I build production-grade GeoAI: spatial decision-support systems, ArcGIS automation exposed to LLM agents, and deep-learning pipelines for satellite imagery — connecting rigorous mathematical models to systems people can actually use.

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Technical Core & Methodology

I focus on structural data science and deep learning architectures, building robust end-to-end machine learning pipelines (O(N) efficiency), data ingestion engines, and secure automation interfaces that connect mathematical models with complex enterprise systems.

Core AI/ML

Spatial Data Science

MLOps & Infrastructure

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Flagship Systems & ML Infrastructure

1. arcgis-mcp-bridge

An institutional-grade Model Context Protocol (MCP) framework exposing exactly 100 specialized geoprocessing tools directly to LLM hosts and intelligent agents — turning ArcGIS Pro into a programmable backend for AI workflows.

• Process Isolation: Built with a strict decoupled multi-process architecture (Async Core / Isolated Worker Subprocess) to guarantee runtime protection against environment blockages.
• Security Layer: Features a strict PathGuard sandbox enforcing prefix validation over database structures before any algorithmic execution occurs.

flowchart LR
    A["LLM Host / AI Agent"] -->|MCP protocol| B["MCP Server<br/>Async Core"]
    B --> C{"PathGuard<br/>sandbox"}
    C -->|path validated| D["Isolated Worker<br/>Subprocess"]
    C -.->|rejected| X["Blocked"]
    D --> E["ArcGIS Pro · ArcPy"]
    E --> F["100 geoprocessing tools"]

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2. agri-dss  —  Live at tarimsalkoridor.online

A fully client-side Spatial Decision Support System (Agri-DSS) for the Western Antalya agricultural corridor — 5 districts, 147 neighborhoods (Demre, Finike, Kaş, Kemer, Kumluca) — turning local agronomic and economic knowledge into a concrete, printable plan for each neighborhood: seasonal crops rated by yield and profitability, a long-term orchard investment, and an emerging market opportunity.

• Zero-Backend Static Architecture: A single vanilla-JS index.html carrying all DSS logic against a decoupled data.json layer — no backend, no build step. Trivially hostable on GitHub Pages, instantly auditable, and immune to server outages.
• DRY Data Contract: A compact cropSets / longTermCrops / regions structure resolved at runtime — recommendations can be updated by editing data.json alone, with no code changes.
• Swiss / Typographic Interface: International Typographic Style UI (Archivo + Space Mono, modular grid, single agricultural-green accent) with a guided stepper, corridor diagram, and live counters — collapsing to a clean ink-on-white A4 print layout for village boards and cooperatives.

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Academic & Research Projects

Reproducible, peer-review–oriented studies in spatial econometrics, urban resilience, and deep learning for remote sensing.

agri-unet  ·  Deep Learning / CV  · 

The codebase for my TÜBİTAK 2209-A research project (University Students Research Projects Support Program) — an accepted, grant-funded study. A U-Net semantic segmentation pipeline for agricultural pattern identification from high-resolution, multi-temporal satellite imagery, extracting field parcels and crop structures for downstream suitability modeling.

turkiye-housing-prices-pandemic  ·  Spatial Econometrics

Region-level analysis of Türkiye's housing market that separates real (inflation-adjusted) price growth from inflation, comparing the six years before and after the COVID-19 pandemic (2014–2025).

• Reproducible Python notebook with high-resolution choropleth figures and a House Price Index (HPI) deflation pipeline.
• LISA (Local Indicators of Spatial Association) analysis to detect statistically significant regional clusters and spatial outliers.

kutri-resilience-index  ·  Composite Indicators

A reproducible urban-territorial resilience index prototype for Kaş / Bayındır, Antalya, based on a five-pillar composite indicator framework.

• Transparent indicator normalization and weighting methodology with fully reproducible notebooks and figures.
• Bridges quantitative spatial analysis with applied territorial planning.

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Featured Repositories

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Active Research & Deep Learning Workspace

• Computer Vision for Remote Sensing: Formulating automated pipelines for agricultural pattern identification and urban object extraction from high-resolution multi-temporal satellite imagery using Convolutional Neural Networks (CNN) and U-Net segmentation models.
• Urban Resilience Forecasting: Engineering predictive spatial suitability matrices and long-term geometric resilience frameworks for horizon target lines using robust statistical models.

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Focus

Open to collaborative tracks involving production-grade Data Science, Spatial Machine Learning pipelines, and automated GeoAI systems architecture.

• Live App: tarimsalkoridor.online
• Email: edcoders@gmail.com