{"id":7,"date":"2026-09-22T21:22:39","date_gmt":"2026-09-22T19:22:39","guid":{"rendered":"https:\/\/dropzone-software.com\/tandemedit-mac\/"},"modified":"2026-09-24T11:28:36","modified_gmt":"2026-09-24T09:28:36","slug":"tandemedit-mac","status":"publish","type":"page","link":"https:\/\/dropzone-software.com\/en\/tandemedit-mac\/","title":{"rendered":"TandemEdit Mac"},"content":{"rendered":"
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\n TandemEdit Mac \u00b7 the editing station<\/span>\n

Plug in the GoPro. Check three points. GO.<\/span><\/h1>\n

The station identifies the guest by QR code, finds exit, canopy opening and landing, and edits them into a main video, best-of, reel and photo selection. Processing runs on a Mac mini at your dropzone. The manifest then sends the link.<\/p>\n

\n Request a demo <\/svg><\/a>\n How an edit runs<\/a>\n <\/div>\n
\n <\/svg> In daily use on Mac mini M4<\/span>\n <\/svg> Camera is emptied only after the copy has been verified<\/span>\n <\/div>\n <\/div>\n
\n \"Illustration:\n
<\/svg><\/span>GoPro detected<\/b>11 clips \u00b7 checksum running<\/small><\/span><\/div>\n
<\/svg><\/span>Markers found<\/b>Exit \u00b7 Opening \u00b7 Landing<\/small><\/span><\/div>\n
<\/svg><\/span>Versions in progress<\/b>Video \u00b7 Best-of \u00b7 Reel \u00b7 Photos<\/small><\/span><\/div>\n <\/div>\n <\/div>\n<\/section>\n\n\n\n
In a guest's day<\/span>
  1. 01<\/i>Voucher and booking<\/span><\/li>
  2. 02<\/i>Check-in on the iPad<\/span><\/li>
  3. 03<\/i>Load and jump<\/span><\/li>
  4. 04<\/i>Editing at the station<\/span>This page<\/em><\/li>
  5. 05<\/i>Media page and purchase<\/span><\/li>
  6. 06<\/i>Settlement<\/span><\/li><\/ol><\/div><\/section>\n\n\n\n
    \n
    \n
    \n From the GoPro to the link<\/span>\n

    Nine steps. Three of them are done by a person.<\/h2>\n

    Before the jump the camera films a QR code, after landing it is plugged in, then a quick check. The station, NAS and manifest handle the rest, right up to the message to the guest.<\/p>\n <\/div>\n

    \n
    \n
    \n Lena Hoffmann<\/b>TM: Jonas \u00b7 Load L3 \u00b7 14:20<\/small>\n <\/svg>\n GM-2417<\/code>Point the GoPro at the QR code and film briefly<\/span>\n<\/div><\/div>\n
    Import<\/span><\/div>\n
      \n
    1. 01<\/span>

      Film the QR code<\/h4>

      Before boarding, the tandem instructor taps his guest on the staff terminal or at the station and films the QR code briefly with the GoPro. Forgot? Then the guest is picked from the day list at the station.<\/p>Tandem instructor<\/span><\/div><\/li>\n

    2. 02<\/span>

      Plug in, import with checksum<\/h4>

      After landing, the GoPro goes on the USB cable or the SD card into the card reader. The station loads all clips, verifies every file and finds the QR code along the way. Guest, load, tandem instructor and booked package come from the manifest.<\/p>Tandem instructor \u00b7 Station<\/span><\/div><\/li>\n

    3. 03<\/span>

      Empty the camera<\/h4>

      Only once the copy is confirmed is the GoPro emptied. Until then it says in red: do not unplug. \"Keep clips\" cancels the emptying.<\/p>Station<\/span><\/div><\/li>\n <\/ol>\n <\/div>\n

      \n
      \n
      Freefall start<\/span><\/i>Looks good<\/em><\/div>\n
      Freefall end<\/span><\/i>Looks good<\/em><\/div>\n
      Landing<\/span><\/i>Check<\/em><\/div>\n
      Markers correct?<\/span>YES<\/b><\/div>\n<\/div><\/div>\n
      Edit<\/span><\/div>\n
        \n
      1. 04<\/span>

        AI markers<\/h4>

        The analysis divides the clips into six scenes and sets exit, canopy opening and landing, each marker with a confidence score. For a typical jump this takes around 20 seconds.<\/p>Station<\/span><\/div><\/li>\n

