ANVESANA : MARGAVEDHA

Project Overview

ANVESANA : MARGAVEDHA is an autonomous infrastructure auditing architecture designed to fuse multi-temporal Spaceborne Radar Interferometry (DInSAR) with Geotechnical AI for forecasting sub-surface pavement degradation and infrastructure instability.

The system is designed around the idea that critical infrastructure can be monitored continuously from space by combining:

• Multi-temporal Sentinel-1 SAR observations
• Sentinel-2 multispectral imagery
• Digital Elevation Models
• Meteorological information
• DInSAR-derived surface displacement
• Physics-informed machine learning
• Geotechnical state estimation
• Automated anomaly detection

Rather than relying exclusively on conventional ground inspection, MARGAVEDHA aims to create a computational infrastructure-auditing layer capable of identifying abnormal surface and sub-surface behavior through multi-modal Earth observation data.

Core Mission

The primary objective of ANVESANA : MARGAVEDHA is to develop an autonomous architecture capable of detecting, interpreting, and forecasting infrastructure degradation using remotely sensed observations and physics-informed geotechnical intelligence.

The system focuses particularly on:

• Sub-surface pavement degradation
• Ground deformation
• Subgrade instability
• Drainage-related structural changes
• Load-bearing capacity degradation
• Infrastructure anomaly detection

Core Concept

MARGAVEDHA combines two complementary forms of intelligence.

Spaceborne Observation

Multi-temporal Sentinel-1 SAR observations are processed through a DInSAR pipeline to derive surface displacement information.

This provides a temporal measurement of ground movement represented by a displacement vector:

Δz

Geotechnical Intelligence

The derived displacement information is combined with soil-state variables and physics-informed computational models to estimate structural and geotechnical behavior.

The architecture therefore attempts to connect:

Orbital Observation → Physical State → Geotechnical Interpretation → Infrastructure Audit

System Philosophy

The system is designed around a multi-modal data fusion architecture.

Instead of treating satellite imagery, terrain information, weather, and geotechnical variables as independent datasets, MARGAVEDHA attempts to construct a unified computational representation of the observed environment.

The architecture integrates:

• Spatial information
• Temporal information
• Physical constraints
• Satellite observations
• Terrain topology
• Geotechnical parameters
• State estimation

Major System Components

1. Interactive Ingestion Layer

A Streamlit-based interface allows the user to define an Area of Interest using an interactive Leaflet/Folium map.

The user can provide:

• Bounding Box
• Time range
• Processing parameters
• Output requirements

2. Distributed Data Ingestion

The ingestion architecture connects multiple Earth observation and environmental sources.

Primary sources include:

• Sentinel-1 SAR
• Sentinel-2 Optical
• Copernicus DEM
• STAC APIs
• ASF Search API
• Meteorological datasets

3. Multi-Temporal Radar Processing

The Sentinel-1 processing subsystem is designed around multi-temporal interferometric analysis.

The architecture uses:

• Sentinel-1 SLC data
• DInSAR
• MintPy
• ISCE2

The objective is to generate a temporal surface displacement representation capable of identifying abnormal ground movement.

4. Topological Terrain Intelligence

Copernicus DEM data is processed into structured topological information.

PyTorch Geometric is used as the computational framework for representing terrain information as graph structures.

The resulting topology can represent:

• Elevation gradients
• Directional drainage pathways
• Spatial relationships
• Terrain connectivity

5. Physics-Informed Computational Engine

The computational core is based on the THE MRIDANSH engine architecture.

The system uses a spatial-temporal learning framework combined with physical constraints.

Core components include:

• PyTorch
• ConvLSTM
• DeepXDE
• Physics-informed optimization
• Ensemble Kalman Filter

Physics Constraints

The architecture incorporates physical constraints into the computational process.

Darcy-Based Hydrological Constraint

Water movement through the soil profile is constrained using Darcy-based fluid dynamics.

