2.2. Data Flow
The data flow architecture in memories-dev represents the heart of the system’s ability to transform raw Earth observation data into actionable intelligence. This documentation explains the entire data lifecycle, from initial acquisition to delivery of insights.
2.2.1. Core Data Flow Principles
The data flow in memories-dev is built on several key principles:
Asynchronous Processing: Non-blocking operations enable concurrent data handling
Parallel Execution: Multiple analyzers run simultaneously for maximum efficiency
Intelligent Caching: Tiered memory system optimizes for both speed and cost
Adaptive Routing: Data flows to appropriate processors based on content and context
Pipeline Architecture: Sequential and branching processing stages with clear interfaces
2.2.2. System-Level Data Flow
The following diagram illustrates the high-level data flow through the system:
This architecture enables data to flow efficiently while maintaining appropriate feedback loops between components.
2.2.3. Scientific Foundations of Data Flow
The data flow architecture in memories-dev is grounded in several scientific principles from distributed systems, information theory, and geospatial computing.
2.2.3.1. Information Flow Optimization
The system optimizes information flow using principles from information theory. The core equation governing information transfer is:
I(X;Y) = \sum_{y \in Y} \sum_{x \in X} p(x,y) \log \left( \frac{p(x,y)}{p(x)p(y)} \right) Where: - \(I(X;Y)\) is the mutual information between source X and destination Y - \(p(x,y)\) is the joint probability distribution - \(p(x)\) and \(p(y)\) are the marginal probability distributions This principle guides the design of data routing and filtering mechanisms to maximize information transfer while minimizing redundancy. Parallel Processing Efficiency - ————————– The efficiency of parallel processing in the data flow is modeled using Amdahl’s Law:
S(n) = \frac{1}{(1-p) + \frac{p}{n}} Where: - \(S(n)\) is the theoretical speedup - \(n\) is the number of processors - \(p\) is the proportion of the program that can be parallelized The system architecture is designed to maximize the parallelizable portion (p) of data processing tasks. Geospatial Data Transformation ————————— Geospatial data transformations follow rigorous mathematical principles. For coordinate transformations:
begin{bmatrix} x’ \ y’ \ 1 end{bmatrix} = begin{bmatrix} a & b & c \ d & e & f \ 0 & 0 & 1 end{bmatrix} begin{bmatrix} x \ y \ 1 end{bmatrix} Where the transformation matrix encodes rotation, scaling, and translation operations for accurate geospatial alignment. Detailed Component Data Flows =========================== Acquisition Layer ————– The data acquisition layer handles the ingestion of data from various sources: .. mermaid:
A3[Sensor Networks] --> A
A4[Environmental APIs] --> A
A --> B1[Data Validation]
A --> B2[Format Conversion]
A --> B3[Metadata Extraction]
B1 & B2 & B3 --> C[Validated Data]
style A1 fill:#1e40af,color:white
style A2 fill:#1e40af,color:white
style A3 fill:#1e40af,color:white
style A4 fill:#1e40af,color:white
style A fill:#1d4ed8,color:white
style B1 fill:#1d4ed8,color:white
style B2 fill:#1d4ed8,color:white
style B3 fill:#1d4ed8,color:white
style C fill:#1d4ed8,color:white
Key Operations:
API Communication: Handles authentication, rate limiting, and retries
Data Validation: Checks for completeness, accuracy, and format consistency
Format Conversion: Normalizes data formats across sources
Metadata Extraction: Extracts and indexes metadata for efficient retrieval
Implementation Details:
The acquisition layer is implemented through the DataManager class, which coordinates data acquisition from multiple sources:
from memories.data_acquisition import DataManager
# Initialize the data manager with a cache directory
data_manager = DataManager(cache_dir="data/cache")
# Acquire satellite data
async def get_satellite_data():
satellite_data = await data_manager.get_satellite_data(
bbox_coords=[-122.4194, 37.7749, -122.3844, 37.8049],
start_date="2020-01-01",
