Version: QGIS 4.0.2 (Final) on macOS

Project file: ~/Sync/farm/emlid-exports/brownsville-property.qgz

Primary data source: PostGIS on CT100 (farmdb). QGIS connects directly via the PostGIS connector — host 192.168.8.100, port 5432, database farmdb, user farmuser, SSL disabled. All points and geometries live there; GIS state stays single-source-of-truth instead of drifting between a GeoPackage copy and the DB.

GeoPackage (brownsville-points.gpkg) is retired as the primary format (May 2026). It still exists as a backup snapshot of points but isn’t kept in sync — for live data use the PostGIS connection. ArcGIS Pro instructions further down still cover the GPKG path for portability.

The QGIS project is generated by a Python script (brownsville-property.py or setup-project.py for the console version) and includes:

• ESRI World Imagery satellite basemap
• All GPS points from all-points.csv, loaded as a delimited text layer (EPSG:4326)
• Categorized symbology by feature code:

• White labels with black buffer showing feature names
• Zoomed to point extent with ~100m padding

Regenerating the project:

From the QGIS Python Console (Plugins → Python Console → Show Editor):

exec(open(os.path.expanduser('~/Sync/farm/emlid-exports/setup-project.py')).read())

Or standalone from Terminal:

/Applications/QGIS-final-4_0_2.app/Contents/MacOS/bin/python3 \
  ~/Sync/farm/emlid-exports/brownsville-property.py

Adding new sessions: After running the processing pipeline, update all-points.csv with the new session data, then reload the layer in QGIS (right-click layer → Reload). The symbology and labels persist.

ArcGIS Pro can work with the same data sources. The key consideration is coordinate system alignment and data format.

Importing GPS Data

From CSV (all-points.csv):

1. Open ArcGIS Pro → Insert → New Map
2. Map tab → Add Data → XY Table to Point
3. Browse to ~/Sync/farm/emlid-exports/processed/all-points.csv
4. Set X Field = lon, Y Field = lat, Z Field = elev_m
5. Coordinate System = GCS_WGS_1984 (EPSG:4326)
6. Click Run

From GeoPackage:

1. Catalog pane → right-click Databases → Add Database
2. Browse to brownsville-points.gpkg
3. Drag the points layer onto the map

Coordinate System Notes

• Emlid data is in WGS84 geographic (lat/lon, EPSG:4326). ArcGIS Pro will project on-the-fly for display.
• For measurement accuracy, project to Ohio State Plane South (EPSG:3735, US feet) or UTM Zone 17N (EPSG:32617, meters).
• Heights are ellipsoidal, not orthometric. Apply GEOID18 via the Transformation tool if you need MSL elevations.
• To convert: Geoprocessing → Project → set output coordinate system and check “Geographic Transformations” to include the geoid.

Symbology

To replicate the QGIS categorized symbology:

1. Right-click the points layer → Symbology
2. Change from Single Symbol to Unique Values
3. Field = code
4. Add values: p (green circle), i (gray square), u (orange diamond), n (blue triangle)
5. Apply

Labeling

1. Right-click layer → Label Properties
2. Label Field = feature_name
3. Set halo (white text, 1.5pt black buffer) for readability over satellite imagery

Basemap

ArcGIS Pro includes built-in satellite imagery:

• Map tab → Basemap → Imagery (or Imagery Hybrid for labels)
• This is higher resolution than the ESRI XYZ tile service used in QGIS

Future: ArcGIS Field Maps

ArcGIS Field Maps on iOS cannot directly connect to the RS3 (not MFi-certified). Two potential workarounds:

1. Bluetooth GPS forwarding — third-party apps that relay NMEA from the RS3 to the iOS location stack (unreliable, not recommended)
2. Post-process in ArcGIS Pro — continue using Emlid Flow for field capture, import to ArcGIS Pro for analysis. This is the recommended approach.
3. Android tablet — Field Maps on Android can connect to the RS3 via mock location. QField is another option.

Future pipeline: all-points.csv → PostGIS on CT100 farmdb → queryable via Farm MCP tools, QGIS, and ArcGIS Pro.

Planned tables:

• gis_points — all raw GPS captures with geometry
• plantings — plants with species FK, planting date, zone
• infrastructure — above-ground features
• underground — buried utilities (critical for future excavation)

Planned Farm MCP spatial tools:

• nearest_features(lat, lon, radius_m) — find what’s nearby
• query_in_radius(lat, lon, radius_m, code) — filter by feature type
• underground_near(lat, lon, radius_m) — specifically for avoiding buried lines