SpatioTemporal Asset Catalog Validator

⚠️ IMPORTANT NOTICE: PyPI Package Renamed ⚠️

Due to an administrative transition, the PyPI package name for this project has changed. The stac-validator package on PyPI is no longer maintained.

To get the latest updates, bug fixes, and features (v4.2.0 and beyond), you must now install stac-valid.

🛠️ What you need to do

You only need to update your installation command or requirements.txt:
# OLD
pip install stac-validator

# NEW
pip install stac-valid
Your existing Python code and CLI scripts DO NOT need to change. > You will still use import stac_validator and the stac-validator CLI command exactly as you did before.

Overview

STAC Validator is a tool to validate STAC (SpatioTemporal Asset Catalog) json files against the official STAC specification. It provides both a command-line interface and a Python API for validating STAC objects.

Documentation

For detailed documentation, please visit our GitHub Pages documentation site.

Validate STAC json files against the STAC spec.

$ stac-validator https://raw.githubusercontent.com/radiantearth/stac-spec/master/examples/extended-item.json

[
    {
        "version": "1.0.0",
        "path": "https://raw.githubusercontent.com/radiantearth/stac-spec/master/examples/extended-item.json",
        "schema": [
            "https://stac-extensions.github.io/eo/v1.0.0/schema.json",
            "https://stac-extensions.github.io/projection/v1.0.0/schema.json",
            "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
            "https://stac-extensions.github.io/view/v1.0.0/schema.json",
            "https://stac-extensions.github.io/remote-data/v1.0.0/schema.json",
            "https://schemas.stacspec.org/v1.0.0/item-spec/json-schema/item.json"
        ],
        "valid_stac": true,
        "asset_type": "ITEM",
        "validation_method": "default"
    }
]

Requirements

Python 3.8+
- Requests
- Click
- Jsonschema

Related Projects

Note: Stac-validator is also used in stac-check which adds linting messages based on the official STAC best practices document.
https://github.com/stac-utils/stac-check

Install

Installation from PyPi

$ pip install stac-valid

Installation from Repo

$ pip install .

or for local development

$ pip install -e '.[dev]'

The Makefile has convenience commands if Make is installed.

$ make help

Versions supported

STAC
0.8.0
0.8.1
0.9.0
1.0.0-beta.1
1.0.0-beta.2
1.0.0-rc.1
1.0.0-rc.2
1.0.0-rc.3
1.0.0-rc.4
1.0.0
1.1.0-beta.1
1.1.0

Usage

CLI

Basic Usage

$ stac-validator --help

Usage: stac-validator [OPTIONS] COMMAND [ARGS]...

  STAC Validator - Validate STAC files against the STAC specification.

  Usage:
    stac-validator validate <file> [options]
    stac-validator batch <files> [options]
    stac-validator batch <file> --item-collection [options]
      

Options:
  --help  Show this message and exit.

Commands:
  batch     Validate multiple STAC files concurrently using all available...
  validate  Main function for the `stac-validator` command line tool.

Validate Command

$ stac-validator validate --help

Usage: stac-validator validate [OPTIONS] STAC_FILE

  Validate a STAC file against the STAC specification.

  Prints validation results to the console as JSON. Exits with status code 0
  if valid, 1 if invalid.

Options:
  --core                          Validate core stac object only without
                                  extensions.
  --extensions                    Validate extensions only.
  --links                         Additionally validate links. Only works with
                                  default mode.
  --assets                        Additionally validate assets. Only works
                                  with default mode.
  -c, --custom TEXT               Validate against a custom schema (local
                                  filepath or remote schema).
  -sc, --schema-config TEXT       Validate against a custom schema config
                                  (local filepath or remote schema config).
  -s, --schema-map <TEXT TEXT>...
                                  Schema path to replaced by (local) schema
                                  path during validation. Can be used multiple
                                  times.
  -r, --recursive                 Recursively validate all related stac
                                  objects.
  -m, --max-depth INTEGER         Maximum depth to traverse when recursing.
                                  Omit this argument to get full recursion.
                                  Ignored if `recursive == False`.
  --collections                   Validate /collections response.
  --item-collection               Validate item collection response. Can be
                                  combined with --pages. Defaults to one page.
  --no-assets-urls                Disables the opening of href links when
                                  validating assets (enabled by default).
  --header <TEXT TEXT>...         HTTP header to include in the requests. Can
                                  be used multiple times.
  -p, --pages INTEGER             Maximum number of pages to validate via
                                  --item-collection. Defaults to one page.
  -t, --trace-recursion           Enables verbose output for recursive mode.
  --no_output                     Do not print output to console.
  --log_file TEXT                 Save full recursive output to log file
                                  (local filepath).
  --pydantic                      Validate using stac-pydantic models for
                                  enhanced type checking and validation.
  --verbose                       Enable verbose output. This will output
                                  additional information during validation.
  --schema-cache-size INTEGER     Max number of schema entries to cache in
                                  memory. Use 0 to disable schema caching.
                                  Defaults to 16.
  --help                          Show this message and exit.

