v.4.0.4

loop-calling.ipynb

@@ -2829,7 +2829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "1080c073",
    "metadata": {},
    "outputs": [

stage-1-read-garmin-img/readme.md

@@ -0,0 +1,115 @@
+I kept pushing it.
+
+The useful step here was not trying to turn GPXSee into an exporter wholesale, but using it as a reference model for Garmin object classification and parser structure while keeping the Python extractor focused on export. GPXSee does support Garmin IMG/GMAP offline maps, and OsmAndMapCreator’s documented shell modes include `generate-obf`, `generate-map`, `generate-poi`, and `generate-roads`, so we can separate “feature extraction works” from “routing index is still unstable.” ([gpxsee.org][1])
+
+I built a new revision here:
+
+[garmin_img_to_osmand_v4.py](sandbox:/mnt/data/garmin_img_to_osmand_v4.py)
+
+What changed in v4:
+
+* exact-coordinate point landmark export is now a first-class path
+* unique landmark type summaries can be exported to CSV and JSON
+* GPXSee-style class predicates are folded in as an additional taxonomy layer
+* water sources can be exported directly as CSV or GeoJSON
+* filtering now works by semantic tag, Garmin type/subtype, and GPXSee-style class names
+
+Sample outputs from your uploaded `02335140` mapset:
+
+* [water_sources_02335140.csv](sandbox:/mnt/data/water_sources_02335140.csv)
+* [water_sources_02335140.geojson](sandbox:/mnt/data/water_sources_02335140.geojson)
+* [landmark_types_02335140.csv](sandbox:/mnt/data/landmark_types_02335140.csv)
+* [landmark_types_02335140.json](sandbox:/mnt/data/landmark_types_02335140.json)
+
+On that sample mapset, the current parser found 4 exact-coordinate water-source points, all of Garmin type `0x64` subtype `0x14`, which the current semantic layer maps to `amenity=drinking_water`.
+
+Use it like this.
+
+List all exact-coordinate landmark types for a tile:
+
+```bash id="51801"
+python garmin_img_to_osmand_v4.py gmapsupp.img ^
+  --mapset 02335140 ^
+  --list-landmark-types
+```
+
+Export the landmark type summary in machine-readable form:
+
+```bash id="51802"
+python garmin_img_to_osmand_v4.py gmapsupp.img ^
+  --mapset 02335140 ^
+  --landmark-types-csv landmark_types.csv ^
+  --landmark-types-json landmark_types.json
+```
+
+Export all exact-coordinate point landmarks:
+
+```bash id="51803"
+python garmin_img_to_osmand_v4.py gmapsupp.img ^
+  --mapset 02335140 ^
+  --landmarks-csv points.csv ^
+  --landmarks-geojson points.geojson
+```
+
+Export only water sources:
+
+```bash id="51804"
+python garmin_img_to_osmand_v4.py gmapsupp.img ^
+  --mapset 02335140 ^
+  --category water_sources ^
+  --landmarks-csv water_sources.csv ^
+  --landmarks-geojson water_sources.geojson
+```
+
+Filter by semantic tag directly:
+
+```bash id="51805"
+python garmin_img_to_osmand_v4.py gmapsupp.img ^
+  --filter-tag amenity=drinking_water ^
+  --landmarks-csv drinking_water.csv
+```
+
+Filter by raw Garmin type:
+
+```bash id="51806"
+python garmin_img_to_osmand_v4.py gmapsupp.img ^
+  --filter-kind point ^
+  --filter-type 0x64 ^
+  --filter-subtype 0x14 ^
+  --landmarks-csv type_64_14.csv
+```
+
+Filter by GPXSee-style class:
+
+```bash id="51807"
+python garmin_img_to_osmand_v4.py gmapsupp.img ^
+  --gpxsee-class water_line ^
+  --geojson water_lines.geojson
+```
+
+The routing crash you posted is still a separate layer. The good news is that your later log proved the map index itself is no longer empty; the failure is in OsmAnd’s route-connectivity post-processing, not in basic OSM loading. Until the road topology export is cleaner, use:
+
+```bash id="51808"
+java.exe -Xms256M -Xmx4096M -cp "OsmAndMapCreator.jar;lib/*" ^
+  net.osmand.MainUtilities generate-map C:\path\to\out.osm.gz
+```
+
+or:
+
+```bash id="51809"
+java.exe -Xms256M -Xmx4096M -cp "OsmAndMapCreator.jar;lib/*" ^
+  net.osmand.MainUtilities generate-poi C:\path\to\out.osm.gz
+```
+
+Those modes are explicitly supported by OsmAndMapCreator’s documented utility commands. ([gpxsee.org][1])
+
+What still needs more reverse engineering:
+
+* better label decoding, especially where names are clearly truncated or mangled
+* fuller support for GPXSee-style extended/local/class fields on marine and special points
+* safer road export so `generate-obf` can survive the route phase
+* possibly Huffman-backed text/object decoding for maps that use those sections
+
+The next high-value target is the label path and extended point metadata, because that improves both landmark names and water-source extraction quality without waiting for full routing stability.
+
+[1]: https://www.gpxsee.org/doc "https://www.gpxsee.org/doc"

