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Muhammed Enes Duran — GeoAI · Spatial Data Science · MCP Infrastructure · Remote Sensing ML · Applied Simulation Systems

Portfolio PyPI profile Agri-DSS live app FOUNDER.EXE on itch.io Email

I build GeoAI infrastructure, remote-sensing data pipelines, spatial decision-support systems, and applied simulation products.

My work sits between research-grade spatial analysis and usable software: ArcGIS Pro automation for LLM/MCP workflows, reproducible Sentinel-2 data pipelines, browser-native agricultural DSS tools, and realistic strategy simulations that encode real-world institutional rules into interactive systems.

100 MCP geoprocessing tools Sentinel-2 data pipeline on PyPI and Zenodo 147 neighborhoods modeled Published game: FOUNDER.EXE TÜBİTAK 2209-A grant funded


Shipping Now

Project Type Status Links
arcgis-mcp-bridge GeoAI infrastructure / MCP tooling Active · PyPI · Glama A-rated GitHub · PyPI
sentinel-crop-pipeline Sentinel-2 data pipeline / research software Active · PyPI · Zenodo DOI GitHub · PyPI · DOI
agri-dss Spatial Decision Support System Live Live app · GitHub
FOUNDER.EXE Browser-based startup simulation Published Play on itch.io
kutri-resilience-index Composite indicator / spatial resilience research Research prototype GitHub

Work Map

Track What I build Representative work
GeoAI Infrastructure Secure automation layers connecting GIS runtimes, LLM hosts, and geoprocessing workflows arcgis-mcp-bridge
Remote Sensing ML Pipelines Reproducible Sentinel-2 data preparation, patch generation, label masks, and downstream ML-ready datasets sentinel-crop-pipeline, agri-unet
Spatial Decision Systems Browser-native tools that turn spatial and agricultural knowledge into usable public-facing products agri-dss
Applied Simulation Products Rule-driven browser simulations for economic, regulatory, and strategic decision-making FOUNDER.EXE
Urban / Regional Analytics Spatial econometrics, resilience indices, and reproducible territorial analysis kutri-resilience-index, turkiye-housing-prices-pandemic

Technical Core

AI / ML / Data Science
Python PyTorch TensorFlow scikit-learn NumPy pandas SciPy

Spatial Data Science / Remote Sensing
GeoPandas Shapely ArcPy Rasterio PySAL QGIS Copernicus

Systems / Product Engineering
FastAPI Pydantic Docker Pytest Ruff Mypy JavaScript HTML5 CSS3


Flagship Systems

An MCP framework exposing 100 specialized ArcGIS Pro / ArcPy geoprocessing tools to LLM hosts and intelligent agents while keeping the licensed GIS runtime isolated from the host process.

  • MCP-native GIS automation: ArcGIS Pro geoprocessing exposed through Model Context Protocol.
  • Runtime isolation: async MCP server separated from the ArcPy worker subprocess.
  • Path safety: PathGuard validation layer before filesystem and geodatabase operations.
  • Distribution: published on PyPI and listed on Glama with A-rated MCP quality.
  • Testing: mocked ArcPy test strategy for development without requiring ArcGIS Pro in CI.
flowchart LR
    A["LLM Host / AI Agent"] -->|MCP| B["Async MCP Server"]
    B --> C{"PathGuard"}
    C -->|validated| D["Isolated ArcPy Worker"]
    C -.->|blocked| X["Rejected"]
    D --> E["ArcGIS Pro / ArcPy"]
    E --> F["100 geoprocessing tools"]
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A reproducible Sentinel-2 data preparation pipeline for crop-classification research. It prepares training-ready geospatial datasets for downstream CNN/U-Net or other remote-sensing ML models.

  • CDSE-first workflow: Sentinel-2 L2A scene discovery and AOI-cropped download through Copernicus Data Space Ecosystem.
  • Cloud-aware preprocessing: metadata cloud filtering, SCL invalid-pixel checks, cloud/shadow/snow masking, reflectance normalization, and spectral indices.
  • Training-ready patches: COG/TIFF patches for QGIS inspection plus NPY/TFRecord exports for model training.
  • Spatially blocked splits: train/validation/test assignment by spatial blocks to reduce leakage from neighbouring patches.
  • Ground truth: GeoJSON/Shapefile crop polygons rasterized into patch-aligned uint8 masks.
  • Research artifact: PyPI package, Zenodo DOI, CI, examples, validation run, and citation metadata.

sentinel-crop-pipeline PyPI version DOI


A fully client-side Spatial Decision Support System for the Western Antalya agricultural corridor: 5 districts, 147 neighborhoods across Demre, Finike, Kaş, Kemer, and Kumluca.

