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"
}
0:03
Let me start by reading the input files to understand what we're working with.
0:03
Bash
ls -la /work/
total 56 drwxr-xr-x 1 agent agent 138 Jun 18 14:20 . drwxr-xr-x 1 root root 14 Jun 18 14:20 .. -rw-r--r-- 1 agent agent 188 Jun 18 14:20 column_map.csv -rw-r--r-- 1 agent agent 6 Jun 18 14:20 parcels.cpg -rw-r--r-- 1 agent agent 25726 Jun 18 14:20 parcels.dbf -rw-r--r-- 1 agent agent 457 Jun 18 14:20 parcels.prj -rw-r--r-- 1 agent agent 8260 Jun 18 14:20 parcels.shp -rw-r--r-- 1 agent agent 580 Jun 18 14:20 parcels.shx
0:07
Let me first examine the column_map.csv and the shapefile attributes to understand the mapping.
0:07
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
0:07
Bash
cat /work/parcels.cpg
CP1252
0:07
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/parcels.shp')
print('Columns:', list(gdf.columns))
print('CRS:', gdf.crs)
print('Num rows:', len(gdf))
print('Geometry types:', gdf.geometry.type.unique())
print()
print(gdf.head())
print()
for c in gdf.columns:
print(f'{c}: dtype={gdf[c].dtype}, sample={gdf[c].iloc[0]!r}')
"
Columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
CRS: EPSG:31287
Num rows: 60
Geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
KATASTRALG ... geometry
0 Innere Stadt ... POLYGON ((625700 483400, 625700 483425, 625730...
1 Mariahilf ... POLYGON ((625732 483400, 625732 483425, 625762...
2 Währing ... POLYGON ((625764 483400, 625764 483425, 625794...
3 Döbling ... POLYGON ((625796 483400, 625796 483425, 625826...
4 Hütteldorf ... POLYGON ((625828 483400, 625828 483425, 625858...
[5 rows x 7 columns]
KATASTRALG: dtype=str, sample='Innere Stadt'
GRUNDSTUEC: dtype=str, sample='0013/1'
EIGENTUEME: dtype=str, sample='Müller GmbH'
WIDMUNG_BE: dtype=str, sample='Wohngebiet'
STRASSE_NA: dtype=str, sample='Mariahilfer Straße'
FLAECHE_M2: dtype=float64, sample=np.float64(750.0)
geometry: dtype=geometry, sample=<POLYGON ((625700 483400, 625700 483425, 625730 483425, 625730 483400, 62570...>
0:16
Let me analyze the situation: 1. The shapefile has truncated column names (10 chars max due to DBF limitations): - `KATASTRALG` → `KATASTRALGEMEINDE_NAME` - `GRUNDSTUEC` → `GRUNDSTUECKSNUMMER` - `EIGENTUEME` → `EIGENTUEMER_NAME` - `WIDMUNG_BE` → `WIDMUNG_BEZEICHNUNG` - `STRASSE_NA` → `STRASSE_NAME` - `FLAECHE_M2` → `FLAECHE_M2` (same) 2. The encoding is CP1252 (from `.cpg` file), and there are special characters like "ü", "ß", "ä", "ö" in the data. 3. The CRS is EPSG:31287 (MGI / Austria GK Central). 4. All geometries are Polygon (single polygons), no MultiPolygon yet. 5. I need to produce `parcels.geojson` with: - Proper full column names - Proper character encoding (UTF-8 for GeoJSON) - MultiPolygon or Polygon geometries Let me write a script to do this.
0:16
0:16
Write
/work/solve.py
content · 30 lines · py
import geopandas as gpd
import pandas as pd
# 1. Read the column mapping
col_map = pd.read_csv("/work/column_map.csv")
rename_dict = dict(zip(col_map["truncated"], col_map["original"]))
print("Rename dict:", rename_dict)
# 2. Read the shapefile with explicit CP1252 encoding
gdf = gpd.read_file("/work/parcels.shp", encoding="CP1252")
print("Original columns:", list(gdf.columns))
print("CRS:", gdf.crs)
# 3. Rename columns
gdf = gdf.rename(columns=rename_dict)
print("Renamed columns:", list(gdf.columns))
# 4. Ensure all geometries are Polygon or MultiPolygon
# If any are single Polygon, keep as Polygon; MultiPolygon stays MultiPolygon.
# GeoJSON handles both natively. We'll do a simple validation.
if not all(gdf.geometry.type.isin(["Polygon", "MultiPolygon"])):
raise ValueError(f"Unexpected geometry types: {gdf.geometry.type.unique()}")
# 5. Write as GeoJSON with UTF-8 encoding (default for GeoJSON)
gdf.to_file("/work/parcels.geojson", driver="GeoJSON")
print("Done — wrote /work/parcels.geojson")Wrote /work/solve.py (1027 chars).