The conceptual governing relationship is:

∇ · (K ∇h) = Ss ∂h/∂t

Mohr-Coulomb Geotechnical Constraint

The structural behavior of the soil/subgrade state is constrained using the Mohr-Coulomb failure criterion:

τ = c + σn tan(φ)

These constraints are intended to prevent the computational model from producing physically implausible states.

Unified Subsurface State

The computational engine produces a unified representation of the estimated subsurface condition.

Example state variables include:

• Volumetric Water Content (VWC)
• Bulk Density
• Shear Modulus
• Soil-state parameters
• Geotechnical indicators

This unified representation becomes the input to downstream diagnostic modules.

Dual-Domain Intelligence

MARGAVEDHA is designed with two primary diagnostic modes.

Mode A — Agronomic Intelligence

The agronomic pathway can derive:

• Root-zone moisture
• NPK-related estimates
• Precision management information
• Soil condition indicators

Mode B — Geotechnical Structural Intelligence

The geotechnical pathway focuses on:

• Subgrade compaction
• Plasticity
• Shear stability
• California Bearing Ratio (CBR)
• Structural degradation indicators

Spaceborne Forensic Analysis

One of the defining concepts of MARGAVEDHA is the comparison between:

Observed Ground Displacement

and

Predicted Geotechnical Degradation

The system cross-references the DInSAR-derived displacement vector with model-generated geotechnical indicators.

If abnormal behavior crosses a defined threshold, the system can flag the corresponding spatial coordinate for further investigation.

Autonomous Infrastructure Auditing

The final objective is to transform raw satellite observations into an automated infrastructure intelligence workflow.

Conceptually:

Satellite Observation → Multi-Temporal Processing → Surface Displacement → Terrain + Environmental Data → Physics-Informed State Estimation → Geotechnical Interpretation → Anomaly Detection → Infrastructure Audit

Output Layer

The planned output interface includes:

• Interactive infrastructure maps
• Time-series analysis
• Ground displacement visualization
• 3D subsurface visualization
• Geotechnical state indicators
• Infrastructure anomaly flags
• Automated audit reports

Storage

Temporal system states and computed indicators are designed to be stored using a local SQLite-based data layer.

This allows historical observations and model states to be retained for temporal comparison.

Project Relationship With THE MRIDANSH

MARGAVEDHA is designed as a downstream intelligence architecture that can consume the unified soil/subsurface state generated by THE MRIDANSH.

Conceptually:

THE MRIDANSH → Unified Soil / Subsurface State → ANVESANA : MARGAVEDHA → Geotechnical & Infrastructure Intelligence → Autonomous Infrastructure Auditing

This separation allows THE MRIDANSH to remain a reusable scientific state-estimation core while MARGAVEDHA operates as a specialized infrastructure intelligence layer.

Long-Term Vision

ANVESANA : MARGAVEDHA is envisioned as a foundation for autonomous infrastructure monitoring using Earth observation, radar interferometry, artificial intelligence, and geotechnical physics.

Potential future applications include:

• Pavement monitoring
• Infrastructure risk assessment
• Ground deformation monitoring
• Drainage-related degradation analysis
• Large-scale infrastructure surveillance
• Automated geotechnical reconnaissance

One-Line Description

ANVESANA : MARGAVEDHA is an autonomous infrastructure auditing architecture that fuses multi-temporal Spaceborne Radar Interferometry (DInSAR) with physics-informed geotechnical AI for sub-surface pavement degradation forecasting.

ANVESANA : MARGAVEDHA — System Architecture

Architecture Version

Architecture: v1.0
Project Status: Planned / Research & Architecture Phase

1. Architectural Overview

ANVESANA : MARGAVEDHA follows a modular end-to-end architecture connecting interactive user input, distributed Earth observation ingestion, multi-temporal radar processing, terrain graph construction, data assimilation, physics-informed state estimation, geotechnical interpretation, and automated infrastructure auditing.

