Geospatial Solutions
💾
🤖 Powered by MCP Agents

Spatial Data Acquisition & ETL

Get the right data, cleaned and ready for action

90% (80 hrs → 8 hrs/week)
Time Saved
$90K/year
Cost Reduction
99.7% data quality
Accuracy
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Industries Served:
All industries - foundational service

Overview

Sourcing from 25+ authoritative providers, plus niche datasets as needed. Automated ETL pipelines: deduplication, reprojection, QA/QC, and warehousing. Flexible delivery: OGC services, direct DB connections, or offline packages.

Visual Workflow

How It Works: Visual Breakdown

See the complete automation workflow with diagrams and code examples

Automated Workflow Diagram
Visual representation of the MCP agent workflow from trigger to delivery

System Architecture

Intelligent data source recommendation system with MCP agent, multi-source acquisition, GDAL processing pipeline, and automated quality validation.

🏗️Component Architecture Diagram
Visual representation of system components, data flow, and integrations

⚙️Key Components

MCP Source Recommendation Agent

Analyzes data needs and recommends optimal sources from 100+ catalogs

Data Source Catalog

Indexed database of 100+ federal, state, and commercial data sources

GDAL Processing Pipeline

Format conversion, reprojection, and standardization (50+ formats)

Data Quality Checks

Automated validation of completeness, accuracy, and consistency

Metadata Generation

ISO 19115-compliant metadata with lineage documentation

S3 Bucket Storage

Organized cloud storage with versioning and access controls

Diagram Legend
MCP AI Agents
Processing/Storage
Output/Visualization
Analytics/Monitoring

Visual Examples

See the solution in action with real dashboard examples and visual comparisons

🖼️Data Source Catalog
Searchable catalog of 100+ federal, state, and commercial GIS data sources

Key Features:

✓Filterable by data type (vector, raster, tabular)
✓Search by keyword, geography, or theme
✓Source metadata (update frequency, resolution)
✓Direct download links
✓MCP agent recommendations based on request
✓Popularity and quality ratings
🖼️Organized File Structure
Standardized folder structure with processed data and metadata

Key Features:

✓Organized by theme (elevation, boundaries, demographics)
✓Original and processed subfolders
✓Standardized naming convention
✓README.md with data dictionary
✓ISO 19115 metadata XML files
✓QA/QC validation reports

💡Note: The dashboard screenshots above are placeholders. Actual screenshots will be added after finalizing native in-app dashboards or capturing real application screenshots. Image paths are specified for easy integration.

🤖 Agentic Workflow

Automated MCP Agent Workflow

Powered by n8n, Make.com, and Model Context Protocol agents

Workflow Trigger
How the automation starts

New data request submitted or scheduled refresh

1
Webhook Trigger
Receives data request (type, location, format, frequency)
n8n
REST API
2
MCP Agent Source Selection
AI suggests authoritative sources, formats, and ETL steps based on requirements
MCP
Azure OpenAI
LangChain
MCP Agent Prompt:

“List the best sources for elevation and hydrography data for Iowa. Recommend: authoritative federal/state sources (USGS 3DEP for elevation, NHD Plus for hydro), optimal formats (GeoTIFF for elevation, geodatabase for hydro), and ETL steps (download, reproject to NAD83, clip to Iowa boundary, load to PostGIS with spatial indexes).”

3
API Nodes
Download data from multiple sources simultaneously
REST APIs
FTP
S3
Web scraping
4
ETL Processing
GDAL transformations: reproject, clip, simplify, validate topology
GDAL/OGR
Docker
Python
GeoPandas
5
Data Quality Checks
Automated QA: null checks, geometry validation, duplicate detection, schema compliance
Python
PostGIS
Great Expectations
6
Database Load
Load to PostGIS with spatial indexes, constraints, and metadata
PostGIS
PostgreSQL
pgAdmin
7
Notification
Send QA report (PDF), update dashboard, notify via Slack/Teams
SendGrid
Slack API
Microsoft Teams
Puppeteer
Deliverables
What you receive automatically
  • PostGIS database with spatial indexes
  • OGC-compliant WMS/WFS services
  • Mapbox tileset (vector tiles)
  • Offline data packages (Shapefiles, GeoPackage)
  • Data quality report (PDF)
  • API documentation for access

Key Features

25+ authoritative data sources

Automated change detection

Multi-format support (Shapefile, GeoJSON, GeoTIFF, GeoPackage, KML)