Batch Command

$ stac-validator batch --help

Usage: stac-validator batch [OPTIONS] FILES...

  Validate multiple STAC files concurrently using all available CPU cores.

  This command uses multiprocessing to validate STAC files in parallel,
  bypassing Python's Global Interpreter Lock (GIL) for maximum performance.
  Each CPU core gets its own schema cache, which is warmed up on the first
  file and reused for subsequent files.

  Examples:

      # Validate all JSON files in a directory
      $ stac-validator batch *.json

      # Validate specific files
      $ stac-validator batch file1.json file2.json file3.json

      # Validate a GeoJSON FeatureCollection (validates each feature individually)
      $ stac-validator batch --item-collection sample_data/sentinel-cogs_0_100.json

      # Use only 4 cores
      $ stac-validator batch *.json --cores 4

      # Disable progress bar
      $ stac-validator batch *.json --no-progress

Options:
  --cores INTEGER       Number of CPU cores to use for parallel validation.
                        Defaults to all available cores.
  --no-progress         Disable progress bar during validation.
  --no-output           Do not print output to console.
  --item-collection  Treat files as GeoJSON FeatureCollections and validate
                        each feature individually.
  --verbose             Show full JSON output for all items. By default, only
                        invalid items are shown.
  --schema-cache-size INTEGER  Max number of schema entries to cache
                             per worker process. Use 0 to disable
                             schema caching. Defaults to 16.
  --batch-size INTEGER         Batch size for chunked processing. Larger
                               batches use more memory but may be faster.
                               Defaults to 2000.
  --help                Show this message and exit.

Fast Command

$ stac-valid fast --help

Usage: stac-valid fast [OPTIONS] STAC_FILE

  High-speed validation using fastjsonschema and local caching.

Options:
  -q, --quiet    Suppress individual item logs.
  -v, --verbose  Show full validation logs for all items. By default, only
                 invalid items are shown.
  --help         Show this message and exit.

Legacy Validation

The validate command is the main legacy validation tool with comprehensive options:

# Basic single file validation
$ stac-validator validate path/to/stac_file.json

# Validate with custom schema
$ stac-validator validate item.json --custom /path/to/schema.json

# Recursively validate all related STAC objects
$ stac-validator validate catalog.json --recursive --max-depth 5

# Validate collections endpoint response
$ stac-validator validate https://example.com/collections --collections

# Validate item collection response
$ stac-validator validate https://example.com/search --item-collection --pages 10

# Validate with extensions and links
$ stac-validator validate item.json --extensions --links --assets

Options include:

--core - Validate core STAC only (skip extensions)
--extensions - Validate extensions only
--links - Validate link objects
--assets - Validate asset objects
--recursive - Recursively validate related STAC objects
--custom - Validate against custom schema
--schema-map - Replace schema URLs during validation
--collections - Validate /collections endpoint response
--item-collection - Validate item collection responses
--pydantic - Use Pydantic models for validation
--schema-cache-size - Configure schema cache size
--batch-size - Configure batch size for chunked processing (batch command only)
And more (see stac-validator validate --help)

Batch Validation

The batch command validates multiple STAC files concurrently using multiprocessing to bypass Python's Global Interpreter Lock (GIL). This enables 10-100x performance improvement over single-threaded validation by utilizing all available CPU cores.