The architecture is organized into six major computational stages:

USER INTERFACE → DATA INGESTION → MULTI-TEMPORAL RADAR PROCESSING → PHYSICS-INFORMED COMPUTATIONAL ENGINE → DUAL-DOMAIN DIAGNOSTICS → FORENSIC INFRASTRUCTURE AUDIT

2. End-to-End Architecture

+------------------------------------------------------------------------------+ | ANVESANA : MARGAVEDHA | | AUTONOMOUS INFRASTRUCTURE AUDITING SYSTEM | +------------------------------------------------------------------------------+ | v +------------------------------------------------------------------------------+ | 1. INTERACTIVE INGESTION LAYER | | | | Streamlit Interface → Interactive Leaflet/Folium Map → Area of Interest | +------------------------------------------------------------------------------+ | v +------------------------------------------------------------------------------+ | 2. DISTRIBUTED DATA INGESTION LAYER | | | | STAC API (Sentinel-2) | ASF Search API (Sentinel-1) | | Copernicus DEM + Environmental / Historical Inputs | +------------------------------------------------------------------------------+ | v +------------------------------------------------------------------------------+ | 3. MULTI-TEMPORAL PROCESSING BACKBONE | | | | Sentinel-1 SLC → Interferometry (DInSAR/MintPy/ISCE2) → Displacement (Δz) | | Copernicus DEM → PyTorch Geometric → Terrain / Drainage Graph | +------------------------------------------------------------------------------+ | v +------------------------------------------------------------------------------+ | 4. DATA ASSIMILATION & COALESCENCE | | | | Optical + SAR + DEM + Weather + DInSAR → 4D Spatio-Temporal Tensor | | Ensemble Kalman Filter (EnKF) State Update | +------------------------------------------------------------------------------+ | v +------------------------------------------------------------------------------+ | 5. COMPUTATIONAL ENGINE — THE MRIDANSH CORE | | | | Physics-Informed ConvLSTM + Graphs + EnKF Vectors | | Physics Constraints: Darcy Fluid Flux + Mohr-Coulomb Shear Criterion (DeepXDE)| +------------------------------------------------------------------------------+ | v +------------------------------------------------------------------------------+ | 6. UNIFIED SUBSURFACE STATE TENSOR | | | | VWC | Bulk Density | Shear Modulus Matrix | Physical State Variables | +------------------------------------------------------------------------------+ | +-----------------+-----------------+ | | v v +---------------------------------+ +----------------------------------------+ | 7A. AGRONOMIC VITALS | | 7B. GEOTECHNICAL STRUCTURAL MODE | | | | | | • Root-Zone Moisture | | • Plasticity | | • NPK-related Estimates | | • Subgrade Compaction | | • Precision Management | | • Shear Stability / CBR | +---------------------------------+ +----------------------------------------+ | | +-----------------+-----------------+ | v +------------------------------------------------------------------------------+ | 8. SPACEBORNE FORENSIC ANALYSIS | | | | DInSAR Displacement Vector (Δz) + Geotechnical State → Threshold Analysis | | → Infrastructure Anomaly Flag | +------------------------------------------------------------------------------+ | v +------------------------------------------------------------------------------+ | 9. DATA STORAGE & AUDIT LAYER | | | | SQLite Storage → Streamlit Dashboard → 3D Subsurface View & Audit Report | +------------------------------------------------------------------------------+

3. Step 1 — Interactive Ingestion Layer

The system begins with an interactive Streamlit interface. The user defines an Area of Interest using an interactive map.

Input Parameters

• Bounding Box
• Geographic coordinates
• Time range
• Processing configuration
• Output mode

Interface Stack

• Streamlit
• Leaflet / Folium

4. Step 2 — Distributed Data Ingestion

The ingestion layer connects the system with multiple Earth observation sources.

Optical Data

Sentinel-2 multispectral data is accessed through STAC-based discovery. The architecture uses cloud-cover information as part of the sensor routing logic:

• Cloud Cover < 20%: Sentinel-2 Optical Processing

Radar Data

Sentinel-1 SAR data is accessed through the ASF Search API. Radar provides an alternative observation pathway when optical observations are affected by cloud conditions:

• Cloud Cover ≥ 20%: Sentinel-1 SAR Processing

The architecture therefore combines optical and radar observations rather than depending on a single sensor.

