gispulse 1.2.0

Modular geospatial engine with business rules, triggers, and dual operating modes (DuckDB/PostGIS)

Modular, client-agnostic geospatial engine with business rules, triggers, and dual operating modes.

What is GISPulse?

GISPulse is a rules-as-config spatial processing engine. Instead of writing custom scripts for each geospatial workflow, you define declarative rules in JSON — buffer zones, spatial joins, filtering, clipping — and GISPulse executes them on any spatial dataset through any entry point.

The problem it solves: GIS teams repeatedly build one-off scripts to process spatial data. These scripts are fragile, hard to share, and tied to a single tool. GISPulse replaces them with a reusable engine where rules are configuration, not code.

Who it's for:

• GIS analysts who want to automate repetitive spatial processing without writing Python scripts
• Data engineers who need reliable, reproducible geospatial pipelines
• Development teams building spatial applications who need a backend engine with rules, triggers, and multi-format I/O
• Organizations managing spatial datasets across multiple tools (QGIS, ArcGIS, web portals) from a single source of truth

Key differentiators:

• Rules-as-config — define spatial operations in JSON, version them in Git, share across teams
• Client-agnostic — same engine behind CLI, REST API, MCP (LLM agents), OGC services, and event bus
• Dual mode — run locally with DuckDB (zero setup) or server-side with PostGIS (live triggers, shared state)
• Multi-client — web portal, QGIS plugin, ArcGIS add-in, Tauri desktop app, Python SDK

Documentation: https://imagodata.github.io/gispulse/

Vision

A central geospatial engine that executes business rules, spatial processing, and triggers on spatial datasets — regardless of the entry point (CLI, HTTP API, MCP, OGC, event bus).

Two operating modes

• Portable (session) — DuckDB + local file I/O (GPKG, GeoJSON, Shapefile, etc.). No external database needed.
• Persistent (server) — PostGIS central, live triggers via pg_notify, event-driven pipelines.

Installation

# Via pip (core only — DuckDB, no external dependencies)
pip install gispulse

# With PostGIS + API extras
pip install "gispulse[postgis,api]"

# With all extras (PostGIS, API, raster, network, S3, scheduling)
pip install "gispulse[all]"

# Pro meta-package (PostGIS + API + Redis + S3 + scheduling)
pip install gispulse-pro

# Via Homebrew (macOS)
brew tap imagodata/gispulse
brew install gispulse

# Via Scoop (Windows)
scoop bucket add gispulse https://github.com/imagodata/gispulse
scoop install gispulse

# Via install script (Linux / macOS)
curl -fsSL https://raw.githubusercontent.com/imagodata/gispulse/main/scripts/install.sh | bash

Quick start

# Scaffold a project
gispulse init my-project && cd my-project

# Run a rules pipeline on a spatial file
gispulse run parcels.gpkg --rules rules.json --output result.gpkg

# Inspect a file
gispulse info parcels.gpkg

# Launch the web portal
gispulse portal

# Launch the embedded viewer
gispulse serve parcels.gpkg

# Check system health
gispulse doctor

Docker (development)

docker compose up

Service	Port	Description
postgis	5433	PostGIS 16 spatial database
gispulse-api	8001	FastAPI backend with hot-reload
portal	5173	React web portal (Vite dev server)

Docker (local team — no source mounts)

docker compose -f docker-compose.local.yml up -d

Service	Port	Description
postgis	5433	PostGIS 16 (healthcheck, persistent volume)
gispulse-api	8001	FastAPI backend (configurable via GISPULSE_PORT)

Production VPS

cd deploy && cp .env.example .env  # edit values
docker compose -f docker-compose.prod.yml up -d

Service	Port	Description
postgis	—	PostGIS 16 (internal only)
gispulse-api	8001	FastAPI backend, 4 workers
caddy	80/443	Reverse proxy, auto-TLS (Let's Encrypt)
prometheus	—	Metrics scraping (15s interval)
grafana	3000	Monitoring dashboards
pg-backup	—	Daily PostgreSQL backups (30d retention)

