the Remote ID Broadcast Module for Drones market. Innovations in wireless communication protocols, miniaturization of hardware, and improved battery efficiencies are enabling the development of lightweight, cost-effective, and highly reliable broadcast modules. The emergence of multi-protocol modules supporting Bluetooth, Wi-Fi, and cellular connectivity is expanding the addressable market and opening new possibilities for remote identification in complex operational environments. Additionally, the increasing adoption of cloud-based drone management platforms and the integration of artificial intelligence for real-time data analytics are further enhancing the value proposition of Remote ID solutions, fostering broader market acceptance and adoption.

From a regional perspective, North America currently leads the global market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to early regulatory adoption, a mature drone ecosystem, and significant investments in drone infrastructure. Europe’s market growth is propelled by harmonized regulatory standards and strong demand from commercial and governmental sectors, while Asia Pacific is witnessing rapid expansion due to increasing drone deployments in agriculture, logistics, and public safety. As regulatory clarity improves and drone adoption accelerates across emerging markets, the global Remote ID Broadcast Module for Drones market is poised for sustained, multi-regional growth through 2033.

Type Analysis

The Type segment of the Remote ID Broadcast Module for Drones market is broadly categorized into Standalone Modules and Integrated Modules. Standalone modules are external devices that can be retrofitted onto existing drones, making them a popular choice for operators seeking to upgrade legacy fleets for regulatory compliance. These modules are designed for easy installation and compat

Global Aeronautical Distress Safety System Market Outlook

According to our latest research, the global aeronautical distress safety system market size reached USD 1.62 billion in 2024. The market is exhibiting robust growth, with a recorded compound annual growth rate (CAGR) of 7.8% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a valuation of USD 3.21 billion. This growth trajectory is primarily driven by the increasing emphasis on aviation safety protocols, stringent regulatory mandates, and the rising adoption of advanced communication and tracking technologies in the aviation sector worldwide.

A key growth driver for the aeronautical distress safety system market is the intensification of global aviation safety regulations. Regulatory bodies such as the International Civil Aviation Organization (ICAO) and the Federal Aviation Administration (FAA) have enforced rigorous standards for distress signal tracking and emergency response. These mandates require airlines and operators to integrate state-of-the-art distress safety systems, including emergency locator transmitters and advanced communication networks, to ensure swift location and rescue operations during aviation emergencies. The growing awareness among airlines regarding the critical importance of minimizing search-and-rescue time has further accelerated the adoption of technologically advanced safety systems, thereby fueling market expansion.

Another significant factor contributing to market growth is the rapid technological advancements in satellite-based communication and tracking solutions. The evolution of global navigation satellite systems (GNSS), next-generation distress beacons, and hybrid tracking devices has revolutionized the efficiency and reliability of aeronautical distress safety systems. These innovations enable real-time aircraft tracking, seamless communication during emergencies, and precise location identification even in remote or oceanic regions. The increasing collaboration between aerospace technology providers and satellite network operators is further enhancing the capabilities of these systems, making them indispensable for commercial, military, and general aviation sectors.

Furthermore, the surge in global air traffic and the expansion of commercial aviation fleets are pivotal in driving demand for aeronautical distress safety systems. As airlines and airports strive to accommodate the rising number of passengers and flights, the need for robust safety infrastructure becomes paramount. Emerging markets in Asia Pacific and the Middle East, in particular, are investing heavily in upgrading their aviation safety frameworks to align with international standards. This trend, coupled with the increasing deployment of distress safety systems in military and defense aviation, is expected to sustain the marketÂ’s upward trajectory throughout the forecast period.

The Aircraft Emergency Locator Transmitter (ELT) is a critical component in the aeronautical distress safety system market, providing a vital lifeline in the event of an aviation emergency. These devices are designed to automatically activate upon impact, transmitting a distress signal to search and rescue teams. The integration of ELTs with satellite-based systems ensures that the location of an aircraft in distress can be pinpointed with high accuracy, even in remote areas. This capability is crucial for minimizing search-and-rescue times and enhancing the chances of survival for passengers and crew. As technology advances, modern ELTs are becoming more sophisticated, offering features such as GPS integration and automatic activation, which further enhance their reliability and effectiveness in emergency situations.

From a regional perspective, North America currently dominates the aeronautical distress safety system market, attributed to its technologically advanced aviation sector and stringent regulatory environment. However, the Asia Pacific region is projected to witness the fastest growth rate, driven by burgeoning investments in airport infrastructure, expanding commercial aviation fleets, and rising adoption of next-generation safety technologies. Europe also holds a significant share, supported by proactive regulatory measures and the presence of leading aerospace companies. Meanwhile, Latin America and the