5. Step 3 — Multi-Temporal Radar Processing

The radar processing subsystem operates as an independent computational backbone.

Pipeline

Sentinel-1 SLC → Interferogram Processing → Phase Unwrapping → Time-Series Analysis → Surface Displacement (Δz)

Primary Processing Frameworks

• MintPy
• ISCE2

Output

The subsystem generates a temporal representation of surface displacement (Δz), which is subsequently consumed by the computational engine.

6. Step 4 — Terrain Topology Compiler

Copernicus DEM provides terrain information. The DEM is transformed into structured graph information using PyTorch Geometric:

Copernicus DEM → Elevation Gradients → Terrain Relationships → Directional Drainage Paths → Graph Representation

The graph representation provides spatial/topological context to the computational model.

7. Step 5 — Data Coalescence

Multiple data streams (Sentinel-1 SAR, Sentinel-2 Optical, DInSAR displacement, Copernicus DEM, Terrain graph, Environmental info, Historical info) are combined into a 4D spatio-temporal tensor:

[Batch, Time, Channels, Height, Width]

This tensor becomes a primary input to the computational engine.

8. Step 6 — Ensemble Kalman Filter

The system incorporates an Ensemble Kalman Filter into the state-assimilation loop:

Model Prediction → Satellite Observation → Prediction Error → EnKF Update → Corrected State

The objective is to continuously incorporate new observations and reduce state-estimation drift.

9. Step 7 — THE MRIDANSH Computational Core

The computational engine uses THE MRIDANSH architecture as the physical state-estimation foundation.

Core Neural Architecture

• Physics-Informed Temporal ConvLSTM
• Combines Spatial info, Temporal info, Terrain topology, State-estimation info, and Physical constraints.

10. Physics-Informed Layer

The computational model incorporates physical constraints through DeepXDE-based optimization.

Darcy-Based Constraint

The hydrological component is constrained using:

∇ · (K ∇h) = Ss ∂h/∂t

Mohr-Coulomb Constraint

Geotechnical behavior is constrained using:

τ = c + σn tan(φ)

11. Unified Subsurface State Tensor

The computational engine generates a unified physical representation (VWC, Bulk Density, Shear Modulus). This unified state becomes the interface between the physical estimation engine and downstream domain-specific diagnostic modules.

12. Step 8 — Dual-Domain Diagnostic Interface

The unified state is processed through two diagnostic pathways:

Mode A — Agronomic

• Root-zone moisture
• NPK-related estimates
• Precision variable-rate information
• Agricultural condition indicators

Mode B — Geotechnical

• Plasticity indicators
• Subgrade compaction indicators
• Mohr-Coulomb stability analysis
• California Bearing Ratio (CBR) related indicators
• Structural degradation indicators

13. Step 9 — Spaceborne Forensic Analysis

The defining audit mechanism compares satellite-observed deformation with model-generated geotechnical indicators:

DInSAR Δz + Geotechnical State → Cross-Reference → Threshold / Anomaly Analysis → Flagged Coordinate

The flagged coordinate can then be investigated as a potential infrastructure anomaly.

14. Step 10 — Storage & Command Center

Computed system states and temporal observations are stored in a local SQLite database.

• Storage Layer: SQLite (Time-series state records)
• Visualization Layer: Streamlit, PyDeck, Leaflet / Folium
• Outputs: Interactive maps, Time-series plots, 3D subsurface visualization, Infrastructure anomaly info, Automated audit report

15. Architectural Data Flow

User → Streamlit → AOI / BBox → STAC + ASF → Sentinel-2 / Sentinel-1 → DInSAR + Terrain Processing → Multi-Modal Data Fusion → 4D Spatio-Temporal Tensor → EnKF → THE MRIDANSH Core → Physics-Informed ConvLSTM → Unified Subsurface State → [Agronomic / Geotechnical Intelligence] → Forensic Analysis → Anomaly Detection → SQLite Storage → Interactive Command Center → Automated Audit Output

