fio-l1-vienna-shapefile-recovery
Map (reference ← swipe → agent)
0:00
I'm migrating an old parcel shapefile (`parcels.shp`) into our web viewer, and a reference file `column_map.csv` is provided alongside it. Can you produce a clean GeoJSON `parcels.geojson` with the correct full column names and proper character encoding? The features should come through as Polygon or MultiPolygon.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:07
Let me start by reading the input files to understand their structure.
0:07
Bash
ls -la /work/
total 56 drwxr-xr-x 1 agent agent 138 Jun 15 16:38 . drwxr-xr-x 1 root root 14 Jun 15 16:38 .. -rw-r--r-- 1 agent agent 188 Jun 15 16:38 column_map.csv -rw-r--r-- 1 agent agent 6 Jun 15 16:38 parcels.cpg -rw-r--r-- 1 agent agent 25726 Jun 15 16:38 parcels.dbf -rw-r--r-- 1 agent agent 457 Jun 15 16:38 parcels.prj -rw-r--r-- 1 agent agent 8260 Jun 15 16:38 parcels.shp -rw-r--r-- 1 agent agent 580 Jun 15 16:38 parcels.shx
0:13
Let me inspect the input files: the shapefile components and the column mapping CSV.
0:13
Read
/work/column_map.csv
1 truncated,original
2 KATASTRALG,KATASTRALGEMEINDE_NAME
3 GRUNDSTUEC,GRUNDSTUECKSNUMMER
4 EIGENTUEME,EIGENTUEMER_NAME
5 WIDMUNG_BE,WIDMUNG_BEZEICHNUNG
6 STRASSE_NA,STRASSE_NAME
7 FLAECHE_M2,FLAECHE_M2
0:13
Read
/work/parcels.cpg
1 CP1252
0:13
Bash
python -c "
python -c "
import geopandas as gpd
gdf = gpd.read_file('/work/parcels.shp')
print('Columns:', list(gdf.columns))
print('CRS:', gdf.crs)
print('Geometry types:', gdf.geometry.type.unique())
print('Shape:', gdf.shape)
print()
print(gdf.head(3).to_string())
print()
print('dtypes:')
print(gdf.dtypes)
"
Columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
CRS: EPSG:31287
Geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
Shape: (60, 7)
KATASTRALG GRUNDSTUEC EIGENTUEME WIDMUNG_BE STRASSE_NA FLAECHE_M2 geometry
0 Innere Stadt 0013/1 Müller GmbH Wohngebiet Mariahilfer Straße 750.00 POLYGON ((625700 483400, 625700 483425, 625730 483425, 625730 483400, 625700 483400))
1 Mariahilf 0020/2 Schönbrunner Bauges. m.b.H. Wohngebiet Döblinger Hauptstraße 751.25 POLYGON ((625732 483400, 625732 483425, 625762 483425, 625762 483400, 625732 483400))
2 Währing 0027/3 Müller GmbH Wohngebiet Währinger Gürtel 752.50 POLYGON ((625764 483400, 625764 483425, 625794 483425, 625794 483400, 625764 483400))
dtypes:
KATASTRALG str
GRUNDSTUEC str
EIGENTUEME str
WIDMUNG_BE str
STRASSE_NA str
FLAECHE_M2 float64
geometry geometry
dtype: object
0:26
Let me understand the task: 1. Read `parcels.shp` (shapefile) 2. Use `column_map.csv` to rename truncated columns to full names 3. Ensure proper character encoding (CP1252 per the .cpg file) 4. Output as GeoJSON `parcels.geojson` with Polygon or MultiPolygon geometry types 5. CRS is EPSG:31287 (Austria Lambert) Let me look at the column map more carefully: - KATASTRALG → KATASTRALGEMEINDE_NAME - GRUNDSTUEC → GRUNDSTUECKSNUMMER - EIGENTUEME → EIGENTUEMER_NAME - WIDMUNG_BE → WIDMUNG_BEZEICHNUNG - STRASSE_NA → STRASSE_NAME - FLAECHE_M2 → FLAECHE_M2 (stays the same) The encoding is CP1252 per the .cpg file. I need to make sure the output GeoJSON preserves the proper encoding. GeoJSON is UTF-8 by default, so I need to ensure the data is properly decoded from CP1252 first. Let me write a script to do this.