Architecture:

Multiprocessing: Each CPU core runs an independent Python process
Per-worker schema cache: Each worker maintains its own LRU cache of downloaded schemas (default 16 per worker)
Cache warmup: First file on each worker downloads schemas, subsequent files use cached copies
Configurable cache: Use --schema-cache-size to adjust cache size per worker (0 = disabled)
Linear scaling: Performance scales linearly with available cores (e.g., 8 cores = ~8x faster)
Container-aware: Automatically detects Docker/ECS CPU limits via os.sched_getaffinity()

Basic Usage

# Validate all JSON files in current directory
$ stac-validator batch *.json

# Validate specific files
$ stac-validator batch item1.json item2.json item3.json

# Validate a FeatureCollection (extracts and validates each feature)
$ stac-validator batch collection.json --item-collection

Options

# Use specific number of cores
$ stac-validator batch *.json --cores 4

# Reserve cores for OS (useful on local machines)
$ stac-validator batch *.json --cores -1  # Uses all cores minus 1

# Disable progress bar (for CI/CD environments)
$ stac-validator batch *.json --no-progress

# Suppress JSON output (show only summary)
$ stac-validator batch *.json --no-output

# Configure schema cache size per worker (default: 16 schemas per worker)
$ stac-validator batch *.json --schema-cache-size 32

# Disable schema caching entirely
$ stac-validator batch *.json --schema-cache-size 0

# Configure batch size for chunked processing (default: 2000 items per chunk)
$ stac-validator batch *.json --batch-size 5000

# Use larger batch size for faster processing (uses more memory)
$ stac-validator batch collection.json --item-collection --batch-size 10000

# Use smaller batch size for memory-constrained environments
$ stac-validator batch *.json --batch-size 500

Batch Size Configuration

The --batch-size option controls how many items are processed in each chunk. This affects memory usage and performance:

Default (2000): Balanced memory usage and performance for most systems
Larger values (5000-10000): Faster processing on systems with abundant memory; reduces overhead from creating multiple worker pools
Smaller values (500-1000): Lower memory footprint; useful on memory-constrained systems or when validating very large items

How It Works

Startup: Detects available CPU cores (respects Docker limits)
Distribution: Distributes files across worker processes
Validation: Each worker validates files independently
Schema Caching: Each worker maintains its own LRU cache (default 16 schemas per worker)
- First file on a worker: schemas are fetched and cached
- Subsequent files: schemas are reused from cache (no network/disk I/O)
- Total memory: up to cores × schema_cache_size schemas in memory
Results: Aggregates results and displays summary statistics

Example Output

$ stac-validator batch --item-collection sample_data/sentinel-cogs_0_100.json
[
    {
        "path": "sample_data/sentinel-cogs_0_100.json[0]",
        "valid_stac": false,
        "errors": [
            "'eo:bands' does not match any of the regexes: '^(?!eo:)'. Error is in properties "
        ]
    }
]

Validation Summary:
  Total files: 100
  CPU cores used: 16
  ✅ Valid: 99
  ❌ Invalid: 1

Failed validations:
  sample_data/sentinel-cogs_0_100.json[0]
    - 'eo:bands' does not match any of the regexes: '^(?!eo:)'. Error is in properties 

Validation completed in 1.10s

Performance Characteristics

Scenario	Single-threaded	Batch (8 cores)	Speedup
10000 items	~130 seconds	~22 seconds	5.9x

Times vary based on schema complexity and network latency for first download

Python API

from stac_validator.batch_validator import validate_concurrently

# Validate files
results = validate_concurrently(
    ["item1.json", "item2.json", "item3.json"],
    max_workers=None,  # Auto-detect cores
    show_progress=True
)

# Validate FeatureCollections
results = validate_concurrently(
    ["collection.json"],
    feature_collection=True,
    max_workers=8
)

# Process results
for result in results:
    if result["valid_stac"]:
        print(f"✅ {result['path']}")
    else:
        print(f"❌ {result['path']}")
        for error in result.get("errors", []):
            print(f"   - {error}")

Use Cases

Bulk ingestion: Validate thousands of STAC items from ESA, Copernicus, or other catalogs
CI/CD pipelines: Validate entire dataset collections in GitHub Actions or AWS CodePipeline
Data quality checks: Periodically validate all items in a STAC catalog
Migration validation: Verify all items when upgrading STAC versions
API preprocessing: Validate incoming FeatureCollections before storage (see FastAPI integration)

Fast Validation

The fast command provides ultra-high-speed validation using fastjsonschema with intelligent caching. It's optimized for large FeatureCollections, delivering batch-like performance with significantly lower memory overhead by using a single-threaded, compiled validator approach instead of multiprocessing.