end_date="2020-12-31"
)
return satellite_data
# Acquire vector data
async def get_vector_data():
vector_data = await data_manager.get_vector_data(
bbox=[-122.4194, 37.7749, -122.3844, 37.8049],
layers=["buildings", "roads", "landuse"]
)
return vector_data
The DataManager class integrates with various data sources through specialized APIs:
# Initialize data sources
self.overture = OvertureAPI(data_dir=str(self.cache_dir))
self.planetary = PlanetaryCompute(cache_dir=str(self.cache_dir))
self.sentinel = SentinelAPI(data_dir=str(self.cache_dir))
self.landsat = LandsatAPI(cache_dir=str(self.cache_dir))
self.osm = OSMDataAPI(cache_dir=str(self.cache_dir))
Code Example:
from memories.data_acquisition import DataAcquisitionManager
from memories.data_acquisition.sources import SatelliteSource, VectorSource
# Initialize data sources
satellite_source = SatelliteSource(
provider="sentinel",
api_key=os.environ.get("SENTINEL_API_KEY")
)
vector_source = VectorSource(
provider="overture",
categories=["buildings", "roads", "landuse"]
)
# Initialize data acquisition manager
acquisition_manager = DataAcquisitionManager(
sources=[satellite_source, vector_source],
validation_level="strict",
cache_enabled=True
)
# Acquire data asynchronously
async def acquire_location_data(lat, lon, radius_km=5):
data = await acquisition_manager.acquire(
location={"lat": lat, "lon": lon},
radius_km=radius_km,
time_range={"start": "2020-01-01", "end": "2023-01-01"},
resolution="high"
)
return data
2.2.3.2. Processing Layer
The processing layer transforms raw data into structured formats suitable for analysis:
Key Operations:
Data Cleaning: Removes noise, handles missing values, and corrects errors
Feature Extraction: Identifies and extracts relevant features from raw data
Temporal Alignment: Aligns data from different time periods
Spatial Registration: Ensures spatial consistency across different data sources
Implementation Details:
The processing layer uses specialized processors for different data types:
# Initialize processors
self.image_processor = ImageProcessor()
self.vector_processor = VectorProcessor()
self.data_fusion = DataFusion()
These processors implement various algorithms for data cleaning, feature extraction, and alignment:
# Image processing example
def process_satellite_image(image_data, options):
# Apply atmospheric correction
corrected = atmospheric_correction(image_data, method=options.get('correction_method', 'dos1'))
# Calculate indices (e.g., NDVI, NDWI)
indices = calculate_indices(corrected, indices=options.get('indices', ['ndvi', 'ndwi']))
# Apply cloud masking
masked = apply_cloud_mask(corrected, method=options.get('cloud_mask_method', 'qa'))
# Perform spatial resampling if needed
if options.get('resample', False):
resampled = resample(masked, resolution=options.get('target_resolution'))
return resampled
return masked
Scientific Algorithms:
The processing layer implements several scientific algorithms, including:
Atmospheric Correction Models: - Dark Object Subtraction (DOS) - Second Simulation of the Satellite Signal in the Solar Spectrum (6S) - Quick Atmospheric Correction (QUAC)
Spectral Indices: - Normalized Difference Vegetation Index (NDVI) - Normalized Difference Water Index (NDWI) - Soil Adjusted Vegetation Index (SAVI) - Enhanced Vegetation Index (EVI)
Spatial Registration Techniques: - Feature-based registration using SIFT/SURF - Intensity-based registration using mutual information - Control point-based registration
Code Example:
from memories.processing import ProcessingManager
from memories.processing.processors import (
CleaningProcessor,
FeatureExtractionProcessor,
TemporalAlignmentProcessor,
SpatialRegistrationProcessor
)
# Initialize processors
processors = [
CleaningProcessor(fill_missing=True, remove_outliers=True),
FeatureExtractionProcessor(features=["ndvi", "urban_density", "elevation"]),
TemporalAlignmentProcessor(interval="monthly"),
SpatialRegistrationProcessor(output_crs="EPSG:4326")
]
# Initialize processing manager
processing_manager = ProcessingManager(
processors=processors,