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0:26
Write
/work/solve.py
content · 52 lines · py
import geopandas as gpd
import pandas as pd
import json
# 1. Read shapefile with explicit CP1252 encoding for the DBF text fields
gdf = gpd.read_file('/work/parcels.shp', encoding='CP1252')
print("Original columns:", list(gdf.columns))
print("Geometry types:", gdf.geometry.type.unique())
print("Shape:", gdf.shape)
# 2. Read column mapping
col_map = pd.read_csv('/work/column_map.csv')
# Build rename dict: truncated -> original
rename_dict = {}
for _, row in col_map.iterrows():
parts = row.iloc[0].split(',')
if len(parts) == 2:
trunc, orig = parts
trunc = trunc.strip()
orig = orig.strip()
if trunc and orig and trunc != orig:
rename_dict[trunc] = orig
print("Rename mapping:", rename_dict)
# 3. Rename columns
gdf = gdf.rename(columns=rename_dict)
# 4. Ensure geometry is strictly Polygon or MultiPolygon
# If any geometry type is something else, try to convert
actual_types = gdf.geometry.type.unique()
print("Geometry types after load:", actual_types)
# 5. Write as GeoJSON
# GeoJSON spec requires WGS84 (EPSG:4326) but for modern web viewers
# the GeoJSON can be in any CRS. However, best practice is to reproject to WGS84
# so the web viewer can display it properly.
# Let's check the CRS first.
print("Original CRS:", gdf.crs)
# Most web viewers expect WGS84 (lon/lat) in GeoJSON.
# EPSG:31287 is Austria Lambert (metric). Let's reproject.
gdf_wgs84 = gdf.to_crs('EPSG:4326')
print("Reprojected CRS:", gdf_wgs84.crs)
# Drop CRS for GeoJSON output (GeoJSON is natively WGS84)
gdf_wgs84.to_file('/work/parcels.geojson', driver='GeoJSON', encoding='utf-8')
print("Done! Output columns:", list(gdf_wgs84.columns))
print("Output CRS:", gdf_wgs84.crs)
print("Number of features:", len(gdf_wgs84))Wrote /work/solve.py (1762 chars).
0:29
Let me run the script and then verify the output.
0:29
Bash
cd /work && python solve.py
Original columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
Geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
Shape: (60, 7)
Rename mapping: {}
Geometry types after load: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
Original CRS: EPSG:31287
Reprojected CRS: EPSG:4326
Done! Output columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
Output CRS: EPSG:4326
Number of features: 60
0:40
The rename dict is empty. Let me check why. The CSV has a header row "truncated,original" and data rows. Let me look at it more carefully.
From the Read output:
```
1 truncated,original
2 KATASTRALG,KATASTRALGEMEINDE_NAME
3 GRUNDSTUEC,GRUNDSTUECKSNUMMER
4 EIGENTUEME,EIGENTUEMER_NAME
5 WIDMUNG_BE,WIDMUNG_BEZEICHNUNG
6 STRASSE_NA,STRASSE_NAME
7 FLAECHE_M2,FLAECHE_M2
```
So the CSV has:
- Row 1: header "truncated,original"
- Row 2: "KATASTRALG,KATASTRALGEMEINDE_NAME"
- Row 3: "GRUNDSTUEC,GRUNDSTUECKSNUMMER"
- Row 4: "EIGENTUEME,EIGENTUEMER_NAME"
- Row 5: "WIDMUNG_BE,WIDMUNG_BEZEICHNUNG"
- Row 6: "STRASSE_NA,STRASSE_NAME"
- Row 7: "FLAECHE_M2,FLAECHE_M2"
The CSV is read with default header, so the column name becomes "truncated,original". Then iterating over rows, row.iloc[0] gives the whole line as a string. Wait, but if the CSV is read properly, it should split by comma and have two columns. Let me debug this.
Actually, looking at the file more carefully — the readme output shows:
```
1 truncated,original
2 KATASTRALG,KATASTRALGEMEINDE_NAME
```
Notice line 1 doesn't have spaces after the comma, but line 2 does: `KATASTRALG,KATASTRALGEMEINDE_NAME` — no wait, there are no visible spaces there. Let me re-read the CSV with more detail.
0:40
0:40
Bash
cd /work && python -c "
cd /work && python -c "
import pandas as pd
df = pd.read_csv('/work/column_map.csv')
print('Columns:', list(df.columns))
print('Shape:', df.shape)
print()
print(df.to_string())
print()
for _, row in df.iterrows():
print(repr(row.iloc[0]))
"
Columns: ['truncated', 'original']
Shape: (6, 2)
truncated original
0 KATASTRALG KATASTRALGEMEINDE_NAME
1 GRUNDSTUEC GRUNDSTUECKSNUMMER
2 EIGENTUEME EIGENTUEMER_NAME
3 WIDMUNG_BE WIDMUNG_BEZEICHNUNG
4 STRASSE_NA STRASSE_NAME
5 FLAECHE_M2 FLAECHE_M2
'KATASTRALG'
'GRUNDSTUEC'
'EIGENTUEME'
'WIDMUNG_BE'
'STRASSE_NA'
'FLAECHE_M2'
0:50
The CSV has a header row "truncated,original" and 6 data rows. My initial parsing was wrong - I was iterating rows and splitting on comma, but the CSV already has proper columns. Let me fix the script.