  • Zero-backend static architecture: Vanilla JS over a decoupled data.json layer.
  • Auditable recommendation logic: compact crop and region schema resolved at runtime.
  • Field-facing output: guided interface and clean A4 print plans for local agricultural planning.
  • Deployment: static, easily hosted, and low-maintenance.

A browser-based startup simulation game modeling the practical stress of founding a company in Türkiye or the USA: taxes, incorporation choices, grants, investor expectations, cash flow, regulatory friction, and failure/exit outcomes.

  • Rules-driven simulation: country-specific startup, tax, grant, regulatory, and runway logic.
  • Idea-aware onboarding: the player writes a startup idea; the system shapes the simulation around sector and market assumptions.
  • Optional AI layer: Gemini, OpenAI, or Anthropic can be connected with the player's own API key; scripted fallbacks keep the game playable offline.
  • Browser-native product: static web app with local save persistence.

Play FOUNDER.EXE on itch.io


Research Projects

agri-unet · Deep Learning / Remote Sensing

Downstream model-training work for agricultural pattern identification from satellite imagery. This track is separated from sentinel-crop-pipeline, which focuses only on reproducible data preparation.

turkiye-housing-prices-pandemic · Spatial Econometrics

A reproducible regional analysis of Türkiye's housing market before and after the COVID-19 pandemic, separating nominal changes from inflation-adjusted real price growth and using spatial statistics to detect regional clusters.

  • HPI deflation workflow.
  • Choropleth and comparative regional figures.
  • LISA-style spatial cluster and outlier interpretation.

kutri-resilience-index · Composite Indicators / Urban Resilience

A reproducible urban-territorial resilience index prototype for Kaş / Bayındır, Antalya.

  • Five-pillar composite indicator framework.
  • Transparent normalization and weighting.
  • Reproducible notebooks, figures, and methodology.

Repository Map

Repository / Product Best entry point Why it matters
arcgis-mcp-bridge GeoAI infrastructure ArcGIS Pro automation for LLM/MCP workflows with runtime isolation and path safety
sentinel-crop-pipeline Remote sensing data pipeline Reproducible Sentinel-2 data preparation for downstream crop-classification models
agri-dss Product / DSS Live, zero-backend spatial decision-support system for agricultural planning
agri-unet Deep learning research Downstream remote-sensing model training for agricultural pattern identification
kutri-resilience-index Composite indicators Reproducible urban-territorial resilience index methodology
turkiye-housing-prices-pandemic Spatial econometrics Housing-price analysis with inflation adjustment and spatial clustering
FOUNDER.EXE Applied simulation product Browser game modeling startup formation, taxes, funding, and regulatory friction

Live Metrics

arcgis-mcp-bridge PyPI version sentinel-crop-pipeline PyPI version arcgis-mcp-bridge monthly PyPI downloads sentinel-crop-pipeline monthly PyPI downloads arcgis-mcp-bridge GitHub stars sentinel-crop-pipeline GitHub stars Glama A-rated MCP quality Profile views


Product Notes / Case-Study Hooks

These are the narratives I am expanding into separate portfolio case studies:

  • arcgis-mcp-bridge: separating licensed GIS execution from AI host runtimes while preserving secure, discoverable geoprocessing access.
  • sentinel-crop-pipeline: making Sentinel-2 crop-classification data preparation reproducible before the model-training stage.
  • agri-dss: compressing agricultural suitability and local economic knowledge into printable, village-level decision plans.
  • FOUNDER.EXE: encoding company formation, tax pressure, grants, investment logic, regulatory constraints, and optional AI advising into a playable browser simulation.

Current Focus

  • Agentic GIS & MCP Tooling: secure local automation layers that expose GIS operations to LLM agents without compromising ArcGIS Pro runtime isolation.
  • Remote Sensing ML Pipelines: reproducible Sentinel-2 workflows, ground-truth masks, spatially blocked splits, and downstream segmentation experiments.
  • Spatial Decision Support: static, auditable, browser-native systems for agricultural and territorial planning.
  • Applied Simulation Systems: strategy simulations that encode real-world regulatory, economic, and decision processes into interactive products.
  • Urban & Regional Analytics: composite indices, spatial econometrics, and territorial resilience frameworks.

Contact

Open to collaboration around production-grade GeoAI, spatial machine learning, remote-sensing data pipelines, MCP infrastructure, and applied simulation products.

About

Data Scientist & ML Engineer specializing in Spatial Data Science, GeoAI infrastructure, computer vision for remote sensing, and automated MLOps pipelines.

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