dd-l2-bangkok-multicrs-audit

Status: done
Score: 1.000
Duration: 0:54
Cost: 0.12¢
Model: deepseek/deepseek-v4-flash

Map (reference ← swipe → agent)

0:00
A contractor just shipped `bangkok_contractor_delivery.gpkg` and I need a defect cite-sheet before I sign off. Please walk every layer in the file and write one row per layer to `crs_audit.csv` with these columns: layer_name, declared_crs (formatted as `EPSG:NNNN`), geometry_type, feature_count, sample_x and sample_y for a sample coordinate taken from the layer as it sits on disk, and encoding_detected. For encoding_detected, look at the text attributes for anomalies and report either `latin1-mojibake` or `utf-8`.
0:00
System info
{
  "adapter": "openrouter",
  "model": "deepseek/deepseek-v4-flash",
  "base_url": "https://openrouter.ai/api/v1"
}
0:11
Let me start by exploring the file to understand its structure - what layers it contains, their schemas, etc.
0:11
Bash ls -la /work/bangkok_contractor_delivery.gpkg
-rw-r--r-- 1 agent agent 2703360 Jun 16 22:51 /work/bangkok_contractor_delivery.gpkg
0:15
Let me first explore the GeoPackage file to understand its structure - what layers it contains, their schemas, etc.
0:15
Bash cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import fiona

# List all layers
layers = fiona.listlayers('bangkok_contractor_delivery.gpkg')
print('Layers:', layers)
"
Layers: ['parcels', 'roads', 'markets']
0:24
Let me explore each layer to understand the structure.
0:24
Bash cd /work && python3 -c "
cd /work && python3 -c "
import fiona
layers = fiona.listlayers('bangkok_contractor_delivery.gpkg')
for lyr in layers:
    with fiona.open('bangkok_contractor_delivery.gpkg', layer=lyr) as src:
        meta = src.meta
        schema = src.schema
        crs = src.crs
        count = len(src)
        # get first feature
        feat = next(iter(src))
        print(f'=== {lyr} ===')
        print(f'  CRS: {crs}')
        print(f'  Schema: {schema}')
        print(f'  Count: {count}')
        print(f'  First feature geometry: {feat[\"geometry\"]}')
        print(f'  First feature props: {feat[\"properties\"]}')
        print()
"
=== parcels ===
  CRS: EPSG:24047
  Schema: {'properties': {'id': 'str', 'name_th': 'str', 'owner_class': 'str'}, 'geometry': 'Polygon'}
  Count: 4000
  First feature geometry: fiona.Geometry(coordinates=[[(657421.5158199039, 1509162.3701859994), ...]], type='Polygon')
  First feature props: fiona.Properties(id='P00000', name_th='à¹\x81à¸\x9bลà¸\x87à¸\x97ีà¹\x88à¸\x94ิà¸\x99 à¸\x9aาà¸\x87รัà¸\x81', owner_class='private')

=== roads ===
  CRS: EPSG:32647
  Schema: {'properties': {'id': 'str', 'name': 'str', 'highway': 'str'}, 'geometry': 'LineString'}
  Count: 5000
  First feature geometry: fiona.Geometry(coordinates=[(656835.6850029442, 1509487.0031804747), ...], type='LineString')
  First feature props: fiona.Properties(id='R00000', name='à¸\x96à¸\x99à¸\x99สุà¸\x82ุมวิà¸\x97', highway='primary')

=== markets ===
  CRS: EPSG:4326
  Schema: {'properties': {'id': 'str', 'name_th': 'str', 'kind': 'str'}, 'geometry': 'Point'}
  Count: 1000
  First feature geometry: fiona.Geometry(coordinates=(100.4533, 13.655000000000001), type='Point')
  First feature props: fiona.Properties(id='M00000', name_th='ตลาดนัดจตุจักร', kind='fresh')
0:37
Interesting! Let's analyze:

1. **parcels**: CRS is EPSG:24047 (Indian 1954 / UTM zone 47N, I think). The name_th field shows `à¹\x81à¸\x9bลà¸\x87à¸\x97ีà¹\x88à¸\x94ิà¸\x99 à¸\x9aาà¸\x87รัà¸\x81` - this looks like UTF-8 bytes being interpreted as Latin-1 (mojibake). The actual Thai characters like "แผนการ" etc. encoded in UTF-8 and then displayed as Latin-1 would produce those sequences.

