US military planes in South China Sea

There are a lot of US military activity in the area between Taiwan, Hong Kong and the South China Sea. Transports, SIGINT, reconnaissance, protection of US carriers in the area… mainly conducted by the US Air Force and the US Navy. Here is a list of planes seen in past few months (click on the link to see the radar map when the trackers got a MLAT position – these aircraft generally do not broadcast their position on ADSB):

AE049B – Tail number: 61-0313 – Boeing KC-135R – US Air Force

AE1492 – Tail number: 96-0042 – Boeing E-8C Joint Stars – US Air Force

AE1D8A – Tail number: 15-6511 – Lockheed EP-3E Aries 2 Orion – US Navy

AE1D95- Tail number: 15-9893 – Lockheed EP-3E Orion – US Navy

AE1D91- Tail number: 15-7318 – Lockheed EP-3E Aries2 Orion – US Navy

AE1D5D – Tail number: 16-1586 – Lockheed P-3C-II.2 Orion – US Navy

AE17EF- Tail number: 05-0730 – Boeing C-40C – US Air Force

AE04FD – Tail number: 58-0095 – Boeing KC135T – US Air Force

AE01CE – Tail number: 62-4134 – Boeing RC135W Stratotanker SIGINT – US Air Force

You also can see the latest detected military and other aircraft of interest and the pick of the day or search my database on

SAM727 flying over Guangzhou with Secretary Pompeo

Today (2020-10-30), the Boeing C-32A with tail 99-0003 was spotted by my trackers over Guangzhou with the CallSign SAM727.

A quick search shows a departure from Hanoi, and a current position over Japan. Probably used by Secretary Pompeo who was visiting Hanoi yesterday.

Having fun with my ADS-B tracker project

How it started

Some years ago, I bumped into an article on Mashable about Jeremy Merrill using a Raspberry Pi ADS-B tracker to detect aircraft flying over his house and display the origin or destination of that plane (see article here and github there). As my place is just on a busy plane corridor, that gave me an idea. I decided to work on a similar project based on Piaware. Looking at how he did it and at the long literature you can find on the web on ADS-B, I created this site, my own software in Python to run on my Pi and other fun stuffs. I also ran into this site by SonicGoose and that gave me many new ideas (including the basic structure of this site – I never had any experience of html or php before).

One key part is getting access to aircraft databases to get more info from the ModeS hex code you get from the Piaware tracker. I am sharing my own database of planes, airports and routes I detected with one of my trackers (I have a fixed one at home, another one in Briancon in France (solar powered and connected by SigFox!) and a mobile one I take with me during my trips over the world).

When I started few years ago, I didn’t know that it will become such a fun project!


[2019/02] Update of installation script and new links
[2019/05] Update of the Radar page that now use my own positions database. Added aircraft silhouettes and Airlines logo.
[2019/05] Changed the domain to !!!
[2019/06] Changed landing page and reworked index for Google
[2019/07] Added Great Circle tracks between origin, destination and seen positions on Radar
[2019/09] Added Airports data, API page, better sitemap for Google index
[2020/01] Added a solar powered ADS-B tracker in Briancon, with a SigFox interface by SNOC to send new / special planes to this database when Wifi is off – works great!
Running an ADS-B tracker on a solar panel is quite challenging. After some research, I believed a 20W solar panel would be enough, but the stick consumption is very high, and solar energy production is far from stable… after few month using it, I would need about 80W to be able to power the system for 24h (Raspberry Pi 4 + Flightaware Pro Stick Plus + SNOC SigFox). SigFox is a great solution to replace a 4G modem which would also take too much energy. The limitation to 6 messages per hour is manageable, as I am only reporting new or special planes to this website, while the detailed data are stored and shared when the device has access to wifi.
The biggest issue I didn’t plan for was that the Pi doesn’t have a RTC, so time was wrong (it only progress when the Pi is on, about 8 to 12h per day… after few days, it was completely off). Hopefully, I could also use the downlink SigFox messages to get the network time and update the Pi time at each boot. Here the limitation is at 4 messages per day. It is controlled by the callback feature so it is quite easy to be sure you will not exceed the budget, even with a Pi with no idea what is current date or time. Next step is to work on shuting down properly the Pi when battery is low in order to avoid memory issue. So far it works with multiple wild shutdowns per day, but I don’t know for how long.
[2020/02] Updated the stats page to show more details
[2020/03] Added tweets on Sigfox interface with Briancon tracker
[2020/07] Working on adding MoPi-2 on the solar powered tracker to allow clean power down. Changed all the time in UTC. With trackers everywhere, it doesn’t make sens anymore to be on Hong Kong time.
[2020/09] Added a new blog. Let’s hope I will post regularly from now on![2020/10] API optimisation – reduced the errors from 2% to 0.05%
[2020/11] New landing page with a RSS feed reader to get up to date links to my blog posts. Let’s see the impact on Search ranking!
[2020/12] Upgraded all my Raspberry Pi code to Python3. I have been delaying that for a long time, but it was much easier than expected. Further optimisation of the API to reduce the error rate further. Some trackers are now at 0 errors, some still at 0.05%, still struggling to understand why.