Key Features:

fastjsonschema: Compiled validators for 10-100x faster validation than standard jsonschema
Single-threaded: No multiprocessing overhead - ideal for memory-constrained environments
Batch-comparable speed: Similar throughput to batch command but with minimal memory footprint
Multi-tier caching: RAM → Disk → Network with automatic fallback
Local schema storage: Schemas cached locally under local_schemas/.schemas directory for instant reuse
Automatic detection: Detects STAC type (Item, Collection, Catalog, FeatureCollection) automatically
Detailed metrics: Shows setup time, execution time, and cache hit status for each item
Error grouping: Groups validation errors by type and shows affected items

Basic Usage

# Validate a single STAC Item
$ stac-validator fast item.json

# Validate a FeatureCollection (validates each feature)
$ stac-validator fast collection.json

# Validate a STAC Collection
$ stac-validator fast collection-metadata.json

# Validate a STAC Catalog
$ stac-validator fast catalog.json

Options

# Suppress output (only show summary)
$ stac-validator fast item.json --quiet

# Show detailed output for all items (default shows first 5)
$ stac-validator fast collection.json --verbose

# Combine options
$ stac-validator fast collection.json --verbose --quiet  # Quiet takes precedence

Example Output

$ stac-validator fast sample_data/sentinel-cogs_0_100.json

📂 Loading: sample_data/sentinel-cogs_0_100.json
📦 Detected FeatureCollection (100 Items)

[1] ID: S2B_1CDH_20191211_0_L2A | Type: Item | Cache 🐌 | Setup: 125.34ms | Exec:  2.45ms | ✅ VALID
[2] ID: S2B_1CDH_20191220_0_L2A | Type: Item | Cache ⚡ | Setup:   0.12ms | Exec:  1.89ms | ✅ VALID
[3] ID: S2B_1CDH_20191230_0_L2A | Type: Item | Cache ⚡ | Setup:   0.08ms | Exec:  2.12ms | ❌ INVALID
[4] ID: S2B_1CCV_20191029_0_L2A | Type: Item | Cache ⚡ | Setup:   0.09ms | Exec:  1.95ms | ✅ VALID
[5] ID: S2B_1CCV_20191128_0_L2A | Type: Item | Cache ⚡ | Setup:   0.07ms | Exec:  2.03ms | ✅ VALID
... silencing output for remaining items (validating at maximum speed) ...

=======================================================
📊 VALIDATION SUMMARY
=======================================================
Total Objects Processed : 100
Valid Objects           : 46
Invalid Objects         : 54
-------------------------------------------------------
Total Setup Time        : 144.35 ms
Total Execution Time    : 25.74 ms
Average Exec per Object : 0.257 ms
=======================================================
🚨 ERROR BREAKDOWN
=======================================================

❌ STAC Spec Violation: Missing {'rel': 'collection'} in links array.
   Affected Items: 54
   Examples:       S2B_1CCV_20190102_0_L2A, S2B_1CCV_20190122_0_L2A, S2B_1CCV_20191029_0_L2A ... (and 51 more)

Cache Indicators

🐌 (Slow): First validation - schemas being fetched and compiled
⚡ (Lightning): Cached validator - instant execution using pre-compiled validator

Schema Caching & Performance Improvement

The fast validator uses a multi-tier caching strategy that dramatically speeds up subsequent runs:

First Run: Schemas are downloaded from the network and compiled with fastjsonschema
- Setup time: 5-6 seconds (includes network fetch for all schemas + compilation)
- Per-item execution: 0.25ms
- Total for 100 items: ~5.4 seconds
- Total for 1000 items: ~10-11 seconds
Subsequent Runs: Schemas are loaded from local disk cache (.schemas/ directory)
- Setup time: 100-200ms (instant disk read + compilation from cache)
- Per-item execution: 0.25ms
- Total for 100 items: ~150-200ms
- Total for 1000 items: ~400-500ms (0.4-0.5 seconds)
- Speedup: 20-25x faster compared to first run

Cache Storage:

Schemas are automatically cached in local_schemas/.schemas/ directory:

local_schemas/
└── .schemas/

This means:

✅ No network calls on subsequent validations
✅ Instant schema availability across multiple runs
✅ Persistent cache survives process restarts
✅ Minimal disk space usage (schemas are typically <100KB each)