2. **roads**: CRS is EPSG:32647 (WGS 84 / UTM zone 47N). The name field shows `à¸\x96à¸\x99à¸\x99สุà¸\x82ุมวิà¸\x97` - also looks like mojibake.

3. **markets**: CRS is EPSG:4326 (WGS 84). The name_th field shows `ตลาดนัดจตุจักร` which looks like proper Thai/UTF-8 text.

So the encoding detection:
- parcels: latin1-mojibake (Thai text encoded as UTF-8 but stored/read as Latin-1)
- roads: latin1-mojibake
- markets: utf-8

Now I need to write a script to generate the CSV. Let me also get sample coordinates. I need the coordinate from the geometry as it sits on disk.

For sample_x and sample_y, I'll grab the first coordinate from the first feature of each layer.

Let me write the full script.
0:37
Bash cd /work && python3 -c "
cd /work && python3 -c "
import fiona

def get_sample_coord(layer):
    with fiona.open('bangkok_contractor_delivery.gpkg', layer=layer) as src:
        feat = next(iter(src))
        geom = feat['geometry']
        # Handle different geometry types to extract one coordinate
        if geom['type'] == 'Point':
            return geom['coordinates'][0], geom['coordinates'][1]
        elif geom['type'] in ('Polygon', 'LineString'):
            # first coord of first ring/segment
            coords = geom['coordinates']
            if geom['type'] == 'Polygon':
                coords = coords[0]  # exterior ring
            return coords[0][0], coords[0][1]
        elif geom['type'] == 'MultiPoint':
            return geom['coordinates'][0][0], geom['coordinates'][0][1]
    return None, None

def detect_encoding(layer):
    with fiona.open('bangkok_contractor_delivery.gpkg', layer=layer) as src:
        feat = next(iter(src))
        props = feat['properties']
        for k, v in props.items():
            if isinstance(v, str) and any(ord(c) > 127 for c in v):
                # Check if it's valid UTF-8 Thai or mojibake
                # Mojibake pattern: sequences like ๠etc (UTF-8 bytes read as Latin-1)
                # We can try to re-encode as Latin-1 and decode as UTF-8 to test
                try:
                    # If the string is actually UTF-8 bytes misinterpreted as Latin-1,
                    # we can round-trip: encode('latin-1') -> bytes, then decode('utf-8') -> proper Thai
                    recovered = v.encode('latin-1').decode('utf-8')
                    # Check if all chars are printable/category
                    import unicodedata
                    # If recovery yields consistent Thai characters, it's mojibake
                    # Check if recovered looks like proper Thai
                    thai_chars = sum(1 for c in recovered if '\u0E00' <= c <= '\u0E7F')
                    if thai_chars > 0:
                        return 'latin1-mojibake'
                    else:
                        return 'utf-8'
                except:
                    # If the round-trip fails, it might be legit UTF-8
                    return 'utf-8'
    return 'utf-8'

layers = fiona.listlayers('bangkok_contractor_delivery.gpkg')
for lyr in layers:
    print(f'Layer: {lyr}')
    x, y = get_sample_coord(lyr)
    print(f'  Sample coord: {x}, {y}')
    enc = detect_encoding(lyr)
    print(f'  Encoding: {enc}')
"
Layer: parcels
  Sample coord: 657421.5158199039, 1509162.3701859994
  Encoding: latin1-mojibake
Layer: roads
  Sample coord: 656835.6850029442, 1509487.0031804747
  Encoding: latin1-mojibake
Layer: markets
  Sample coord: 100.4533, 13.655000000000001
  Encoding: utf-8
0:45
Let me now get more details to build the CSV. I need:

1. layer_name
2. declared_crs as EPSG:NNNN
3. geometry_type
4. feature_count
5. sample_x and sample_y (for a sample coordinate taken from the layer as it sits on disk)
6. encoding_detected

Let me write a comprehensive script to produce the CSV.
0:45
Bash cd /work && python3 << 'PYEOF'
cd /work && python3 << 'PYEOF'
import csv
import fiona

layers = fiona.listlayers('bangkok_contractor_delivery.gpkg')

rows = []

for lyr in layers:
    with fiona.open('bangkok_contractor_delivery.gpkg', layer=lyr) as src:
        # CRS
        crs = src.crs
        if crs is None:
            declared_crs = 'EPSG:None'
        elif crs.get('init'):
            declared_crs = crs['init'].upper()
        else:
            # try to build from epsg code
            epsg = crs.to_epsg() if hasattr(crs, 'to_epsg') else None
            if epsg:
                declared_crs = f'EPSG:{epsg}'
            else:
                declared_crs = str(crs)
        
        # Geometry type
        geom_type = src.schema['geometry']
        
        # Feature count
        count = len(src)
        
        # Sample coordinate from first feature
        feat = next(iter(src))
        geom = feat['geometry']
        coords = geom['coordinates']
        
        if geom_type == 'Point':
            sample_x, sample_y = coords[0], coords[1]
        elif geom_type in ('MultiPoint',):
            sample_x, sample_y = coords[0][0], coords[0][1]
        elif geom_type == 'LineString':
            sample_x, sample_y = coords[0][0], coords[0][1]
        elif geom_type == 'MultiLineString':
            sample_x, sample_y = coords[0][0][0], coords[0][0][1]
        elif geom_type == 'Polygon':
            sample_x, sample_y = coords[0][0][0], coords[0][0][1]
        elif geom_type == 'MultiPolygon':
            sample_x, sample_y = coords[0][0][0][0], coords[0][0][0][1]
        else:
            sample_x, sample_y = None, None
        
        # Encoding detection
        props = feat['properties']
        encoding = 'utf-8'
        for k, v in props.items():
            if isinstance(v, str) and any(ord(c) > 127 for c in v):
                try:
                    recovered = v.encode('latin-1').decode('utf-8')
                    thai_chars = sum(1 for c in recovered if '\u0E00' <= c <= '\u0E7F')
                    if thai_chars > 0:
                        encoding = 'latin1-mojibake'
                        break
                except:
                    pass
        
        rows.append({
            'layer_name': lyr,
            'declared_crs': declared_crs,
            'geometry_type': geom_type,
            'feature_count': count,
            'sample_x': sample_x,
            'sample_y': sample_y,
            'encoding_detected': encoding
        })

with open('crs_audit.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['layer_name', 'declared_crs', 'geometry_type', 'feature_count', 'sample_x', 'sample_y', 'encoding_detected'])
    writer.writeheader()
    writer.writerows(rows)

print("Done. Contents:")
with open('crs_audit.csv') as f:
    print(f.read())
PYEOF
Done. Contents:
layer_name,declared_crs,geometry_type,feature_count,sample_x,sample_y,encoding_detected
parcels,EPSG:24047,Polygon,4000,657421.5158199039,1509162.3701859994,latin1-mojibake
roads,EPSG:32647,LineString,5000,656835.6850029442,1509487.0031804747,latin1-mojibake
markets,EPSG:4326,Point,1000,100.4533,13.655000000000001,utf-8
0:53
The task is complete. The CSV file has been written to `/work/crs_audit.csv` with all required columns and correct data for each layer.
0:53