0:50
0:50
Write
/work/solve.py
content · 43 lines · py
import geopandas as gpd
import pandas as pd
# 1. Read shapefile with explicit CP1252 encoding for the DBF text fields
gdf = gpd.read_file('/work/parcels.shp', encoding='CP1252')
print("Original columns:", list(gdf.columns))
# 2. Read column mapping (already has header: truncated,original)
col_map = pd.read_csv('/work/column_map.csv')
print("Column map:")
print(col_map.to_string())
# Build rename dict
rename_dict = {}
for _, row in col_map.iterrows():
trunc = row['truncated'].strip()
orig = row['original'].strip()
if trunc in gdf.columns and trunc != orig:
rename_dict[trunc] = orig
print("Rename mapping:", rename_dict)
# 3. Rename columns
gdf = gdf.rename(columns=rename_dict)
print("Renamed columns:", list(gdf.columns))
# 4. Check geometry types
print("Geometry types:", gdf.geometry.type.unique())
# 5. Reproject to WGS84 for web GeoJSON consumption
gdf_wgs84 = gdf.to_crs('EPSG:4326')
# 6. Write as GeoJSON
gdf_wgs84.to_file('/work/parcels.geojson', driver='GeoJSON', encoding='utf-8')
print("Done! Output columns:", list(gdf_wgs84.columns))
print("Output CRS:", gdf_wgs84.crs)
print("Number of features:", len(gdf_wgs84))
# Quick sanity check
print("\nFirst row sample:", gdf_wgs84.iloc[0].to_dict())Wrote /work/solve.py (1244 chars).
0:55
Let me run the corrected script.
0:55
Bash
cd /work && python solve.py
Original columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
Column map:
truncated original
0 KATASTRALG KATASTRALGEMEINDE_NAME
1 GRUNDSTUEC GRUNDSTUECKSNUMMER
2 EIGENTUEME EIGENTUEMER_NAME
3 WIDMUNG_BE WIDMUNG_BEZEICHNUNG
4 STRASSE_NA STRASSE_NAME
5 FLAECHE_M2 FLAECHE_M2
Rename mapping: {'KATASTRALG': 'KATASTRALGEMEINDE_NAME', 'GRUNDSTUEC': 'GRUNDSTUECKSNUMMER', 'EIGENTUEME': 'EIGENTUEMER_NAME', 'WIDMUNG_BE': 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NA': 'STRASSE_NAME'}
Renamed columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry']
Geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
Done! Output columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry']
Output CRS: EPSG:4326
Number of features: 60
First row sample: {'KATASTRALGEMEINDE_NAME': 'Innere Stadt', 'GRUNDSTUECKSNUMMER': '0013/1', 'EIGENTUEMER_NAME': 'Müller GmbH', 'WIDMUNG_BEZEICHNUNG': 'Wohngebiet', 'STRASSE_NAME': 'Mariahilfer Straße', 'FLAECHE_M2': 750.0, 'geometry': <POLYGON ((16.371 48.21, 16.371 48.21, 16.371 48.21, 16.371 48.21, 16.371 48...>}
1:00
The script ran successfully. Let me verify the output file and check the GeoJSON content.
1:00
1:00
Bash
cd /work && python -c "
cd /work && python -c "
import json
with open('parcels.geojson', 'r', encoding='utf-8') as f:
data = json.load(f)
print('Type:', data['type'])
print('CRS:', data.get('crs'))
print('Feature count:', len(data['features']))
print()
feat = data['features'][0]
print('Geometry type:', feat['geometry']['type'])
print('Properties:', json.dumps(feat['properties'], ensure_ascii=False))
" && echo "---" && wc -c parcels.geojson
Type: FeatureCollection
CRS: {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}}
Feature count: 60
Geometry type: Polygon
Properties: {"KATASTRALGEMEINDE_NAME": "Innere Stadt", "GRUNDSTUECKSNUMMER": "0013/1", "EIGENTUEMER_NAME": "Müller GmbH", "WIDMUNG_BEZEICHNUNG": "Wohngebiet", "STRASSE_NAME": "Mariahilfer Straße", "FLAECHE_M2": 750.0}
---
31875 parcels.geojson
1:16
Everything looks good: 1. **Column renaming** ✓ — All truncated columns mapped to full names via `column_map.csv`: - `KATASTRALG` → `KATASTRALGEMEINDE_NAME` - `GRUNDSTUEC` → `GRUNDSTUECKSNUMMER` - `EIGENTUEME` → `EIGENTUEMER_NAME` - `WIDMUNG_BE` → `WIDMUNG_BEZEICHNUNG` - `STRASSE_NA` → `STRASSE_NAME` - `FLAECHE_M2` → `FLAECHE_M2` (same, not renamed) 2. **Character encoding** ✓ — Read with CP1252 (from the .cpg file), written as UTF-8. Special chars like "ü" in "Müller", "ß" in "Straße" are preserved. 3. **Geometry types** ✓ — All are Polygon (no MultiPolygon but that's fine). 4. **CRS** ✓ — Reprojected from EPSG:31287 (Austria Lambert) to EPSG:4326 (WGS84) for web viewer compatibility. GeoJSON CRS is CRS84. 5. **Output file** ✓ — `parcels.geojson` exists at 31,875 bytes with 60 features.
1:16