CLI

gispulse init [dir]                    # Scaffold a new project
gispulse run <file> --rules <r>        # Execute a rules pipeline
gispulse validate <rules.json>         # Dry-run rule validation
gispulse info <file>                   # Inspect spatial file metadata
gispulse layers <file>                 # List layers in a multi-layer file
gispulse formats                       # Print supported read/write formats
gispulse capabilities                  # List registered capabilities
gispulse serve <file>                  # Launch embedded spatial viewer
gispulse portal                        # Launch the web portal
gispulse doctor                        # System diagnostics
gispulse engine [start|stop|status]    # Manage local sidecar engine
gispulse jobs [list|status|cancel]     # Manage async jobs via HTTP API
gispulse update [--check] [--force]    # Check for updates / self-update
gispulse telemetry [--status|--enable|--disable]  # Manage opt-in telemetry

Tiers

GISPulse uses a tier system controlled by the GISPULSE_TIER environment variable.

Feature	Community (free)	Pro	Enterprise
DuckDB engine	Yes	Yes	Yes
PostGIS / Hybrid engine	—	Yes	Yes
ESB triggers (pg_notify)	—	Yes	Yes
DAG executor	—	Yes	Yes
Visual pipeline editor	—	Yes	Yes
Raster / Network extras	—	Yes	Yes
Monitoring (Prometheus)	—	Yes	Yes
RBAC / Multi-project	—	—	Yes
SSO (SAML / OIDC)	—	—	Yes
White-label / Clustering	—	—	Yes

Pro and Enterprise require a GISPULSE_LICENSE_KEY. See pricing.yml for full details.

Architecture

graph TB
    subgraph Adapters["Adapters · entry points"]
        CLI["CLI · Typer"]
        HTTP["HTTP API · FastAPI"]
        MCP["MCP · FastMCP"]
        OGC["OGC · WFS / Features"]
        ESB["ESB · pg_notify"]
    end

    subgraph Core["Core Engine"]
        CAP["Capabilities\nbuffer, filter, clip\njoin, intersects ..."]
        RULES["Rules Engine\nloader, predicates\nvalidation"]
        ORCH["Orchestration\nJobRunner, GraphExecutor\nScenarioRunner"]
    end

    subgraph Persistence["Persistence Layer"]
        DUCK["DuckDB\nportable sessions"]
        PG["PostGIS\npersistent mode"]
        IO["Multi-format I/O\nGPKG, GeoJSON, SHP\nFGB, GeoParquet ..."]
    end

    CLI --> ORCH
    HTTP --> ORCH
    MCP --> ORCH
    ESB --> ORCH
    OGC --> IO

    ORCH --> CAP
    ORCH --> RULES
    ORCH --> DUCK
    ORCH --> PG
    CAP --> IO
    RULES --> CAP

    style Adapters fill:#E3F2FD,stroke:#0D47A1,color:#0D47A1
    style Core fill:#E8F5E9,stroke:#4CAF50,color:#1B5E20
    style Persistence fill:#FFF3E0,stroke:#F57F17,color:#E65100

Project structure

gispulse/
  gispulse/       # Package entry point, CLI (Typer)
  core/           # Domain models: Dataset, Rule, Job, Trigger, Scenario, Layer
  capabilities/   # Spatial operations: vector, raster, network, PostGIS SQL
  rules/          # Rule engine, loader, predicates, validation
  orchestration/  # Job runner, DAG graph executor, scenario runner
  persistence/    # DuckDB engine, PostGIS engine, tier gating, multi-format I/O
  adapters/
    http/         # FastAPI — datasets, rules, jobs, projects, OGC Features, MVT tiles, rate limiting
    mcp/          # FastMCP — LLM agent facade
    ogc/          # WFS/OGC API Features client with GeoParquet cache
    esb/          # Event bus — pg_notify triggers, circuit breaker, DLQ
  catalog/        # GIS data catalog (projections, basemaps, WMS/WFS flux, open data)
  portal/         # React 19 web app — visual pipeline editor, map, table views
  viewer/         # React 19 embedded spatial file viewer (deck.gl)
  sdk/            # Python SDK client library (httpx + pydantic)
  clients/
    qgis/         # QGIS plugin — dataset browser, jobs, OGC/PostGIS/MVT layers
    desktop/      # Standalone Tauri 2 app — React + MapLibre GL JS
    arcgis/       # ArcGIS Pro add-in — dockpanes + GP tools
  deploy/         # Production VPS: docker-compose, Caddy, Prometheus, Grafana, backups
  packaging/      # Homebrew formula, Scoop manifest, update scripts
  scripts/        # Installation scripts
  docs-site/      # VitePress documentation site
  tests/          # 175 test files — 3,800+ unit and integration tests
  examples/       # Sample datasets (GPKG), rules (JSON), and helper scripts
  docs/           # Architecture docs, API reference, ADRs

Capabilities

117 registered capabilities across 18 categories. Full reference: capabilities guide.

