diff --git a/work_trace/wizards/wt_import_timeline_wizard.py b/work_trace/wizards/wt_import_timeline_wizard.py
index feddc63a..f9c2a722 100644
--- a/work_trace/wizards/wt_import_timeline_wizard.py
+++ b/work_trace/wizards/wt_import_timeline_wizard.py
@@ -21,7 +21,8 @@ SEMANTIC_TYPE_CATEGORY = {
     'WORK': 'Work',
     'INFERRED_WORK': 'Work',
     'SEARCHED_ADDRESS': 'Searched Address',
-    'UNKNOWN': '',
+    'ALIASED_LOCATION': 'Saved Place',
+    'UNKNOWN': 'Unknown',
 }
 
 
@@ -45,8 +46,6 @@ def _activity_type_to_travel_mode(activity_type):
         return None
     if t == 'IN_RAIL_VEHICLE':
         return 'transit'
-    if t == 'IN_PASSENGER_VEHICLE' or t == 'FLYING':
-        return 'flying'
     if t in VEHICLE_ACTIVITIES:
         return 'driving'
     if t in WALKING_ACTIVITIES:
@@ -57,7 +56,7 @@ def _activity_type_to_travel_mode(activity_type):
 
 
 def _speed_to_travel_mode(max_speed_mph):
-    """Infer travel mode from maximum speed observed along a path."""
+    """Infer travel mode from maximum speed observed."""
     if max_speed_mph > 150:
         return 'flying'
     if max_speed_mph > 15:
@@ -115,6 +114,29 @@ def _max_speed_from_path(timeline_path):
     return max_speed
 
 
+def _speed_from_activity_segment(activity, seg_start_ts, seg_end_ts):
+    """Calculate implied speed from an activity segment's start/end coords and timestamps.
+
+    Flights appear as UNKNOWN_ACTIVITY_TYPE with valid start/end latLng but no
+    timelinePath GPS trail, so we derive speed directly from the segment geometry.
+    """
+    start_latlng = activity.get('start', {}).get('latLng', '')
+    end_latlng = activity.get('end', {}).get('latLng', '')
+    if not start_latlng or not end_latlng:
+        return 0.0
+    slat, slng = _parse_latlng(start_latlng)
+    elat, elng = _parse_latlng(end_latlng)
+    if slat is None or elat is None:
+        return 0.0
+    if not seg_start_ts or not seg_end_ts:
+        return 0.0
+    dt_hours = (seg_end_ts - seg_start_ts).total_seconds() / 3600.0
+    if dt_hours <= 0:
+        return 0.0
+    dist_miles = _haversine_miles(slat, slng, elat, elng)
+    return dist_miles / dt_hours
+
+
 class WtImportTimelineWizard(models.TransientModel):
     _name = 'wt.import.timeline.wizard'
     _description = 'Import Google Timeline'
@@ -250,34 +272,45 @@ class WtImportTimelineWizard(models.TransientModel):
     def _parse_semantic_timeline(self, data):
         """Parse Timeline.json (semanticSegments format).
 
-        Travel mode is determined in priority order:
-          1. activity segment topCandidate.type (if not UNKNOWN)
-          2. max speed calculated from preceding timelinePath points
-          3. 'unknown' as fallback
+        Travel mode priority:
+          1. Explicit activity type (if not UNKNOWN)
+          2. Speed inferred from activity segment start/end coords + timestamps
+             (catches flights that have no timelinePath GPS trail)
+          3. Max speed calculated from preceding timelinePath points
+          4. 'unknown' as fallback
         """
         stops = []
         segments = data.get('semanticSegments', [])
 
-        # We scan segments in order, accumulating travel signals between visits
         pending_mode = 'unknown'
-        pending_path_points = []   # all timelinePath points seen since last visit
+        pending_path_points = []
 
         for seg in segments:
-            # --- timelinePath: collect GPS path points for speed inference ---
+            # --- timelinePath: collect GPS points for speed inference ---
             if 'timelinePath' in seg:
                 pending_path_points.extend(seg['timelinePath'])
                 continue
 
-            # --- activity segment: try to get explicit travel mode ---
+            # --- activity segment ---
             if 'activity' in seg:
                 activity = seg['activity']
                 top = activity.get('topCandidate', {})
                 mode = _activity_type_to_travel_mode(top.get('type', ''))
                 if mode:
+                    # Explicit type wins (driving, walking, transit, cycling)
                     pending_mode = mode
+                else:
+                    # UNKNOWN type — infer from segment distance / elapsed time
+                    seg_start = _parse_ts(seg.get('startTime'))
+                    seg_end = _parse_ts(seg.get('endTime'))
+                    speed = _speed_from_activity_segment(activity, seg_start, seg_end)
+                    if speed > 0:
+                        inferred = _speed_to_travel_mode(speed)
+                        if inferred != 'unknown':
+                            pending_mode = inferred
                 continue
 
-            # --- visit segment: the stop we care about ---
+            # --- visit segment: the stop we record ---
             if 'visit' in seg:
                 start_ts = _parse_ts(seg.get('startTime'))
                 end_ts = _parse_ts(seg.get('endTime'))
@@ -287,16 +320,16 @@ class WtImportTimelineWizard(models.TransientModel):
                     continue
 
                 candidate = seg['visit'].get('topCandidate', {})
-                semantic_type = candidate.get('semanticType', '')
+                semantic_type = candidate.get('semanticType', 'UNKNOWN')
                 latlng_str = candidate.get('placeLocation', {}).get('latLng', '')
                 lat, lng = _parse_latlng(latlng_str) if latlng_str else (None, None)
 
-                category = SEMANTIC_TYPE_CATEGORY.get(semantic_type, '')
-                if not category and semantic_type:
-                    category = semantic_type.replace('_', ' ').title()
+                category = SEMANTIC_TYPE_CATEGORY.get(
+                    semantic_type,
+                    semantic_type.replace('_', ' ').title() if semantic_type else 'Unknown'
+                )
 
-                # Determine travel mode: explicit activity type wins,
-                # otherwise infer from max speed along the preceding path
+                # Determine travel mode
                 travel_mode = pending_mode
                 if travel_mode == 'unknown' and pending_path_points:
                     max_speed = _max_speed_from_path(pending_path_points)
@@ -312,12 +345,11 @@ class WtImportTimelineWizard(models.TransientModel):
                     'travel_mode': travel_mode,
                 })
 
-                # Reset for next inter-visit gap
                 pending_mode = 'unknown'
                 pending_path_points = []
                 continue
 
-            # timelineMemory or other unknown segment types — ignore
+            # timelineMemory or other segment types — ignore
             pass
 
         return stops