stage-2-parse-stage-1/readme.md

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+You have **two good OsmAnd targets** now.
+
+The fast, practical one is **GPX waypoint overlay**. OsmAnd supports GPX import, waypoint icons/colors, and **waypoint grouping** through OsmAnd-specific GPX extensions, so this is the closest match to a toggleable landmark layer without fighting the full map compiler. Favorites are also stored/imported as GPX waypoints, which confirms GPX is a native path for point overlays. ([OsmAnd][1])
+
+The more native/searchable one is **POI-only OBF**. OsmAndMapCreator officially supports `generate-poi` separately from `generate-obf`, and it accepts OSM-family input such as `.osm`, `.osm.gz`, `.osm.bz2`, and `.pbf`. That makes it the right target when you want searchable/filterable POIs but do **not** want the routing stage that is currently crashing. ([OsmAnd][2])
+
+I packaged both paths into a converter:
+
+[landmarks_csv_to_osmand.py](sandbox:/mnt/data/landmarks_csv_to_osmand.py)
+
+Sample outputs:
+
+* [sample GPX overlay](sandbox:/mnt/data/water_sources_02335140.gpx)
+* [sample OSM for POI build](sandbox:/mnt/data/water_sources_02335140.osm)
+
+### What I recommend
+
+For a “BGMountains-like thing I can switch on and off,” use **GPX overlays** first.
+For a “native searchable POI pack,” use **POI-only OBF** second.
+
+### Path 1: GPX overlay layer
+
+This is the quickest route.
+
+It converts your landmark CSVs into a GPX file with:
+
+* one waypoint per landmark
+* grouped categories using `<type>`
+* OsmAnd waypoint group styling via `osmand:points_groups`
+* icons/colors per category
+
+Build it like this:
+
+```bash id="14483"
+python landmarks_csv_to_osmand.py *.csv --gpx bg_landmarks.gpx --group-by category
+```
+
+For just water sources:
+
+```bash id="52386"
+python landmarks_csv_to_osmand.py *.csv --category water_sources --gpx bg_water_sources.gpx
+```
+
+Then import that GPX into OsmAnd. GPX waypoints and waypoint groups are supported officially, including icon/color/background customization. ([OsmAnd][1])
+
+**Best practice for toggling:** make **one GPX file per theme**:
+
+* `water_sources.gpx`
+* `villages.gpx`
+* `summits.gpx`
+* `springs.gpx`
+
+That gives you clean on/off control by file instead of stuffing everything into one blob.
+
+### Path 2: native POI pack for OsmAnd
+
+This is better when you want it to behave more like a built-in searchable POI dataset.
+
+First convert CSVs to OSM XML:
+
+```bash id="51512"
+python landmarks_csv_to_osmand.py *.csv --category water_sources --osm water_sources.osm
+```
+
+Then build a POI-only OBF:
+
+```bash id="68855"
+java.exe -Xms256M -Xmx4096M -cp "OsmAndMapCreator.jar;lib/*" ^
+  net.osmand.MainUtilities generate-poi C:\path\to\water_sources.osm
+```
+
+That uses OsmAndMapCreator’s documented `generate-poi` mode and avoids the routing stage entirely. ([OsmAnd][2])
+
+### Which one should you choose?
+
+Use **GPX** when you want:
+
+* fast import
+* easy toggle on/off
+* visual overlay behavior
+* category grouping with custom icons/colors
+
+Use **POI-only OBF** when you want:
+
+* more native OsmAnd integration
+* better POI-style searching/indexing
+* a cleaner long-term pack
+
+### Important limitation
+
+For now, your landmark CSV path is strongest for **exact-coordinate point features**. That is why it works well for springs, drinking water, villages, summits, and similar point landmarks. It is not the same as a full vector map overlay with arbitrary line/polygon filtering.
+
+### Ready-to-use commands
+
+All CSVs into one grouped GPX:
+
+```bash id="40684"
+python landmarks_csv_to_osmand.py *.csv --gpx all_landmarks.gpx --group-by category --summary-json all_landmarks_summary.json
+```
+
+Only water sources into GPX:
+
+```bash id="33043"
+python landmarks_csv_to_osmand.py *.csv --category water_sources --gpx water_sources.gpx
+```
+
+Only water sources into OSM for POI build:
+
+```bash id="57957"
+python landmarks_csv_to_osmand.py *.csv --category water_sources --osm water_sources.osm
+```
+
+Only named villages into GPX:
+
+```bash id="12262"
+python landmarks_csv_to_osmand.py *.csv --filter-tag place=village --named-only --gpx villages.gpx
+```
+
+The cleanest deployment pattern is: **one GPX per category for toggles, plus one POI-only OBF per high-value category for native search**.
+
+[1]: https://osmand.net/docs/technical/osmand-file-formats/osmand-gpx/?utm_source=chatgpt.com "OsmAnd GPX | OsmAnd"
+[2]: https://osmand.net/docs/technical/map-creation/create-offline-maps-yourself/?utm_source=chatgpt.com "Create Offline Raster & Vector Maps | OsmAnd"