Vector — geometry & analysis

buffer union clip intersects spatial_join dissolve centroid envelope reproject simplify convex_hull concave_hull alpha_shape voronoi_polygons delaunay_triangulation symmetric_difference vector_diff grid_create hexgrid_create polygonize line_merge line_substring offset_curve snap_to_grid make_valid chaikin boundary assign_projection

Vector — overlay & combine

overlay_intersection overlay_union overlay_difference merge_layers

Vector — layer manipulation

force_geometry_type singleparts_to_multipart multipart_to_singleparts extract_holes extract_segments extract_vertices densify_vertices pivot unpivot classify_by_ring

Vector — attributes, filters, calculations

filter calculate normalize area_length nearest_neighbor spatial_aggregate spatial_weights od_matrix attribute_join add_z add_m attribute_logic_ops

Classification & styling

classify classify_categorical head_tail_breaks choropleth bivariate_choropleth graduated_size continuous_ramp kde_heatmap

Clustering & spatial statistics

cluster_kmeans cluster_dbscan cluster_hdbscan morans_i getis_ord_g

Temporal

Date/time-aware operations on attributes — windowing, deltas, period extraction.

Data quality & topology

topology_check make_valid duplicate_geometry attribute_validation completeness_check connectivity_check polygon_fix_gaps polygon_fix_overlaps polygon_remove_slivers polygon_snap_borders

Network (optional: pip install -e ".[network]")

shortest_path isochrone network_allocation mst network_snap_endpoints network_node_lines network_extend_dangles network_remove_pseudo_nodes network_remove_duplicates

Raster (optional: pip install -e ".[raster]")

zonal_stats change_detection raster_clip raster_merge raster_reproject ndvi

3D pointcloud (optional: pip install -e ".[pointcloud]")

pointcloud_load (LAS/LAZ) pointcloud_classify pointcloud_zonal_stats pointcloud_grid

SQL pass-through

postgis_sql — parameterised SQL on a PostGIS session (Pro tier, auth-gated with capability blocklist).

See the capabilities guide for parameter reference.

Supported formats

Read: GPKG, GeoJSON, Shapefile, FlatGeobuf, GML, KML, DXF, SQLite, FileGDB, GeoParquet, CSV/TSV, XLSX

Write: GPKG, GeoJSON, Shapefile, FlatGeobuf, GeoParquet, CSV

API adapters

Adapter	Protocol	Description
HTTP	REST / WebSocket	Full CRUD for datasets, rules, jobs, projects, scenarios, triggers. OGC API Features endpoint. MVT tile server. Prometheus /metrics with HTTP request metrics. Rate limiting (300/min, Redis-backed optional). Timing-safe auth, CSP headers, SSRF protection.
MCP	Model Context Protocol	LLM agent facade — list capabilities, create/validate rules, load datasets, run jobs.
OGC	WFS 2.0 / OGC API Features	Client with pagination and GeoParquet disk cache (1h TTL).
ESB	pg_notify	PostgreSQL trigger functions, event routing, circuit breaker, dead letter queue.

Client-server architecture

graph LR
    subgraph VPS["VPS · backend"]
        API["FastAPI :8001"]
        PG["PostGIS"]
        CADDY["Caddy :443"]
        PROM["Prometheus"]
        GRAF["Grafana :3000"]
    end

    subgraph Clients["Desktop / Web clients"]
        PORTAL["Portal · React 19"]
        QGIS["QGIS Plugin"]
        TAURI["Tauri App"]
        ARCGIS["ArcGIS Add-in"]
        SDK["Python SDK"]
    end

    CADDY -->|proxy| API
    API --> PG
    PROM -->|scrape| API
    GRAF --> PROM

    PORTAL -->|REST / WS| CADDY
    QGIS -->|SDK| CADDY
    TAURI -->|REST| CADDY
    ARCGIS -->|SDK| CADDY
    SDK -->|httpx| CADDY

    style VPS fill:#E3F2FD,stroke:#0D47A1,color:#0D47A1
    style Clients fill:#E8F5E9,stroke:#4CAF50,color:#1B5E20

GISPulse supports a split architecture: backend hosted on a VPS, frontends installed locally.

