FeatureServer Integration

Converting ArcGIS FeatureServer services to STAC collections

ArcGIS FeatureServer services provide access to vector datasets including points, lines, and polygons. This page covers FeatureServer-specific configuration and examples.

Note

Prerequisites: Before using FeatureServer integration, complete the ArcGIS Server Integration setup including pyarc2stac installation.

FeatureServer Resources

Tip

For general ArcGIS Server integration support, troubleshooting, and additional resources, see the ArcGIS Server Integration page.

FeatureServer Specialization

FeatureServer integration is specifically designed for:

  • Vector-Based Datasets: Point, line, and polygon features with rich attribute data
  • Timeless Collections: Often static or infrequently updated boundary and reference datasets
  • Attribute-Rich Data: Features with extensive metadata and descriptive attributes requiring preservation

Example: Climate Projections FeatureServer

This example demonstrates FeatureServer integration using a climate and coastal inundation projections service:

# Climate projections FeatureServer with multiple feature layers
feature_server_url = "https://services3.arcgis.com/0Fs3HcaFfvzXvm7w/ArcGIS/rest/services/Climate_Mapping_Resilience_and_Adaptation_(CMRA)_Climate_and_Coastal_Inundation_Projections/FeatureServer"

# FeatureServer-specific inspection
collection = reader.generate_stac()

# Check feature layers and their schemas (FeatureServer specialty)
feature_links = [link for link in collection.links if link.rel == "featureserver"]
if feature_links:
    feature_link = feature_links[0]
    layers = feature_link.extra_fields.get('featureserver:layers', {})
    print(f"Available feature layers: {list(layers.keys())}")
    
    # Inspect layer schemas
    for layer_name, layer_info in layers.items():
        if 'fields' in layer_info:
            field_names = [f['name'] for f in layer_info['fields']]
            print(f"  {layer_name} fields: {field_names[:5]}...")  # Show first 5 fields

# Check timeless configuration (common for FeatureServer)
is_timeless = collection.extra_fields.get('dashboard:is_timeless', False)
print(f"Timeless dataset: {is_timeless}")

# Check geometry types
for layer_name, layer_info in layers.items():
    geom_type = layer_info.get('geometryType', 'Unknown')
    print(f"  {layer_name}: {geom_type}")

Configuration Structure

Complete Example Configuration

Based on the Climate Mapping Resilience and Adaptation projections FeatureServer:

{
  "type": "Collection",
  "id": "climate_mapping_resilience_and_adaptation_(cmra)_climate_and_coastal_inundation_projections",
  "stac_version": "1.1.0",
  "description": "Climate mapping resilience and adaptation climate and coastal inundation projections for vulnerability assessment and planning.",
  "links": [
    {
      "rel": "featureserver",
      "href": "https://services3.arcgis.com/0Fs3HcaFfvzXvm7w/ArcGIS/rest/services/Climate_Mapping_Resilience_and_Adaptation_(CMRA)_Climate_and_Coastal_Inundation_Projections/FeatureServer",
      "type": "application/json",
      "title": "ArcGIS FeatureServer",
      "featureserver:layers": {
        "0": "Counties",
        "1": "Tracts", 
        "2": "American Indian/Alaska Native/Native Hawaiian Areas"
      }
    },
    {
      "rel": "via",
      "href": "https://services3.arcgis.com/0Fs3HcaFfvzXvm7w/ArcGIS/rest/services/Climate_Mapping_Resilience_and_Adaptation_(CMRA)_Climate_and_Coastal_Inundation_Projections/FeatureServer",
      "type": "text/html", 
      "title": "Parent ArcGIS server url"
    }
  ],
  "dashboard:is_timeless": true,
  "extent": {
    "spatial": {
      "bbox": [[-179.23110125118885, -14.601806016382575, 179.8596657289939, 71.43978570694523]]
    },
    "temporal": {
      "interval": [[null, null]]
    }
  },
  "license": "not-applicable",
  "item_assets": {}
}

Key Configuration Elements

Timeless Data Indicator

Most FeatureServer data is timeless (static features):

{
  "dashboard:is_timeless": true,
  "extent": {
    "temporal": {
      "interval": [[null, null]]
    }
  }
}

Layer Information

Maps layer IDs to descriptive names:

{
  "featureserver:layers": {
    "0": "Primary Features",
    "1": "Reference Data",
    "2": "Administrative Boundaries"
  }
}

FeatureServer-Specific Best Practices

Attribute and Schema Validation

  1. Field Schema Verification: Ensure attribute schemas are complete

    # Check for required fields in feature layers
for layer in service_info.get('layers', []):
    layer_info = requests.get(f"{feature_server_url}/{layer['id']}?f=pjson").json()
    fields = layer_info.get('fields', [])

    # Check for essential fields
    field_names = [f['name'] for f in fields]
    if 'OBJECTID' not in field_names:
        print(f"⚠️  Layer {layer['name']} missing OBJECTID field")

  2. Geometry Type Validation: Verify feature geometry consistency

    # Ensure geometry types are properly defined
for layer in layers:
    geom_type = layer.get('geometryType')
    if not geom_type:
        print(f"⚠️  Layer {layer['name']} missing geometry type")
    elif geom_type not in ['esriGeometryPoint', 'esriGeometryPolyline', 'esriGeometryPolygon']:
        print(f"⚠️  Layer {layer['name']} has unexpected geometry type: {geom_type}")

    ```

Support

For FeatureServer integration assistance:

  1. Review the complete working example
  2. Check the pyarc2stac test cases
  3. Open an issue in the pyarc2stac repository