Interesting links on ADS-B trackers

Will airlines recover from COVID-19 crisis?

I have been tracking planes in Hong Kong for few years now with my Raspberry Pi based ADSB trackers. There are few airports in range (Hong Kong, Macau, Guangzhou, Shenzhen, Zhuhai, Huizhou), which was giving me quite some traffic (about 2000-2200 different aircraft per day).

Then came COVID-19. Many international airlines stopped to come to China. Many domestic flights were cancelled too. And then the crisis went worldwide. Daily traffic dropped to 600 aircrafts per day at the peak of the crisis.

Since then, it has been slowly recovering. As China domestic traffic went back almost to normal, I am now at 1300-1400 aircraft per day, still far away from where it was.

Bad news are coming from everywhere… Cathay Pacific just announced they will suppress 6000 positions and stop Cathay Dragon. Many smaller airlines have been bankrupt. Most of the others have retired their old or not efficient aircraft earlier than initially planned (B747 and A380 are the most iconic ones).

And restrictions to international travels are still in place in many countries, while the epidemic is restarting in Europe, and never really slow down in US. Can the industry recover from such an unprecedented crisis? I wish it will! My bet is that it will take time and that it will have deeply modified the industry. 2020 will be a turning point in any case and resilience now has to be in every airline strategy.

Sharing my database of Aircraft, Airports and Routes

I have been gathering data for few years now, from multiple sources (databases found online, some special aircraft found manually online, …). I now have more than 32,500 entries in my Aircraft database, including 6,000 that I flagged as “special”, meaning military or any other interesting features (helicopters, rare aircrafts …).

I also have more than 16,000 airports and 160,000 routes.

They are all accessible through dedicated API.

Search aircraft by Mode S code or tail number

You can access my database of aircraft through the following API:
where XXXXXX is the ModeS code of the aircraft, on 6 digits in hexadecimal.
where XXXXXX is the tail number (or registration) of the aircraft (in some countries, tail numbers have a – like in China B-8638, it should be put here).
The output is in json format (RFC4627).
Working samples of the API:

Returned data:

          "ModeS":            "78102f",
          "ModeSCountry":     "China",
          "Registration":     "B-8638",
          "ICAOTypeCode":     "A321",
          "Type":             "Airbus A321-211",
          "SerialNo":         "---",
          "RegisteredOwners": "China Southern Airlines",
          "OperatorFlagCode": "CSN",
          "FirstSeen":        "2017-03-25 23:58:09",
          "LastSeen":         "2019-09-06 07:25:41"
ModeSModeS code in HEX format of the Aircraft6 characters
ModeSCountryCountry of registration of the Aircraftstring
RegistrationTail Number of the Aircraftstring
ICAOTypeCodeICAO Type Code of the Typestring
TypeType of Aircraftstring
SerialNoSerial Number of the Aircraftstring
RegisteredOwnersRegistered Owners of the Aircraftstring
OperatorFlagCodeCode of the Airline if anystring
FirstSeenTime stamp when my trackers first detected the AircraftTimestamp
LastSeenTime stamp when my trackers last detected the AircraftTimestamp
Json structure description for Aircraft API