Coordinate system reprojection

Topology validation and repair

Deduplication and conflation

Spatial indexing (GIST, BRIN)

Metadata management (ISO 19115)

OGC-compliant web services (WMS, WFS, WMTS)

Technology Stack

Automation
n8n
Apache Airflow
Prefect
GIS & Mapping
GDAL
PostGIS
GeoServer
Mapbox Tilesets API
AI & Analysis
MCP Agents
Azure OpenAI

API Integrations

USGS National Map
Elevation, hydro, boundaries
NREL Solar API
Solar resource data
FEMA NFHL
Flood zones
USFWS Critical Habitat
Endangered species
Census API
Demographics, boundaries
NOAA Weather
Climate and weather data
County Assessor APIs
Parcels, ownership
EPA EnviroFacts
Environmental compliance
HIFLD
Critical infrastructure
Commercial providers
LandVision, CoreLogic, Wood Mackenzie
Success Story

Real-World Results

Utility Scale Solar Developer
All industries - foundational service

Challenge

Daily updates needed for 15+ spatial datasets across 8 states (parcels, transmission, substations, solar resource, environmental constraints, permitting status). 2 GIS analysts spending 4 hrs/day = $100K/year.

Our Solution

n8n workflow (120 nodes) runs daily at 2 AM. Monitors 25+ data sources for updates, downloads new data, runs GDAL transformations (reproject, clip, validate), performs PostGIS deduplication and change detection, generates QA report (PDF), publishes to Mapbox tilesets, sends Slack notification with summary.

Results Achieved

Fully automated: 0 manual hours
2.5 hour nightly processing (unattended)
99.7% data quality (vs 94% manual)
Change detection: identifies 50-200 parcel updates daily
Cost: $10K/year (vs $100K manual)
$90K annual savings (90% reduction)
Data always current (vs 1-2 week lag with manual updates)
Implementation Timeline
2.5 hours (automated nightly)

Flexible Pricing Options

Choose the plan that fits your needs

Pilot Project
Perfect for testing the solution
$8,000 (5 datasets, one region, 30-day trial)

Test the solution with a limited scope project to validate ROI before full deployment.

Get Started
Most Popular
Monthly Subscription
Ongoing automation & support
$3,000/month (unlimited datasets, daily updates, 24/7 monitoring)

Full production deployment with hosting, monitoring, and ongoing updates included.

Schedule Demo
Enterprise
Custom solutions at scale
Custom (multi-region, dedicated infrastructure, SLA guarantees)

White-label solutions, multi-tenant deployments, SLA guarantees, and dedicated support.

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Frequently Asked Questions

Common questions about our GIS automation solutions

What tasks can MCP agents automate?
MCP agents excel at: parcel screening (solar, real estate), queue monitoring, data extraction from PDFs/websites, report generation, spatial analysis workflows, data quality checks, and integration between systems. Basically, any GIS task that's repetitive and rule-based.
How much do MCP agents cost to run?
Can MCP agents learn from our past decisions?
What happens if an MCP agent encounters an error?
How quickly can you deploy a custom MCP agent for my use case?

Still have questions?

Our GIS automation experts are here to help. Schedule a free consultation to discuss your specific needs.

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Schedule a free 30-minute consultation to see how Spatial Data Acquisition & ETL can deliver measurable ROI for your organization.

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Implementation stack

Technologies We Work With

We choose the stack around the job: browser maps for decision surfaces, spatial databases for reliable data, Python and GIS libraries for analysis, and annotation tools when imagery needs reviewer-ready labels.

QGIS

GIS Software

ESRI ArcGIS

GIS Platform

PostgreSQL

Database

PostGIS

Spatial Database

AWS

Cloud Platform

Google Cloud

Cloud Platform

DuckDB

Analytics Database

OpenAI

AI Platform

Claude AI

AI Assistant

CVAT

Annotation Tool

Python

Programming

React

Frontend

Node.js

Backend

Docker

Containerization

Kubernetes

Orchestration

Azure

Cloud Platform

TensorFlow

Machine Learning

Pandas

Data Analysis

NumPy

Scientific Computing

Jupyter

Data Science

Git

Version Control

Linux

Operating System

Ubuntu

Operating System

Mapbox

Mapping Platform

Leaflet

Web Mapping

Fastapi

API Framework

GeoPandas

Geospatial Analysis

GDAL

Geospatial Library

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