      2. 05<\/span>

        Check and GO<\/h4>

        Three short loops, three traffic lights. If all are green, the edit starts by itself after a 30 second countdown; any intervention stops it. Otherwise the tandem instructor presses GO and confirms with \"Markers correct? YES\", or first drags a marker into place.<\/p>Tandem instructor<\/span><\/div><\/li>\n

      3. 06<\/span>

        Versions are created<\/h4>

        Main video, best-of, reel and photo selection. Whatever the guest has not booked is additionally created as a watermarked preview and is finished first.<\/p>Station<\/span><\/div><\/li>\n <\/ol>\n <\/div>\n

        \n
        \n
        \n
        <\/svg><\/span>Station<\/small><\/div><\/i>\n
        <\/svg><\/span>NAS<\/small><\/div><\/i>\n
        <\/svg><\/span>Cloud<\/small><\/div><\/i>\n
        <\/svg><\/span>Guest<\/small><\/div>\n <\/div>\n
        <\/svg><\/span>
        Email with link<\/b>from the manifest, by SMS as well if desired<\/small><\/div><\/div>\n<\/div><\/div>\n
        Deliver<\/span><\/div>\n
          \n
        1. 07<\/span>

          Backup to the NAS<\/h4>

          The project folder is copied to the NAS and verified by checksum. Only then does it count as backed up.<\/p>Station \u00b7 NAS<\/span><\/div><\/li>\n

        2. 08<\/span>

          Upload to cloud storage<\/h4>

          After an adjustable delay, a service on the NAS uploads the media: the full version if it has been purchased, otherwise the watermarked preview. If the internet goes down, it waits and continues afterwards.<\/p>NAS<\/span><\/div><\/li>\n

        3. 09<\/span>

          Link to the guest<\/h4>

          As soon as everything is available, the manifest sends the guest an email with their personal link, by SMS as well if desired.<\/p>Manifest<\/span><\/div><\/li>\n <\/ol>\n <\/div>\n <\/div>\n

          Simplified view with sample data<\/p>\n <\/div>\n<\/section>\n\n\n\n

          \n
          \n
          \n AI markers<\/span>\n

          When the jump begins is in the camera's data.<\/h2>\n

          Besides the picture, the GoPro also records motion data: acceleration around 200 times per second, plus rotation and gravity. Together with the audio and its own image AI, the station sets three markers: exit, canopy opening and landing.<\/p>\n <\/div>\n

          \n Scenes<\/text>six categories<\/text>Motion<\/text>GoPro sensors<\/text>Audio<\/text>Camera audio level<\/text>Image AI<\/text>Still frames per clip<\/text>Briefing<\/text>Walk to the<\/tspan>aircraft<\/tspan><\/text>Climb<\/text>Freefall<\/text>Canopy ride<\/text>Landed<\/text>1 g<\/text>Dip below 1 g<\/text>Opening shock<\/text>Touchdown jolt<\/text>Median 4.5 g<\/text>Exit<\/text>Opening<\/text>Landing<\/text><\/svg>\n Scenes<\/text>Motion<\/text>Audio<\/text>Image AI<\/text>Briefing<\/text>Walk to the<\/tspan>aircraft<\/tspan><\/text>Climb<\/text>Freefall<\/text>Canopy ride<\/text>Landed<\/text>1 g<\/text>Dip<\/text>below 1 g<\/text>Opening<\/text>shock<\/text>Touchdown<\/text>shock<\/text>Median<\/text>4.5 g<\/text>Exit<\/text>Opening<\/text>Landing<\/text><\/svg>\n
          Diagram of a jump, not to scale. The station works with the handcam clips; the scenes are summarized here.<\/figcaption>\n <\/figure>\n
          \n

          <\/i>Motion data<\/h3>

          At the exit, acceleration briefly drops below 1 g, the opening shows a clear shock, the touchdown a sharp one. Our own AI models derive the fine values from this.<\/p><\/div>\n

          <\/i>Audio<\/h3>

          In freefall the wind roars, under the canopy it goes quiet. The audio helps divide the scenes and detects the \"Landed\" scene even when the touchdown was not filmed.<\/p><\/div>\n

          <\/i>Image AI<\/h3>

          A scene AI looks at five still frames per clip. Together with the sensor and audio data, it assigns each clip to one of the six scenes.<\/p><\/div>\n <\/div>\n

          \n

          Every marker gets a confidence score.<\/b> A plausibility check requires exactly one freefall, the opening after the exit and the landing after that. If something does not fit, the score drops. This becomes the traffic light in the check:<\/p>\n