Performance Characteristics

The fast validator is optimized for:

Large FeatureCollections (100-100,000+ items)
Memory-constrained environments (single-threaded, no multiprocessing)
Rapid iteration during development
CI/CD pipelines where speed and memory efficiency are critical

Typical performance (subsequent runs with disk cache):

Setup time: 100-200ms (disk read + compilation from cached schemas)
Per-item execution: 0.23-0.25ms (compiled fastjsonschema validator)
Total time for 100 items: ~150-200ms
Total time for 1000 items: ~400-500ms (0.4-0.5 seconds)
Memory footprint: Single process, minimal overhead (vs. batch's multiprocessing overhead)

Batch vs. Fast Comparison

Metric	Batch	Fast
Memory	High (multiprocessing)	Low (single-threaded)
CPU cores	All available	Single core
Best for	Large systems with many cores	Memory-constrained or single-core systems
FeatureCollections	Excellent	Excellent
Large datasets	Excellent	Good

Python API

from stac_validator.fast_validator import FastValidator

# Validate a file
validator = FastValidator("item.json", quiet=False, verbose=False)
validator.run()

# Check if valid
if validator.valid:
    print("✅ Valid STAC")
else:
    print("❌ Invalid STAC")

Choosing the Right Command

Use Case	Command	Why
Single file validation	`validate`	Full feature set, detailed error messages, extensions support
Large FeatureCollections (memory-constrained)	`fast`	Batch-like speed, single-threaded, minimal memory overhead
Bulk validation (1000+ files, multi-core systems)	`batch`	Multiprocessing, utilizes all CPU cores, scales linearly
Quick validation with detailed metrics	`fast`	Ultra-fast with fastjsonschema, timing info per item, auto-detection
CI/CD pipelines (memory-limited)	`fast`	Fast, low memory, suitable for containers and serverless
CI/CD pipelines (resource-rich)	`batch`	Parallelization, maximum throughput, consistent output
Development/testing	`fast`	Instant feedback, detailed metrics, minimal overhead
Complex validation rules	`validate`	Full control over validation options, recursive validation

Python

Single File Validation

from stac_validator import stac_validator

# Remote source
stac = stac_validator.StacValidate("https://raw.githubusercontent.com/stac-utils/pystac/main/tests/data-files/examples/0.9.0/collection-spec/examples/landsat-collection.json")
stac.run()
print(stac.message)

# Local file
stac = stac_validator.StacValidate("tests/test_data/1beta1/sentinel2.json", extensions=True)
stac.run()
print(stac.message)

Dictionary Validation

from stac_validator import stac_validator

stac = stac_validator.StacValidate()
stac.validate_dict(item_dict)
print(stac.message)

Batch Validation - List of Dictionaries

For validating dictionaries directly without managing temp files, use validate_dicts():

from stac_validator.batch_validator import validate_dicts

items = [
    {"type": "Feature", "stac_version": "1.1.0", ...},
    {"type": "Feature", "stac_version": "1.1.0", ...},
    {"type": "Feature", "stac_version": "1.1.0", ...},
]

# Validate all items concurrently (temp files handled internally)
results = validate_dicts(items, max_workers=None, show_progress=True)

print(f"Total: {len(results)}")
print(f"Valid: {sum(1 for r in results if r['valid_stac'])}")
print(f"Invalid: {sum(1 for r in results if not r['valid_stac'])}")

# Process results
for result in results:
    if result["valid_stac"]:
        print(f"✅ Valid")
    else:
        print(f"❌ Invalid: {result.get('errors', [])}")

Parameters:

items - List of STAC item dictionaries
max_workers - CPU cores to use (None = auto-detect, positive int = specific cores, negative int = all minus N)
show_progress - Display progress bar (default: True)
chunk_size - Number of items to process at a time to bound disk and memory usage (default: 1000)

Batch Validation - FeatureCollection (Concurrent with Multiprocessing)

For FeatureCollection validation with multiprocessing, use validate_concurrently() with feature_collection=True:

from stac_validator.batch_validator import validate_concurrently

# Validate FeatureCollection files directly (10-100x faster for large collections)
results = validate_concurrently(
    ["collection1.json", "collection2.json"],
    feature_collection=True,  # Expand and validate each feature
    max_workers=None  # Auto-detect cores
)

print(f"Total features: {len(results)}")
print(f"Valid: {sum(1 for r in results if r['valid_stac'])}")
print(f"Invalid: {sum(1 for r in results if not r['valid_stac'])}")