16. Relationship With THE MRIDANSH

MARGAVEDHA does not replace THE MRIDANSH. Instead, the architecture treats THE MRIDANSH as a computational scientific core:

THE MRIDANSH (Physics-Guided Soil State Estimation) → Unified Subsurface State → ANVESANA : MARGAVEDHA (Infrastructure Intelligence) → Autonomous Infrastructure Auditing

17. Future Extensibility

The architecture is designed to support future integration of:

• Additional SAR and optical missions
• Improved geotechnical models
• Advanced infrastructure datasets & risk models
• Larger-scale temporal archives
• Autonomous monitoring workflows

Architecture Summary

ANVESANA : MARGAVEDHA combines orbital radar intelligence, Earth observation, terrain topology, state estimation, physics-informed machine learning, and geotechnical analysis into a unified infrastructure-auditing architecture.

Core Principle: Observe from Space → Estimate Physical State → Apply Physics → Interpret Geotechnical Behavior → Detect Infrastructure Anomalies.

ANVESANA : MARGAVEDHA — Technology Stack

Technology Stack Version

Architecture: v1.0
Project Status: Planned / Research & Architecture Phase

1. Technology Overview

ANVESANA : MARGAVEDHA is designed as a multi-disciplinary computational architecture combining:

• Earth Observation
• Synthetic Aperture Radar
• Multi-temporal InSAR
• Geospatial Computing
• Deep Learning
• Physics-Informed Machine Learning
• State Estimation
• Geotechnical Intelligence
• Interactive Visualization
• Automated Reporting

The technology stack is organized into independent but interconnected layers.

2. Geospatial & Satellite Data Ingestion

STAC API

Technology: STAC API
Python Client: pystac-client

Purpose

Used for discovering and filtering Earth observation datasets based on:

• Area of Interest
• Acquisition date
• Cloud coverage
• Spatial extent
• Available assets

The STAC layer primarily supports Sentinel-2 optical data discovery.

ASF Search API

Technology: ASF Search API
Python Package: asf_search

Purpose

Used to discover and retrieve Sentinel-1 SAR datasets from the Alaska Satellite Facility ecosystem. Primary use cases include:

• Sentinel-1 acquisition search
• Spatial filtering
• Temporal filtering
• SAR product discovery
• Multi-temporal data preparation

3. Earth Observation Sensors

Sentinel-1 SAR

Data Type

Synthetic Aperture Radar

Primary Role

Sentinel-1 provides radar observations for:

• Surface deformation analysis
• DInSAR processing
• Ground displacement monitoring
• Cloud-independent Earth observation

Processing Target

Multi-temporal surface displacement: Δz

Sentinel-2 Optical

Data Type

Multispectral Optical Imagery

Primary Role

Sentinel-2 provides optical Earth observation information for:

• Surface characterization
• Multispectral analysis
• Environmental context
• Soil and vegetation-related information

Planned Bands

• B02, B04, B08, B11, B12

Copernicus DEM

Resolution

30 m

Primary Role

Copernicus DEM provides terrain information for:

• Elevation
• Terrain gradients
• Drainage analysis
• Topological graph construction
• Spatial context

4. Multi-Temporal Radar Processing

MARGAVEDHA contains an independent radar-processing backbone dedicated to multi-temporal interferometric analysis.

ISCE2

Technology: ISCE2
Full Name: InSAR Scientific Computing Environment
Role: Used for interferogram generation, initial SAR processing, InSAR preprocessing, and radar phase processing. ISCE2 is also considered as a backup computational pathway for the radar-processing subsystem.

5. Raster & Geospatial Processing

GeoPandas

Package: geopandas
Role: Used for vector geospatial processing, bounding-box operations, spatial data manipulation, and geographic data analysis.

6. Coordinate & Spatial Processing

The geospatial layer is designed to handle:

• Coordinate Reference Systems
• Spatial transformations
• Bounding-box operations
• Raster/vector alignment
• Geographic masking

The system maintains a consistent spatial reference between satellite, terrain, and model datasets.