parallel_execution=True,
max_workers=8
)
# Process data
async def process_data(raw_data):
processed_data = await processing_manager.process(raw_data)
return processed_data
2.2.3.3. Memory Layer
The memory layer stores and organizes data across tiers for optimal access and cost-efficiency:
Key Operations:
Tiered Storage: Manages data across hot, warm, cold, and glacier tiers
Dynamic Migration: Migrates data between tiers based on access patterns
Efficient Indexing: Maintains indices for fast retrieval across dimensions
Compression and Encryption: Optimizes storage and ensures security
Implementation Details:
The memory layer is implemented through the MemoryManager class, which coordinates operations across different memory tiers:
class MemoryManager:
"""Memory manager that handles different memory tiers:
- Hot Memory: GPU-accelerated memory for immediate processing
- Warm Memory: CPU and Redis for fast in-memory access
- Cold Memory: DuckDB for efficient on-device storage
- Glacier Memory: Parquet files for off-device compressed storage
- def __init__(
self, storage_path: Path, redis_url: str = “redis://localhost:6379”, redis_db: int = 0, hot_memory_size: int = 1000, warm_memory_size: int = 10000, cold_memory_size: int = 100000, glacier_memory_size: int = 1000000
- ):
# Initialize memory tiers self.hot = HotMemory(storage_path=storage_path / “hot”, max_size=hot_memory_size) self.warm = WarmMemory(redis_url=redis_url, redis_db=redis_db, max_size=warm_memory_size) self.cold = ColdMemory(storage_path=storage_path / “cold”, max_size=cold_memory_size) self.glacier = GlacierMemory(storage_path=storage_path / “glacier”, max_size=glacier_memory_size)
The memory system uses vector embeddings and similarity search for efficient data retrieval:
def _init_index(self):
"""Initialize FAISS index"""
index_file = self.index_path / "memory.index"
if index_file.exists():
self.index = faiss.read_index(str(index_file))
with open(self.index_path / "metadata.pkl", "rb") as f:
self.metadata = pickle.load(f)
else:
# Create new index
self.index = faiss.IndexFlatL2(512) # 512-dimensional embeddings
self.metadata = {}
Code Example:
from memories.memory import MemoryManager, Config
# Configure memory system
config = Config(
hot_memory_size=5, # GB
warm_memory_size=20, # GB
cold_memory_size=100, # GB
glacier_enabled=True,
compression_level="medium",
encryption_enabled=True
)
# Initialize memory manager
memory_manager = MemoryManager(config)
# Store data in memory
memory_key = memory_manager.store(
data=processed_data,
metadata={
"location": "San Francisco",
"time": "2022-01-01",
"source": "sentinel-2",
"processing_level": "L2A"
},
tags=["urban", "high-resolution", "cloud-free"]
)
# Retrieve data from memory
retrieved_data = memory_manager.retrieve(
query={
"location": "San Francisco",
"time_range": ["2021-01-01", "2022-12-31"],
"tags": ["urban"]
}
)
2.2.3.4. Analysis Layer
The analysis layer applies various analytical techniques to extract insights from the data:
Key Operations:
Statistical Analysis: Computes statistical measures and distributions
Spatial Analysis: Performs spatial operations like clustering and hotspot detection
Temporal Analysis: Analyzes time series data for trends and patterns
Machine Learning: Applies ML models for prediction and classification
Scientific Algorithms:
The analysis layer implements various scientific algorithms:
Spatial Statistics: - Moran’s I for spatial autocorrelation - Getis-Ord Gi* for hotspot detection - Ripley’s K function for point pattern analysis
Time Series Analysis: - Seasonal-Trend decomposition using LOESS (STL) - ARIMA and SARIMA models - Dynamic Time Warping (DTW) for sequence comparison
Machine Learning Techniques: - Random Forest for classification and regression - Gradient Boosting for feature importance - Convolutional Neural Networks for image analysis - Transformer models for sequence data
Implementation Example:
from memories.analysis import AnalysisManager
from memories.analysis.analyzers import (
StatisticalAnalyzer,
SpatialAnalyzer,
TemporalAnalyzer,
MLAnalyzer
)