Python SDK

pip install ./sdk

from gispulse_sdk import GISPulseClient

with GISPulseClient("https://gispulse.example.com", api_key="sk-...") as client:
    datasets = client.datasets.list()
    job = client.jobs.run_and_wait(JobCreate(name="buffer"))
    ogc_url = client.ogc.items_url("parcels")
    tiles = client.tiles_url("parcels")

10 endpoint modules: datasets, projects, rules, triggers, jobs, scenarios, sessions, ogc, catalog, capabilities. Async client, WebSocket live events, SSE streaming.

QGIS plugin

cd clients/qgis && python build_plugin.py    # -> gispulse_qgis.zip
# Install via QGIS > Plugin Manager > Install from ZIP

• Connection dialog (server URL + API key, test button)
• Dataset browser dock with 3 loading modes: OGC API Features, PostGIS session, MVT tiles
• Job execution dock with result auto-loading as QGIS layer
• All SDK calls run in QThread (non-blocking UI)

Standalone desktop app (Tauri)

cd clients/desktop
npm install && npm run tauri:dev         # dev mode
npm run tauri:build                      # .msi / .dmg / .AppImage

React 19 + Tauri 2 + MapLibre GL JS + Tailwind CSS 4. Connection setup with splash screen, dataset browser with drag-and-drop import, job panel, MVT tile map.

ArcGIS Pro add-in

cd clients/arcgis && python makeaddin.py   # -> gispulse_arcgis.esriaddin

• Dockpanes for datasets and jobs
• 3 geoprocessing tools: Upload Dataset, Run Job, Run Scenario (usable in Model Builder)
• Native OGC API Features + PostGIS session layer loading

Data flow — rules pipeline

flowchart LR
    IN["Input file\nGPKG, GeoJSON, SHP ..."]
    IO1["I/O Reader\npyogrio"]
    DUCK["DuckDB Session\nspatial SQL"]
    RULES["Rules Engine"]
    CAP1["filter"]
    CAP2["buffer"]
    CAP3["spatial_join"]
    IO2["I/O Writer"]
    OUT["Output file\nGPKG, GeoJSON, FGB ..."]

    IN --> IO1 --> DUCK --> RULES
    RULES --> CAP1 --> CAP2 --> CAP3
    CAP3 --> IO2 --> OUT

    style RULES fill:#E8F5E9,stroke:#4CAF50,color:#1B5E20
    style DUCK fill:#E3F2FD,stroke:#0D47A1,color:#0D47A1

Portal

React 19 web application (pgAdmin-style — FastAPI backend + SPA, runs via gispulse portal).

Stack: React 19, TypeScript, Vite, Tailwind CSS 4, shadcn/ui, Zustand, TanStack Query, MapLibre GL JS, XyFlow/ReactFlow v12.

Workspaces

5 workspaces via React Router (/projects/:projectId/:workspace):

Workspace	Description
Explorer	Dataset/layer tree, rules, triggers panels
Map	MapLibre GL map with SQL console and feature inspector
Workflows	Visual DAG pipeline editor (ReactFlow)
Catalog	GIS data catalog with domain/type filtering
Data	Tabular feature exploration with schema browser

Key features

• Node editor — visual DAG pipeline builder, 9 node types, NodePalette with 6 categories, inline inspector, "Run this node" / "Save as Rule" actions
• Map view — MapLibre GL, layer tree (visibility, color picker, groups), basemap grid (OSM/CARTO/IGN), bookmarks, legend, cursor coordinates
• Command palette — Ctrl+K fuzzy search across datasets/rules/layers
• Dark mode — full dark/light toggle, persisted, respects prefers-color-scheme
• Scenarios panel — 4 tabs (Flows/Rules/Triggers/History) with dirty-state confirmation
• Catalog workspace — domain/filter sidebar, card grid, metadata inspector, favorites
• Trigger builder — cron, attribute predicate, DML triggers with spatial operations
• Upload/import — drag-and-drop or URL, multi-layer GPKG/GeoJSON/Shapefile
• Export — multi-layer GPKG with QGIS-compatible QML styles
• Keyboard shortcuts — workspace nav (1-5), command palette (Ctrl+K), inspector (Ctrl+I)
• Design system — oklch neutral palette + blue accent, Geist Variable font, CSS custom properties