Search airports by ICAO code

You can also access my database of airports through the following API:
where XXXX is the ICAO code of the airport on 4 characters.
The output is in json format (RFC4627).
Working samples of the API:

Returned data:

          "ICAO":       "VHHH",
          "IATA":       "HKG",
          "Name":       "Hong Kong International Airport",
          "Location":   "Hong Kong",
          "Region":     "Hong Kong",
          "Latitude":   22.3089,
          "Longitude":  113.915
ICAOICAO code of the Airport4 characters
IATAIATA code of the Airport3 characters
NameName of the Airportstring
LocationLocation of the Airport in clearstring
RegionRegion or Country of the Airportstring
LatitudeLatitude of the Airportfloat
LongitudeLongitude of the Airportfloat
Json structure description for Airport API

Search routes by Flight Number (ICAO format)

You can also access my database of routes through the following API:
where XXXXXX is the flight number in ICAO format.
The output is in json format (RFC4627).
Working samples of the API:

Returned data:

          "FNB":            "AFR188",
          "Airline":        "AFR",
          "FlightNumber":   "188",
          "Origin":         "LFPG",
          "Destination":    "VHHH",
          "Via":            ""
FNBFlight number in ICAO formatstring
AirlineICAO code of the airline4 characters
FlightNumberFlight numberstring
OriginOrigin airport (ICAO format)string
DestinationDestination airport (ICAO format)string
ViaVia airport – if any (ICAO format)string
Json structure description for Airports API


All the information provided by these API are provided as is and for information only. They are coming from different sources and may contain mistakes or be out of date. Please use with caution and at your own risk.

You are welcome to comment if you see mistakes. Mode S codes can be re-allocated to new aircraft, and keeping the database up to date is not an easy task.

G-IRTY Spitfire flying over Hong Kong in 2019

One of the most surprising catch of 2019 was for sure seeing a Spitfire in my new aircraft list! I had to check if that was correct or a mistake somewhere in the database of Mode S codes I collected.

It turned out it was the real thing! The Silver Spitfire was doing The Longest Flight, for the 75th anniversary of the D-Day invasion. A 30 legs, 22,138 miles trip around the world.

These guys rock!

A Guide to Aircraft Tracking Around the World

If your a fan of Aircraft Tracking, I highly recommend the article from Global Investigative Journalism Network (or GIJN) “Planespotting: A Guide to Tracking Aircraft Around the World“. This is one of the most comprehensive article I have seen on the subject, addressing it from multiple angles (#avgeek, journalist, activist, …). They even propose a short version if you just want to start tracking.

Great work!

Buzzfeed trains an AI to find spy planes

An interesting article about how Buzzfeed is training an Artificial Intelligence (AI) to detect planes circling over San Francisco (mainly law enforcement and other contractors).

That’s an interesting direction… I will have a look at how to do that and detect anomalies in trajectories on my trackers. I expect to see more planes circling due to delay in the local airports (Hong Kong, Shenzhen, Guangzhou) than spy planes!

More details here on how they use a Random Forests algorithm to classify potential candidates. I found Random Forests libraries in Python, so that definitely going in my to do list.

Another F16 from Belgian Air Force for my tracker in Briancon

My tracker in Briancon has detected another F16 from the Belgian Air Component recently (that’s how the Belgian Air Force is called).

The aircraft detected so far have the tail numbers FA-87, FA-57, FA-133, FA-130, FA-101 and FA-72.

Belgian F16 are regularly flying over France.