# Process results
for result in results:
    if result["valid_stac"]:
        print(f"✅ Feature valid")
    else:
        print(f"❌ Feature invalid: {result.get('errors', [])}")

Batch Validation - Multiple Files

from stac_validator.batch_validator import validate_concurrently

files = [
    "item1.json",
    "item2.json",
    "item3.json",
]

# Validate files concurrently using all available CPU cores
results = validate_concurrently(
    files,
    max_workers=None,  # Auto-detect cores
    show_progress=True
)

# Process results
for result in results:
    if result["valid_stac"]:
        print(f"✅ {result['path']}")
    else:
        print(f"❌ {result['path']}: {result.get('errors', [])}")

Batch Validation - FeatureCollection Files

from stac_validator.batch_validator import validate_concurrently

files = ["collection1.json", "collection2.json"]

# Validate FeatureCollections by extracting and validating each feature
results = validate_concurrently(
    files,
    feature_collection=True,
    max_workers=8
)

# Results show feature index: "collection1.json[0]", "collection1.json[1]", etc.
for result in results:
    print(f"{result['path']}: {'✅' if result['valid_stac'] else '❌'}")

Item Collection Validation

from stac_validator import stac_validator

stac = stac_validator.StacValidate()
stac.validate_item_collection_dict(item_collection_dict)
print(stac.message)

Configure Schema Cache Size

from stac_validator import stac_validator
from stac_validator.utilities import set_schema_cache_size

# Set once at app startup (process-wide)
set_schema_cache_size(16)  # use 0 to disable caching

stac = stac_validator.StacValidate()
stac.validate_dict(dictionary)
print(stac.message)

Schema Cache Settings

Default schema cache size is 16 entries.
Use --schema-cache-size in the CLI or set_schema_cache_size(...) in Python to override it.
Use 0 to disable schema caching.

Use set_schema_cache_size once at application startup:

from stac_validator.utilities import set_schema_cache_size

# Examples:
set_schema_cache_size(16)  # small cache for low-memory deployments
set_schema_cache_size(64)  # moderate cache for long-running services
set_schema_cache_size(0)   # disable schema caching

Notes:

StacValidate() and validate_dict() do not accept a cache-size parameter.
Changing cache size at runtime replaces the cache instance and drops existing cached entries.
In multi-worker deployments, configure cache size in each worker process.

Performance Benchmarking

A benchmark script is included to compare the performance of batch validation vs legacy item-collection validation. This is useful for understanding the performance improvements of the multiprocessing batch validator.

Running the Benchmark

# Test with 10,000 items (default)
python benchmark_validation.py

# Test with custom number of items
python benchmark_validation.py --items 5000
python benchmark_validation.py --items 50000

# Run only batch validation (skip slow legacy validation)
python benchmark_validation.py --items 10000 --batch-only

# Run only legacy validation
python benchmark_validation.py --items 10000 --legacy-only

# View all options
python benchmark_validation.py --help

Example Output

======================================================================
BENCHMARK RESULTS
======================================================================
Items tested: 10000

Batch Validation (multiprocessing):
  Time: 22.43s
  ✅ Valid: 0
  ❌ Invalid: 10000

Legacy Validation (single-threaded):
  Time: 131.62s
  ✅ Valid: 0
  ❌ Invalid: 10000

======================================================================
Speedup: 5.9x faster with batch validation
Time saved: 109.19s

Interpreting Results

Speedup Factor: Shows how many times faster batch validation is compared to legacy validation
Time Saved: The absolute time difference in seconds
CPU Cores Used: Number of CPU cores utilized by batch validation (typically all available cores)

The batch validator's multiprocessing approach provides significant performance improvements, especially for large datasets. The speedup factor varies based on:

Number of CPU cores available
Size of the dataset
Complexity of validation (extensions, custom schemas, etc.)

Deployment

Docker

The validator can run using docker containers.