0:20
Wait, let me check the column_map.csv more carefully. The header is "truncated,original" and the data has pairs like KATASTRALG,KATASTRALGEMEINDE_NAME. So the first data row is line 2. Let me verify with pandas that the read is correct. Also I should double-check that the column mapping covers all columns. The shapefile has 6 attribute columns (non-geometry). The map has 6 rows of data. Let me check.
0:20
Bash
python3 -c "
python3 -c "
import pandas as pd
col_map = pd.read_csv('/work/column_map.csv')
print(col_map)
print()
print('Truncated:', col_map['truncated'].tolist())
print('Original:', col_map['original'].tolist())
"
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 Truncated: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2'] Original: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2']
0:37
Wait — looking at the CSV file more carefully, the first line says `truncated,original` which means the CSV has a header row. So when I read with pandas, the column names will be `truncated` and `original`. But earlier I read the raw file and it showed: ``` truncated,original KATASTRALG,KATASTRALGEMEINDE_NAME GRUNDSTUEC,GRUNDSTUECKSNUMMER ... ``` So the CSV has a header. My script reads it correctly with `pd.read_csv()` — it will parse the header row and the columns will be `truncated` and `original`. The rename dict should be fine. Let me run the script.
0:37
Bash
python3 /work/solve.py
Rename dict: {'KATASTRALG': 'KATASTRALGEMEINDE_NAME', 'GRUNDSTUEC': 'GRUNDSTUECKSNUMMER', 'EIGENTUEME': 'EIGENTUEMER_NAME', 'WIDMUNG_BE': 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NA': 'STRASSE_NAME', 'FLAECHE_M2': 'FLAECHE_M2'}
Original columns: ['KATASTRALG', 'GRUNDSTUEC', 'EIGENTUEME', 'WIDMUNG_BE', 'STRASSE_NA', 'FLAECHE_M2', 'geometry']
CRS: EPSG:31287
Renamed columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry']
Done — wrote /work/parcels.geojson
0:42
Let me verify the output file exists and do a quick sanity check.
0:42
Bash
ls -la /work/parcels.geojson && python3 -c "
ls -la /work/parcels.geojson && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/parcels.geojson')
print('Columns:', list(gdf.columns))
print('CRS:', gdf.crs)
print('Num features:', len(gdf))
print('Geometry types:', gdf.geometry.type.unique())
print()
print(gdf.head())
print()
# Check encoding — content should have umlauts properly
print('Sample special chars:')
print(' EIGENTUEMER_NAME:', gdf['EIGENTUEMER_NAME'].iloc[0])
print(' STRASSE_NAME:', gdf['STRASSE_NAME'].iloc[0])
print(' KATASTRALGEMEINDE_NAME:', gdf['KATASTRALGEMEINDE_NAME'].iloc[2])
"
-rw-r--r-- 1 agent agent 25942 Jun 18 14:20 /work/parcels.geojson Columns: ['KATASTRALGEMEINDE_NAME', 'GRUNDSTUECKSNUMMER', 'EIGENTUEMER_NAME', 'WIDMUNG_BEZEICHNUNG', 'STRASSE_NAME', 'FLAECHE_M2', 'geometry'] CRS: EPSG:31287 Num features: 60 Geometry types: <ArrowStringArray> ['Polygon'] Length: 1, dtype: str KATASTRALGEMEINDE_NAME ... geometry 0 Innere Stadt ... POLYGON ((625700 483400, 625700 483425, 625730... 1 Mariahilf ... POLYGON ((625732 483400, 625732 483425, 625762... 2 Währing ... POLYGON ((625764 483400, 625764 483425, 625794... 3 Döbling ... POLYGON ((625796 483400, 625796 483425, 625826... 4 Hütteldorf ... POLYGON ((625828 483400, 625828 483425, 625858... [5 rows x 7 columns] Sample special chars: EIGENTUEMER_NAME: Müller GmbH STRASSE_NAME: Mariahilfer Straße KATASTRALGEMEINDE_NAME: Währing
0:54
Everything checks out: 1. The file `/work/parcels.geojson` exists. 2. Column names are the full names from `column_map.csv`: `KATASTRALGEMEINDE_NAME`, `GRUNDSTUECKSNUMMER`, `EIGENTUEMER_NAME`, `WIDMUNG_BEZEICHNUNG`, `STRASSE_NAME`, `FLAECHE_M2`. 3. CRS is EPSG:31287 (preserved from input). 4. Geometry types are all Polygon. 5. Special characters (umlauts: ü, ä, ö, ß, é) are properly encoded (CP1252 input → UTF-8 GeoJSON output). 6. 60 features, matching the input.
0:54