EcoAudit-AI 🌍🛰️

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A Digital MRV (Measurement, Reporting, and Verification) Framework utilizing multi-modal deep learning and satellite data fusion to deliver transparent, verifiable carbon sequestration tracking.

📝 Table of Contents

• Problem Statement
• Idea / Solution
• Core Features
• System Architecture & Methodology
• Technology Stack
• Getting Started / Installation
• Team ByteForce Branching Strategy
• Future Scope
• Authors & Team

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🚨 Problem Statement

Accurate quantification of forest biomass (AGB) and carbon stocks underpins the multi-billion-dollar voluntary carbon credit market. Yet, existing audit methods face critical technical barriers that enable greenwashing:

1. Manual Field Inventories: Logistically inefficient and impossible to scale to remote regions.
2. Optical Data Limitations: Standard satellites suffer from Cloud Occlusion (especially in rainforests) and Canopy Signal Saturation (dense forests produce identical reflectance to young woodlands).
3. Hidden Carbon Ignored: Existing tools only measure above-ground biomass, ignoring root systems and soil organic carbon.

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💡 Idea / Solution

EcoAudit-AI is a comprehensive dMRV platform designed to combat greenwashing.

By fusing Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (Optical) satellite imagery via the CIOPB Framework, our system bypasses cloud and canopy saturation limits. We utilize a PIO-optimized BiLSTM Deep Learning Model to generate high-integrity forest biomass tracking, partitioning results into multiple ecological pools paired with a Monte Carlo uncertainty map.

The EcoAudit-AI interactive auditor dashboard rendering multi-pool carbon metrics.

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🚀 Core Features

• 🗺️ Point-and-Click ROI Selector: An interactive Mapbox/Leaflet canvas allowing auditors to draw custom polygons over forested assets for instant evaluation.
• 🌳 Multi-Pool Carbon Splitter: Instantly partitions total biomass inference into three distinct ecological pools: Aboveground Biomass, Belowground Roots, and Soil Organic Carbon.
• 📊 Temporal Timeline Scrubber: Scrub between historical baselines and current dates to compute real-time carbon sequestration trends.
• 📄 Automated PDF Audit Reports: Pixel-level maps, pool breakdowns, and confidence metrics compiled into an immutable, downloadable audit trail to prevent double-counting.
• 🎯 0-40% Uncertainty Propagation: Runs Monte Carlo simulations to deliver confidence maps, proving to auditors exactly where predictions are stable.

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🏗️ System Architecture & Methodology

Our backend data ingestion and machine learning pipeline (CIOPB) transitions seamlessly from raw geospatial extraction to automated validation reports:

🏗️ System Architecture & Methodology

Our backend data ingestion and machine learning pipeline transitions seamlessly from raw multi-modal geospatial extraction to predictive analytics and automated verification reports:

Figure: Detailed pipeline illustrating multi-modal satellite data ingestion, Random Forest regression, and dMRV reporting layers.

1. Multi-Modal Data Fusion & Engineering: Collects structural radar geometry (Sentinel-1 Radar) and multi-spectral bands (Sentinel-2 Optical), filtering out cloud occlusion while calculating key vegetation indices and terrain slopes over selected Regions of Interest (ROI).

2. Predictive Modeling Engine: Utilizes an optimized Random Forest Regression Model trained against high-integrity GEDI Spaceborne LiDAR ground-truth targets to model complex, non-linear environmental relationships.

3. Feature Valuation & Assessment: Leverages the Gini Index (MDI) for strict feature importance ranking, ensuring transparent model explainability before calculating localized biomass, assessing total accuracy, and compiling structural validation reports.

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💻 Technology Stack

• Frontend: Next.js / React, Tailwind CSS, Folium/Mapbox API
• Backend: Python 3.11, FastAPI, Uvicorn
• AI / ML Engine: PyTorch, Scikit-Learn, Google Earth Engine (GEE) Python API
• Database (MVP): In-Memory Dictionary / PostgreSQL (PostGIS)
• Deployment: Vercel (Frontend), Render (Backend)

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🏁 Getting Started / Installation

Prerequisites

• Python 3.11+
• Node.js (v18+)
• Google Earth Engine Service Account credentials (credentials.json)

Installation & Local Setup

1. Clone the repository:

git clone [https://github.com/Adityaraj-Gupta-JI/EcoAudit-AI.git](https://github.com/Adityaraj-Gupta-JI/EcoAudit-AI.git)
cd EcoAudit-AI

2. Backend Setup (FastAPI):

cd backend
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate
pip install -r app/requirements.txt
python -m uvicorn app.main:app --reload

3. Frontend Setup (Next.js):

cd frontend
npm install
npm run dev

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🌿 Team ByteForce Branching Strategy

This repository is an enterprise monorepo. During the hackathon, team members develop exclusively in their assigned sandbox branches:

• 🧠 AI Engineer: feature/ai-engine (Targets /ai-engine/)
• 💻 Backend Developer: feature/api-backend (Targets /backend/)
• 🎨 Frontend Designer: feature/ui-frontend (Targets /frontend/)
• 🛠️ DevOps & Integration: development ➔ main

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🔭 Future Scope

Post-hackathon, EcoAudit-AI plans to transition our mock database ledger to a verified Web3/Polygon blockchain smart contract. This will allow verified carbon offsets to be minted directly as non-falsifiable tokens (dMRV to Tokenization pipeline), integrating seamlessly with Verra and Gold Standard registries.

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✍️ Authors & Team

Team ByteForce (ID: SEH26_114)

• Adityaraj Gupta (DevOps / DevSecOps Lead) - Infrastructure & Deployment
• Aryan Ahirwar (CAIO) - Multi-Modal Deep Learning & GEE Pipelines
• Krishna Agrawal (CTO) - FastAPI Backend & Ledger Integration
• Ronak Kumar (CPO) - Next.js UI/UX & Geospatial Frontend

🎉 Acknowledgments

• 2nd SmartEarth 2026 Hackathon organizers and Grand Jury at Nazarbayev University.
• Open-source data from ESA Copernicus (Sentinel-1/2) and NASA (GEDI LiDAR).