pygeospy 0.2.0

Python GEOINT/OSINT library with Rust-accelerated core

pygeospy 🌍

Python GEOINT/OSINT library with a Rust-accelerated core. Given any image, coordinates, IP, or set of clues — produce a location.

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Install

pip install pygeospy

For optional heavy dependencies (vision, OCR, audio, etc.):

pip install "pygeospy[all]"         # everything
pip install "pygeospy[coords,exif]" # pick modules

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What makes pygeospy different?

Feature	pygeospy	Other OSINT tools
Rust core	✓ 10–100× faster batch math	Pure Python only
SAR module	✓ NASAR grids + ISRID profiles	Not available
Full pipeline	✓ analyze(anything) → coordinates	Module-only APIs
Acoustic analysis	✓ BirdNET + siren classification	Not available
Offline-first	✓ LLaVA/Ollama, zero API keys needed	Cloud-dependent

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Quick Start

import pygeospy

# Haversine distance (Rust-accelerated)
dist = pygeospy.coords.haversine(51.5, -0.1, 48.85, 2.35)
print(f"London → Paris: {dist:.1f} km")

# Shadow → latitude band
result = pygeospy.solar.latitude_band_from_shadow(
    shadow_ratio=2.5,       # shadow is 2.5× taller than object
    shadow_azimuth_deg=195, # shadow points south-southwest
)
print(f"Candidate latitude bands: {result.candidate_lat_bands}")
print(f"Season: {result.estimated_season}")

# EXIF extraction
exif = pygeospy.exif.extract("photo.jpg")
if exif.has_gps:
    print(f"GPS: {exif.coordinates}")

# Full pipeline analysis
result = pygeospy.pipeline.analyze(
    "mystery_photo.jpg",
    shadow_ratio=2.5,
    shadow_azimuth_deg=195,
    vision_backend="llava",  # offline, no API key needed
    export=True,             # saves HTML report, GeoJSON, KML, GPX
)
print(result.summary)

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Modules

v0.1 — Foundation

Module	Description	Backend
pygeospy.coords	Haversine, bearing, UTM, MGRS, bounding boxes, elevation API	Rust + Python
pygeospy.solar	Shadow → sun angle → latitude bands, sunrise/sunset	Rust + Python
pygeospy.exif	GPS, camera fingerprinting, forensic scrub detection, batch	Python
pygeospy.terrain	Slope, aspect, TRI, viewshed, elevation profile	Rust + Python
pygeospy.osm	Overpass queries, building footprints, road density	Python
pygeospy.geo	Nominatim geocoding, reverse geo, IP geolocation	Python
pygeospy.sar	NASAR grid, corridors, POA zones, urgency scoring	Rust + Python
pygeospy.export	Folium maps, HTML reports, GeoJSON/KML/GPX	Python

v0.2 — Visual Intelligence

Module	Description	Backend
pygeospy.visual	Infrastructure/sign/vegetation/vehicle clues, Claude/GPT-4V/LLaVA	Python
pygeospy.chronos	Shadow → time of day, vegetation → season, weather archives	Python
pygeospy.language	OCR, script detection (18 systems), sign geocoding	Python
pygeospy.network	IP/ASN, WiGLE BSSID, MAC OUI, email headers, crt.sh	Python
pygeospy.satellite	Sentinel-2 search, NDVI/EVI/MNDWI, change detection	Rust + Python
pygeospy.acoustic	BirdNET species → region, siren tones, Whisper language	Python
pygeospy.pipeline	Unified analyze() engine, parallel execution	Python