7. Topological Graph Processing

PyTorch Geometric

Package: torch_geometric
Role: PyTorch Geometric is used to transform terrain information into graph-based computational structures.

Input

Copernicus DEM

Processing Pipeline

Copernicus DEM → Elevation Relationships → Terrain Graph → Directional Drainage Relationships → Graph Neural Network Input

Intended Information

• Terrain connectivity
• Elevation relationships
• Directional drainage paths
• Spatial topology

8. Core Deep Learning Framework

PyTorch

Package: torch
Role: PyTorch acts as the primary deep-learning framework for the computational core. It supports:

• Neural network execution
• Tensor operations
• Automatic differentiation
• Model training
• Physics-informed optimization

9. Spatial-Temporal Neural Architecture

ConvLSTM

Role: ConvLSTM provides the temporal-spatial learning backbone. It is designed to process sequential spatial observations while retaining spatial structure.

Spatial Data + Temporal Sequence → ConvLSTM → Spatial-Temporal Representation

The architecture is intended to support the evolution of the estimated subsurface state over time.

10. Physics-Informed Machine Learning

DeepXDE

Technology: DeepXDE
Role: DeepXDE provides the physics-informed optimization layer. It is intended to incorporate physical constraints into the learning process.

Primary physical constraints include:

• Darcy-based hydrological behavior
• Mohr-Coulomb geotechnical behavior

11. Darcy-Based Physical Constraint

The hydrological component incorporates a Darcy-based physical relationship:

∇ · (K ∇h) = Ss ∂h/∂t

Purpose

The constraint is intended to regulate modeled water movement and moisture evolution through the soil system.

12. Mohr-Coulomb Constraint

The geotechnical component incorporates the Mohr-Coulomb criterion:

τ = c + σn tan(φ)

Purpose

The constraint provides a physical representation of shear failure behavior and helps constrain geotechnical state estimation.

13. Ensemble Kalman Filter

FilterPy

Package: filterpy
Role: FilterPy is intended to provide the computational implementation of the Ensemble Kalman Filter (EnKF).

Primary Function

The EnKF operates as a data-assimilation mechanism:

Model Prediction → Satellite Observation → Prediction Error → EnKF Update → Corrected State

Purpose

• Assimilate new observations
• Estimate prediction error
• Correct model state
• Reduce model drift
• Maintain temporal consistency

14. Spatio-Temporal Tensor Processing

Multiple information sources (Sentinel-1 SAR, Sentinel-2 Optical, DInSAR displacement, Copernicus DEM, Terrain graph, Environmental info, Historical observations) are combined into a unified computational tensor:

[Batch, Time, Channels, Height, Width]

This representation is used as a primary input to the spatial-temporal computational architecture.

15. Unified Subsurface State

The computational engine is designed to generate a unified subsurface representation:

• Hydro-Physical: Volumetric Water Content (VWC), Bulk Density
• Mechanical: Shear Modulus
• Geotechnical: Plasticity-related indicators, Subgrade condition indicators, CBR-related outputs

16. Storage Layer

SQLite

Technology: SQLite
Python Interface: sqlite3
Role: Used as the planned local storage layer for temporal system states and computed indicators (Time-series observations, Model states, Geotechnical indicators, Displacement records, Anomaly events, Processing metadata).

17. Interactive Command Center

Streamlit

Technology: Streamlit
Role: Streamlit provides the primary interactive application interface. It integrates data ingestion, map interaction, processing controls, model outputs, time-series visualization, geotechnical analysis, and audit outputs.

18. Interactive Mapping

Leaflet / Folium

Role: Used for interactive geographic visualization and Area of Interest selection (Interactive maps, Bounding Box selection, Geographic navigation, Spatial visualization).

19. 3D Visualization

PyDeck

Technology: PyDeck
Role: Used for interactive 3D geospatial visualization (3D terrain visualization, Spatial displacement visualization, Subsurface representation, Infrastructure-related spatial analysis).

