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version: "1.0.0" name: imagery-provider-adaptation description: "Imagery provider adaptation skill for HASTE. Encapsulates provider-specific logic for satellite imagery sources (Planet, Vantor, Airbus, etc.). Use when: 'new imagery provider', 'add source type', 'satellite provider', 'Planet', 'Vantor', 'Airbus', 'Pleiades', 'WorldView', 'SkySat', 'imagery ingestion', 'provider adapter'." source: "HASTE imagery processing pipeline, satellite provider documentation" domain: "geospatial" level: "advanced" agents: ["gis", "backend-dev"] created_date: "2026-04-27" last_validated: "" validated_by: "" status: "draft"


Imagery Provider Adaptation

Overview

Structured process for adding new satellite imagery providers to HASTE. Each provider has different data formats, coordinate systems, APIs, band configurations, and delivery methods. This skill encapsulates the provider-specific logic needed to adapt a new source.

Key Concepts

Current Providers

ProviderSatellitesFormatBandsDelivery
VantorWorldView-2/3/4, GeoEye-1GeoTIFF4-8 bands (BGRN + extras)S3, STAC, Direct URL
PlanetPlanetScope, SkySatGeoTIFF, COG4 bands (BGRN)Planet API, S3
AirbusPleiades, Pleiades Neo, SPOTGeoTIFF, DIMAP4 bands (BGRN)OneAtlas, S3

Provider Adapter Components

Each new provider requires:

  1. Source Type Definition — Model configuration in hastegeo.core.models
  2. Download Handler — Provider-specific authentication and download logic
  3. Band Mapping — Map provider bands to HASTE's expected band order
  4. Metadata Parser — Extract spatial metadata from provider-specific formats
  5. Preprocessing Rules — Resolution normalization, radiometric correction
  6. Tests — Integration tests with sample provider data

Patterns & Techniques

Adding a New Provider: Step-by-Step

Step 1: Define source type Add to the source type configuration in hastegeo.core.models:

python
# New source type with provider-specific configuration
class NewProviderConfig(BaseModel):
provider_name: str
api_url: str
band_order: list[str] # e.g., ["B", "G", "R", "NIR"]
default_crs: str # e.g., "EPSG:4326"
tile_size: int # e.g., 256

Step 2: Implement download handler In hastegeo.core.processors.imagery:

python
# Handle provider-specific authentication and URL patterns
# Use requests with proper auth (API key, OAuth, etc.)
# Stream large files to avoid memory issues
# Validate downloaded file integrity

Step 3: Implement band mapping

python
# Map provider bands to HASTE standard order
# HASTE expects: [Blue, Green, Red, NIR] for 4-band
# Handle extra bands (e.g., coastal, red-edge, SWIR)
# Handle missing bands (e.g., panchromatic only)

Step 4: Implement preprocessing

python
# 1. Validate CRS — reproject if needed
# 2. Normalize resolution — resample to target GSD
# 3. Apply radiometric correction if needed
# 4. Generate COG with internal tiling and overviews
# 5. Validate output with rasterio

Step 5: Add to imagery processor Update ImageryPreProcessor to route to the new handler based on source type.

Step 6: Write tests

python
# Test with real sample data (small AOI, public data preferred)
# Verify CRS preservation
# Verify band order mapping
# Verify COG compliance
# Verify metadata extraction

Provider-Specific Gotchas

ProviderGotchaMitigation
VantorMultiple UTM zones in a single orderCheck CRS per file, reproject to consistent zone
PlanetUDM2 quality masks delivered separatelyDownload and apply quality mask before processing
AirbusDIMAP format metadataParse XML metadata alongside GeoTIFF
AllDifferent nodata conventionsStandardize nodata to 0 or NaN during preprocessing

Decision Framework

ScenarioApproach
Provider uses standard GeoTIFFMinimal adapter — mostly URL/auth handling
Provider uses proprietary formatFull adapter — format conversion + metadata extraction
Provider delivers via STACUse existing STAC client, add provider-specific auth
Provider requires API keyStore in Config, never hardcode
Provider delivers in tilesImplement tile stitching before COG generation

Quick Reference: COG Output Standard

Format: Cloud Optimized GeoTIFF
Tiling: 256x256 or 512x512 internal tiles
Overviews: Nearest power of 2, down to 256px
Compression: LZW or DEFLATE
CRS: Preserve source CRS (typically UTM or EPSG:4326)
Nodata: 0 for uint8/uint16, NaN for float
Bands: Blue, Green, Red, NIR (minimum)

Common Pitfalls

  • Assuming all providers use the same band order — Always check and map explicitly
  • Hardcoding provider URLs/keys — Use Config class
  • Ignoring quality masks — Bad pixels contaminate training data
  • Not testing with edge cases — Antimeridian crossing, polar regions, dateline
  • Downloading entire scenes when only a small AOI is needed — Use provider APIs to clip
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