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CLI

# Full analysis
pygeospy analyze mystery_photo.jpg --shadow-ratio 2.5 --shadow-azimuth 195 --export

# Solar position
pygeospy solar position 51.5 -0.1 172 14.0

# Shadow → latitude bands
pygeospy solar from-shadow 2.5 195 --doy 172

# EXIF extraction
pygeospy exif extract photo.jpg

# Coordinate conversion
pygeospy coords convert 48.8566 2.3522 --fmt all

# Haversine
pygeospy coords haversine 51.5 -0.1 48.85 2.35

# SAR grid
pygeospy sar grid --lat 47.6 --lon -122.3 --radius 3.0 --cell 0.5 --out grid.geojson

# SAR urgency
pygeospy sar urgency --age 8 --medical --hours 6 --night

# IP analysis
pygeospy analyze --ip 8.8.8.8

# Cache management
pygeospy cache stats
pygeospy cache clear

---

Architecture

pygeospy/
├── _rustcore/               # Rust crate (PyO3, abi3)
│   └── src/
│       ├── lib.rs           # Module entry point
│       ├── coords.rs        # Haversine, bearing, UTM, bbox
│       ├── solar.rs         # Solar elevation/azimuth, shadow geometry
│       ├── terrain.rs       # Slope, aspect, TRI, viewshed
│       ├── sar.rs           # Grid generation, POA rings, urgency
│       └── raster.rs        # NDVI, EVI, pixel statistics, Otsu
├── pygeospy/                # Python package
│   ├── __init__.py
│   ├── _types.py            # GeoResult, Clue, LatLon, BoundingBox
│   ├── _utils.py            # Shared utilities, rate limiter
│   ├── _cache.py            # Disk cache with TTL
│   ├── coords.py            # Rust wrapper + elevation/timezone APIs
│   ├── solar.py             # Rust wrapper + GeoJSON export
│   ├── exif.py              # EXIF extraction and forensics
│   ├── terrain.py           # Rust wrapper + DEM download
│   ├── osm.py               # Overpass API queries
│   ├── geo.py               # Nominatim + IP lookup
│   ├── sar.py               # Rust wrapper + GPX export
│   ├── export.py            # Folium maps, HTML reports
│   ├── visual.py            # Vision model integration
│   ├── chronos.py           # Temporal analysis
│   ├── language.py          # OCR + linguistic analysis
│   ├── network.py           # IP/network OSINT
│   ├── satellite.py         # Sentinel-2 + spectral indices
│   ├── acoustic.py          # Audio geographic signals
│   ├── pipeline.py          # Unified analysis engine
│   └── cli.py               # Typer CLI
├── tests/
│   ├── test_coords.py
│   ├── test_solar.py
│   ├── test_sar.py
│   ├── test_terrain.py
│   └── test_pipeline.py
├── pyproject.toml
├── Makefile
└── README.md

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Example: Brick-Wall-to-Coordinates Pipeline

The classic GEOINT workflow, automated:

import pygeospy

# Step 1: Check EXIF
exif = pygeospy.exif.extract("brick_wall.jpg")
# → No GPS found, EXIF timestamp: 2024-06-15 14:23:00

# Step 2: Solar analysis from shadow
solar = pygeospy.solar.analyze_shadow(
    shadow_ratio=2.1,        # measured from image
    shadow_azimuth_deg=200,  # estimated from image
    timestamp_utc="2024-06-15T14:23:00Z",
)
# → Candidate bands: 35°N–55°N (northern summer afternoon)

# Step 3: Visual clues (offline with LLaVA)
pygeospy.visual.set_backend("llava")
clues = pygeospy.visual.extract_clues("brick_wall.jpg")
# → brick bond: English bond → Northern Europe / UK
# → mortar: white repointing → post-1950 UK
# → stone sill: grey limestone → Northern England / Scotland

# Step 4: OSM region narrowing
from pygeospy._types import BoundingBox
bb = BoundingBox(50, -5, 58, 2)  # England
arch = pygeospy.osm.architectural_tags(53.8, -1.5, radius_m=500)

# Step 5: Full pipeline
result = pygeospy.pipeline.analyze(
    "brick_wall.jpg",
    shadow_ratio=2.1,
    shadow_azimuth_deg=200,
    vision_backend="llava",
    export=True,
)
print(result.summary)
print(result.candidate_countries[:3])

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Building the Rust Core

The Rust core compiles to a single .pyd/.so file that works on Python 3.9+. If Rust is unavailable, all modules fall back to pure-Python automatically (with a RuntimeWarning at import).

# Prerequisites
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
pip install maturin

# Development build (fast, unoptimised)
maturin develop

# Release build (optimised)
maturin build --release

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Vision Model Backends

import pygeospy.visual as v

# Claude (best accuracy, requires API key)
v.set_backend("claude", api_key="sk-ant-...")

# GPT-4V (requires OpenAI API key)
v.set_backend("gpt4v", api_key="sk-...")

# LLaVA via Ollama — FULLY OFFLINE, no API key needed
# Install: https://ollama.ai  then: ollama pull llava:13b
v.set_backend("llava", base_url="http://localhost:11434", model="llava:13b")

# Rule-based only (no model)
v.set_backend("none")

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Optional API Keys

Service	Module	Required?	Notes
Anthropic Claude	visual	Optional	Best visual analysis
OpenAI GPT-4V	visual	Optional	Alternative
ip-api.com	geo, network	Optional	Free tier: 45 req/min
WiGLE	network	Optional	Wi-Fi BSSID lookup
What3Words	geo	Optional	W3W address conversion
Meteostat	chronos	Optional	Historical weather
Open-Topo-Data	coords, terrain	Free / no key	Elevation data

All core features work without any API keys.

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Testing

pip install pytest
PYTHONPATH=. pytest tests/ -v

71 tests, no network required.

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License

MIT © pygeospy contributors