# Initialize analyzers
analyzers = [
StatisticalAnalyzer(metrics=["mean", "variance", "distribution"]),
SpatialAnalyzer(operations=["clustering", "hotspot_detection"]),
TemporalAnalyzer(operations=["trend_analysis", "seasonality_detection"]),
MLAnalyzer(models=["random_forest", "gradient_boosting"])
]
# Initialize analysis manager
analysis_manager = AnalysisManager(
analyzers=analyzers,
parallel_execution=True,
max_workers=4
)
# Analyze data
async def analyze_data(processed_data):
analysis_results = await analysis_manager.analyze(processed_data)
return analysis_results
2.2.3.5. Model Integration Layer
The model integration layer incorporates AI models for advanced analysis:
Key Operations:
Model Selection: Chooses appropriate models based on data and task
Model Execution: Runs models on prepared data
Output Integration: Combines outputs from multiple models
Uncertainty Estimation: Quantifies uncertainty in model predictions
Implementation Example:
from memories.models import ModelManager
from memories.models.models import (
ComputerVisionModel,
NLPModel,
TimeSeriesModel,
MultiModalModel
)
# Initialize models
models = [
ComputerVisionModel(type="segmentation", backbone="resnet50"),
NLPModel(type="entity_extraction", model="bert-base"),
TimeSeriesModel(type="forecasting", model="prophet"),
MultiModalModel(type="fusion", architecture="transformer")
]
# Initialize model manager
model_manager = ModelManager(
models=models,
device="cuda" if torch.cuda.is_available() else "cpu",
batch_size=16
)
# Run models
async def run_models(analysis_results):
model_outputs = await model_manager.run(analysis_results)
return model_outputs
2.2.3.6. Application Layer
The application layer delivers insights to end-users through various interfaces:
Key Operations:
Visualization: Creates interactive visualizations of data and insights
Reporting: Generates automated reports and summaries
API Endpoints: Provides programmatic access to data and insights
Decision Support: Offers recommendations and decision support tools
Implementation Example:
from memories.applications import ApplicationManager
from memories.applications.components import (
Visualization,
Reporting,
APIEndpoint,
DecisionSupport
)
# Initialize application components
components = [
Visualization(types=["maps", "charts", "dashboards"]),
Reporting(formats=["pdf", "html", "json"]),
APIEndpoint(protocols=["rest", "graphql"]),
DecisionSupport(tools=["recommendation", "scenario_analysis"])
]
# Initialize application manager
app_manager = ApplicationManager(
components=components,
authentication_required=True,
logging_enabled=True
)
# Deliver insights
async def deliver_insights(model_outputs):
delivery_results = await app_manager.deliver(model_outputs)
return delivery_results
2.2.4. Data Flow Optimization
The memories-dev framework implements several optimization techniques to ensure efficient data flow:
2.2.4.1. Caching Strategy
The system uses a multi-level caching strategy to minimize redundant operations:
def cache_exists(self, cache_key: str) -> bool:
"""Check if data exists in cache."""
cache_path = self.cache_dir / f"{cache_key}.json"
return cache_path.exists()
def get_from_cache(self, cache_key: str) -> Optional[Dict]:
"""Get data from cache."""
cache_path = self.cache_dir / f"{cache_key}.json"
if cache_path.exists():
with open(cache_path, 'r') as f:
return json.load(f)
return None
def save_to_cache(self, cache_key: str, data: Dict) -> None:
"""Save data to cache."""
cache_path = self.cache_dir / f"{cache_key}.json"
with open(cache_path, 'w') as f:
json.dump(data, f)
2.2.4.2. Parallel Processing
The system leverages asynchronous and parallel processing for improved performance:
async def prepare_training_data(
self,
bbox: Union[Tuple[float, float, float, float], List[float], Polygon],
start_date: str,
end_date: str,
satellite_collections: List[str] = ["sentinel-2-l2a"],
vector_layers: List[str] = ["buildings", "roads", "landuse"],
cloud_cover: float = 20.0,
resolution: Optional[float] = None
) -> Dict[str, Any]:
"""Prepare training data by combining satellite and vector data."""