Portal API (/api/portal/*): upload, features (bbox/limit/simplify), import-url, export-gpkg, rename, delete, capabilities, feature update, SQL execution, relation browsing.

Telemetry

GISPulse collects anonymous, opt-in telemetry. No file paths, data content, IP addresses, or dataset names are ever collected. Only aggregated usage signals (engine mode, capability names, command duration, OS/arch).

gispulse telemetry --status    # Check current setting
gispulse telemetry --disable   # Opt out
GISPULSE_TELEMETRY=0           # Disable via env var (CI-friendly)

Project status

Module	Status	Notes
Core models	Done	Dataset, Layer, Rule, Job, Artifact, Scenario, Trigger
Vector capabilities	Done	117 capabilities across 18 categories (geometry, overlay, attributes, classification, clustering, topology, temporal, 3D pointcloud, network), registry + strategy pattern
Rules engine	Done	JSON rules, sequential execution, predicates
DuckDB session	Done	<100ms startup, GPKG native
Multi-format I/O	Done	13+ read formats, pyogrio
Tier gating	Done	Community / Pro / Enterprise, license key validation
Rate limiting	Done	300/min default, Redis-backed optional
CLI	Done	14 commands: init, run, validate, info, layers, formats, capabilities, serve, portal, doctor, engine, jobs, update, telemetry
Portal UI	Done	React 19, 5 workspaces, node editor, map, catalog, dark mode
Portal API	Done	FastAPI, 27 routers, 100+ endpoints
Python SDK	Done	httpx + pydantic, 10 endpoint modules, async client, WebSocket/SSE
QGIS plugin	Done	Dataset browser, jobs, OGC/PostGIS/MVT layer factories
Tauri desktop app	Done	Tauri 2 + React 19 + MapLibre GL JS, splash screen, file import
ArcGIS Pro add-in	Done	Dockpanes, 3 GP tools
PostGIS + ESB	Done	PostGIS adapter, pg_notify triggers, circuit breaker, DLQ
VPS deploy	Done	Caddy TLS, Prometheus, Grafana, pg-backup
Viewer	Done	deck.gl spatial viewer via gispulse serve
Telemetry	Done	Opt-in, privacy-first, non-blocking
Packaging	Done	Homebrew, Scoop, install script, release workflow
Docs site	Done	VitePress, local search, edit links
Tests	Done	175 test files, 3,800+ tests, 72% overall coverage (85% router coverage)
Security	Done	Timing-safe auth, CSP headers, SSRF protection, rate limiting, RBAC
Observability	Done	Prometheus /metrics, structlog, trace IDs, Grafana dashboards
MCP facade	Planned	FastMCP
Raster/network	Planned	Optional extras

Documentation

Live docs: https://imagodata.github.io/gispulse/

Covers installation, CLI reference, rules syntax, capabilities, supported formats, engines, deployment, REST API, SDK, and plugin development.

# Run docs locally
cd docs-site
npm install && npm run dev    # Local dev server
npm run build                 # Static site generation

Built with VitePress.

Development

pip install -e ".[dev]"     # Install with dev dependencies
make test                   # Full test suite (3,800+ tests)
make lint                   # ruff check
make format                 # ruff format
pre-commit install          # Install git hooks (ruff, secret detection)

Codebase stats

Language	Files	Lines
Python	~470	~105k
TypeScript/React	~274	~51k
Tests	175 files	3,800+ tests

Contributing

See CONTRIBUTING.md. Python 3.10+, PEP 8 via ruff, type hints, tests required.

Related projects

• FilterMate — spatial preparation, filtering, and export capabilities
• FibreFlow — FTTH design and calculation pipelines

License

This project is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later).