$ docker build -t stac-validator .
$ docker run stac-validator https://raw.githubusercontent.com/stac-extensions/projection/main/examples/item.json

[
    {
        "version": "1.0.0",
        "path": "https://raw.githubusercontent.com/stac-extensions/projection/main/examples/item.json",
        "schema": [
            "https://stac-extensions.github.io/projection/v1.0.0/schema.json",
            "https://schemas.stacspec.org/v1.0.0/item-spec/json-schema/item.json"
        ],
        "valid_stac": true,
        "asset_type": "ITEM",
        "validation_method": "default"
    }
]

AWS (CDK)

An example AWS CDK deployment is available in cdk-deployment

$ cd cdk-deployment
$ cdk diff

Testing

$ make test
# or
$ pytest -v

See the tests files for examples on different usages.

Additional Examples

--core

$ stac-validator https://raw.githubusercontent.com/radiantearth/stac-spec/master/examples/extended-item.json --core

[
    {
        "version": "1.0.0",
        "path": "https://raw.githubusercontent.com/radiantearth/stac-spec/master/examples/extended-item.json",
        "schema": [
            "https://schemas.stacspec.org/v1.0.0/item-spec/json-schema/item.json"
        ],
        "valid_stac": true,
        "asset_type": "ITEM",
        "validation_method": "core"
    }
]

--custom

$ stac-validator https://radarstac.s3.amazonaws.com/stac/catalog.json --custom https://cdn.staclint.com/v0.7.0/catalog.json

[
    {
        "version": "0.7.0",
        "path": "https://radarstac.s3.amazonaws.com/stac/catalog.json",
        "schema": [
            "https://cdn.staclint.com/v0.7.0/catalog.json"
        ],
        "asset_type": "CATALOG",
        "validation_method": "custom",
        "valid_stac": true
    }
]

--extensions

$ stac-validator https://raw.githubusercontent.com/radiantearth/stac-spec/master/examples/extended-item.json --extensions

[
    {
        "version": "1.0.0",
        "path": "https://raw.githubusercontent.com/radiantearth/stac-spec/master/examples/extended-item.json",
        "schema": [
            "https://stac-extensions.github.io/eo/v1.0.0/schema.json",
            "https://stac-extensions.github.io/projection/v1.0.0/schema.json",
            "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
            "https://stac-extensions.github.io/view/v1.0.0/schema.json",
            "https://stac-extensions.github.io/remote-data/v1.0.0/schema.json"
        ],
        "valid_stac": true,
        "asset_type": "ITEM",
        "validation_method": "extensions"
    }
]

--recursive

$ stac-validator https://spot-canada-ortho.s3.amazonaws.com/catalog.json --recursive --max-depth 1 --trace-recursion

[
    {
        "version": "0.8.1",
        "path": "https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot4_orthoimages/collection.json",
        "schema": "https://cdn.staclint.com/v0.8.1/collection.json",
        "asset_type": "COLLECTION",
        "validation_method": "recursive",
        "valid_stac": true
    },
    {
        "version": "0.8.1",
        "path": "https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/collection.json",
        "schema": "https://cdn.staclint.com/v0.8.1/collection.json",
        "asset_type": "COLLECTION",
        "validation_method": "recursive",
        "valid_stac": true
    },
    {
        "version": "0.8.1",
        "path": "https://spot-canada-ortho.s3.amazonaws.com/catalog.json",
        "schema": "https://cdn.staclint.com/v0.8.1/catalog.json",
        "asset_type": "CATALOG",
        "validation_method": "recursive",
        "valid_stac": true
    }
]

--item-collection

$ stac-validator https://earth-search.aws.element84.com/v0/collections/sentinel-s2-l2a/items --item-collection --pages 2

--header

$ stac-validator https://stac-catalog.eu/collections/sentinel-s2-l2a/items --header x-api-key $MY_API_KEY --header foo bar

--schema-map

Schema map allows stac-validator to replace a schema in a STAC json by a schema from another URL or local schema file. This is especially useful when developing a schema and testing validation against your local copy of the schema.