# Validate and convert bbox
bbox_coords = self._validate_bbox(bbox)
# Create tasks for parallel execution
satellite_task = asyncio.create_task(
self.get_satellite_data(
bbox_coords=bbox_coords,
start_date=start_date,
end_date=end_date
)
)
vector_task = asyncio.create_task(
self.get_vector_data(
bbox=bbox_coords,
layers=vector_layers
)
)
# Wait for both tasks to complete
satellite_data, vector_data = await asyncio.gather(satellite_task, vector_task)
# Process and combine the data
# ...
return combined_data
2.2.4.3. Data Compression
The system implements data compression techniques to reduce storage and transmission requirements:
def compress_data(self, data: Dict[str, Any], method: str = "lz4") -> bytes:
"""Compress data using the specified method."""
serialized = json.dumps(data).encode('utf-8')
if method == "lz4":
import lz4.frame
compressed = lz4.frame.compress(serialized)
elif method == "zstd":
import zstandard as zstd
cctx = zstd.ZstdCompressor(level=3)
compressed = cctx.compress(serialized)
elif method == "gzip":
import gzip
compressed = gzip.compress(serialized)
else:
raise ValueError(f"Unsupported compression method: {method}")
return compressed
2.2.4.4. Adaptive Data Routing
The system implements adaptive data routing to optimize processing paths:
def route_data(self, data: Dict[str, Any]) -> str:
"""Determine the optimal processing route for the data."""
# Check data type
if "raster" in data:
# Route raster data
if data.get("cloud_cover", 100) > 50:
return "cloud_processing"
elif data.get("resolution", 0) < 10:
return "high_resolution_processing"
else:
return "standard_raster_processing"
elif "vector" in data:
# Route vector data
if data.get("feature_count", 0) > 10000:
return "large_vector_processing"
else:
return "standard_vector_processing"
else:
# Default route
return "general_processing"
2.2.5. Monitoring and Metrics
The data flow system includes comprehensive monitoring capabilities:
class DataFlowMonitor:
"""Monitors data flow performance and health."""
def __init__(self):
self.metrics = {
"throughput": [],
"latency": [],
"error_rate": [],
"cache_hit_rate": [],
"memory_usage": []
}
self.start_time = time.time()
def record_metric(self, metric_name: str, value: float):
"""Record a metric value."""
if metric_name in self.metrics:
self.metrics[metric_name].append((time.time(), value))
def get_summary(self) -> Dict[str, Any]:
"""Get a summary of metrics."""
summary = {}
for metric_name, values in self.metrics.items():
if values:
times, measurements = zip(*values)
summary[metric_name] = {
"min": min(measurements),
"max": max(measurements),
"mean": sum(measurements) / len(measurements),
"latest": measurements[-1],
"count": len(measurements)
}
else:
summary[metric_name] = {
"min": None,
"max": None,
"mean": None,
"latest": None,
"count": 0
}
summary["uptime"] = time.time() - self.start_time
return summary
2.2.6. Conclusion
The data flow architecture in memories-dev provides a robust foundation for processing Earth observation data. By implementing asynchronous processing, parallel execution, intelligent caching, and adaptive routing, the system achieves high performance and scalability while maintaining flexibility for diverse data sources and applications.
For more information on specific components of the data flow, see the following sections:
‘data_acquisition’ - Details on acquiring data from various sources
‘data_processing’ - Information on data processing techniques
Memory System - Documentation on the memory system for data storage
‘analysis’ - Guide to analytical capabilities
‘models’ - Information on AI model integration