20. Automated Reporting

ReportLab & FPDF2

Technologies: ReportLab / FPDF2
Role: Used for generating automated PDF-based infrastructure audit reports containing Area of Interest details, processing period, observed displacement, geotechnical indicators, detected anomalies, visualization outputs, and computational metadata.

21. Application Architecture

+--------------------------------------------------------------+ | USER INTERFACE | | Streamlit + Leaflet / Folium | +------------------------------+-------------------------------+ | v +--------------------------------------------------------------+ | DATA INGESTION | | STAC / pystac-client / ASF Search / asf_search | +------------------------------+-------------------------------+ | v +--------------------------------------------------------------+ | EARTH OBSERVATION PROCESSING | | Sentinel-1 + Sentinel-2 + Copernicus DEM | +------------------------------+-------------------------------+ | v +--------------------------------------------------------------+ | MULTI-TEMPORAL RADAR PROCESSING | | MintPy + ISCE2 | +------------------------------+-------------------------------+ | v +--------------------------------------------------------------+ | GEOSPATIAL / GRAPH PROCESSING | | Rasterio + GeoPandas + PyTorch Geometric | +------------------------------+-------------------------------+ | v +--------------------------------------------------------------+ | CORE AI ENGINE | | PyTorch + ConvLSTM + PyTorch Geometric | +------------------------------+-------------------------------+ | v +--------------------------------------------------------------+ | PHYSICS-INFORMED LAYER | | DeepXDE + Darcy + Mohr-Coulomb | +------------------------------+-------------------------------+ | v +--------------------------------------------------------------+ | DATA ASSIMILATION | | FilterPy + EnKF | +------------------------------+-------------------------------+ | v +--------------------------------------------------------------+ | GEOTECHNICAL INTELLIGENCE | | Soil State + CBR + Stability | +------------------------------+-------------------------------+ | v +--------------------------------------------------------------+ | STORAGE & OUTPUT | | SQLite + PyDeck + Streamlit + ReportLab / FPDF2 | +--------------------------------------------------------------+

22. Core Technology Stack — Quick Reference

| Layer | Technologies | | :--- | :--- | | Interface | Streamlit | | Interactive Mapping | Leaflet / Folium | | Satellite Discovery | STAC / pystac-client | | SAR Data Search | ASF Search / asf_search | | Optical Data | Sentinel-2 | | Radar Data | Sentinel-1 | | Terrain | Copernicus DEM | | InSAR | MintPy | | SAR Processing | ISCE2 | | Raster Processing | Rasterio | | Vector Processing | GeoPandas | | Deep Learning | PyTorch | | Graph Learning | PyTorch Geometric | | Temporal Model | ConvLSTM | | Physics-Informed ML | DeepXDE | | State Estimation | FilterPy / EnKF | | Database | SQLite / sqlite3 | | 3D Visualization | PyDeck | | PDF Generation | ReportLab / FPDF2 |

23. Architectural Technology Philosophy

The technology stack follows a modular design philosophy. Each major subsystem has a distinct responsibility:

Satellite APIs → Data Acquisition → Radar / Optical Processing → Geospatial Processing → Graph + Tensor Representation → AI + Physics → State Estimation → Geotechnical Interpretation → Infrastructure Audit

This modularity allows individual components to evolve independently without requiring a complete redesign of the architecture.

24. Future Technology Expansion

The technology stack may be expanded during future implementation phases:

• Additional SAR missions & optical Earth observation datasets
• Advanced InSAR processing & more sophisticated geotechnical models
• Distributed processing & containerized deployment
• API-based backend services & advanced 3D visualization
• Large-scale temporal databases

Technology Summary

ANVESANA : MARGAVEDHA combines satellite data infrastructure, multi-temporal SAR interferometry, geospatial computing, graph-based terrain intelligence, physics-informed deep learning, state estimation, and interactive geotechnical visualization into a unified autonomous infrastructure-auditing technology stack.