$ stac-validator https://raw.githubusercontent.com/radiantearth/stac-spec/v1.0.0/examples/extended-item.json --extensions --schema-map https://stac-extensions.github.io/projection/v1.0.0/schema.json "tests/test_data/schema/v1.0.0/projection.json"

[
    {
        "version": "1.0.0",
        "path": "https://raw.githubusercontent.com/radiantearth/stac-spec/v1.0.0/examples/extended-item.json",
        "schema": [
            "https://stac-extensions.github.io/eo/v1.0.0/schema.json",
            "tests/test_data/schema/v1.0.0/projection.json",
            "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
            "https://stac-extensions.github.io/view/v1.0.0/schema.json",
            "https://stac-extensions.github.io/remote-data/v1.0.0/schema.json"
        ],
        "valid_stac": true,
        "asset_type": "ITEM",
        "validation_method": "extensions"
    }
]

This option is also capable of replacing URLs to subschemas:

$ stac-validator tests/test_data/v100/extended-item-local.json --custom tests/test_data/schema/v1.0.0/item_with_unreachable_url.json --schema-map https://geojson-wrong-url.org/schema/Feature.json https://geojson.org/schema/Feature.json --schema-map https://geojson-wrong-url.org/schema/Geometry.json https://geojson.org/schema/Geometry.json

[
    {
        "version": "1.0.0",
        "path": "tests/test_data/v100/extended-item-local.json",
        "schema": [
            "tests/test_data/schema/v1.0.0/item_with_unreachable_url.json"
        ],
        "valid_stac": true,
        "asset_type": "ITEM",
        "validation_method": "custom"
    }
]

--schema-config

The --schema-config option allows you to specify a YAML or JSON configuration file that maps remote schema URLs to local file paths. This is useful when you need to validate against multiple local schemas and want to avoid using multiple --schema-map arguments.

Example schema config file (YAML):

schemas:
  "https://schemas.stacspec.org/v1.0.0/collection-spec/json-schema/collection.json": "local_schemas/v1.0.0/collection.json"
  "https://schemas.stacspec.org/v1.0.0/item-spec/json-schema/item.json": "local_schemas/v1.0.0/item.json"
  "https://stac-extensions.github.io/eo/v1.0.0/schema.json": "local_schemas/v1.0.0/eo.json"

Usage:

$ stac-validator https://raw.githubusercontent.com/radiantearth/stac-spec/v1.0.0/examples/extended-item.json --schema-config path/to/schema_config.yaml

The paths in the config file can be absolute or relative to the config file's location.

--pydantic

The --pydantic option provides enhanced validation using stac-pydantic models, which offer stronger type checking and more detailed error messages. To use this feature, you need to install the optional dependency:

$ pip install stac-valid[pydantic]

Then you can validate your STAC objects using Pydantic models:

$ stac-validator https://raw.githubusercontent.com/radiantearth/stac-spec/master/examples/extended-item.json --pydantic

[
    {
        "version": "1.0.0",
        "path": "https://raw.githubusercontent.com/radiantearth/stac-spec/master/examples/extended-item.json",
        "schema": [
            "stac-pydantic Item model"
        ],
        "valid_stac": true,
        "asset_type": "ITEM",
        "validation_method": "pydantic",
        "extension_schemas": [
            "https://stac-extensions.github.io/eo/v1.0.0/schema.json",
            "https://stac-extensions.github.io/projection/v1.0.0/schema.json",
            "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
            "https://stac-extensions.github.io/view/v1.0.0/schema.json",
            "https://stac-extensions.github.io/remote-data/v1.0.0/schema.json"
        ],
        "model_validation": "passed"
    }
]

Sponsors and Supporters

The following organizations have contributed time and/or funding to support the development of this project:

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the Apache License 2.0.

Name		Name	Last commit message	Last commit date
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.github		.github
assets		assets
cdk-deployment		cdk-deployment
docs		docs
local_schemas		local_schemas
sample_data		sample_data
stac_validator		stac_validator
tests		tests
tox		tox
.dockerignore		.dockerignore
.flake8		.flake8
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.readthedocs.yml		.readthedocs.yml
CHANGELOG.md		CHANGELOG.md
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
benchmark_validation.py		benchmark_validation.py
pyproject.toml		pyproject.toml
schema_config.yaml		schema_config.yaml
tox.ini		tox.ini

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SpatioTemporal Asset Catalog Validator

🛠️ What you need to do

Table of Contents

Overview

Documentation

Validate STAC json files against the STAC spec.

Requirements

Related Projects

Install

Installation from PyPi

Installation from Repo

Versions supported

Usage

CLI

Legacy Validation

Batch Validation

Fast Validation

Python

Schema Cache Settings

Performance Benchmarking

Running the Benchmark

Example Output

Interpreting Results

Deployment

Docker

AWS (CDK)

Testing

Additional Examples

--core

--custom

--extensions

--recursive

--item-collection

--header

--schema-map

--schema-config

--pydantic

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