GeoSpark

Verifiable spatial reasoning for any LLM.

Distances, topology and CRS computed by a geometry engine, not guessed by a language model. Open source, runs anywhere, works with any model that can call a tool.

GeoSpark gives AI models tools that compute spatial answers instead of estimating them: geodesic distances, exact topology, official datum and height conversions, geocoding, elevation, routing, open map data and live environmental data. Every answer carries its sources, units and caveats, and says when it can't be answered rather than guessing.

How it has changed, and why

GeoSpark is tested against the models people actually use, and it changes as they do.

1. The first benchmark (GeoSpark Bench v2). Across 9 models of the time, small open-weight models (3.8B–9B) got numeric distance questions wrong about 95% of the time, and frontier APIs ranged from 25% to 95% correct. With GeoSpark's tools, every model that made valid tool calls answered 76–96% correctly; a 7B model on a CPU went from 5% to 76%.
2. The models improved. Re-checked in October 2026, Claude Opus 5.5 and GPT-6.1 answer those distance and topology questions well on their own, and with GeoSpark's tools a 4B open model on a workstation answers them as well as they do. Distances between two given points are no longer the gap.
3. We looked for the next gap, with practitioners' problems in mind. Both frontier models apply datum shifts, plate motion and map projections to the millimetre, but anything that needs a national grid file defeated them: converting a GNSS or drone height to AHD, ODN, NZVD2016 or NAVD88, or using Britain's OSTN15. Answering directly (even with their own code sandboxes, which have no internet), GPT-6.1 declined and Opus 5.5 estimated, up to 9.9 m off. GeoSpark now does these conversions with the official grids. (Writing a script for you to run online, both frontier models got them right; smaller local models did not.)
4. We checked our own answers, not just the models'. Matching PROJ only proves a model used the tool, so GeoSpark's answers were compared with Geoscience Australia, Ordnance Survey, LINZ and the US National Geodetic Survey: agreement within millimetres, with one disclosed exception. The same audit corrected overclaims on our website and fixed what the edge cases broke.

Every step's questions, scripts and results are in the repository, so anyone can re-run them (benchmark results, October 2026 re-check, agency checks).

What GeoSpark is now

• A live site, API and hosted MCP server at geospark.terrascout.app: ask questions, draw reachable areas, explore open map data, convert datums and heights.
• Verifiable answers: sources and licences, units, caveats (an ambiguous place name, a DEM's accuracy, a local height datum), and status ok, partial or cannot_answer. Coordinates a model types from memory, or a conversion to a different system than the question asks for, mark the answer partial.
• Self-hostable on one server with a local model at no API cost: routing (Valhalla), an OpenStreetMap copy (Overpass) and the official grids, all under one data folder.
• Open: Apache-2.0, 1,190+ offline tests, and benchmarks you can re-run.

Try it

No install: the website. geospark.terrascout.app lets you ask questions, draw reachable areas and explore Overture Maps data around any place. Every result shows its sources, caveats and units, and downloads as GeoJSON.

No install: the hosted API (open, no key needed; API reference)

curl -X POST https://geospark.terrascout.app/api/v1/distance \
  -H "Content-Type: application/json" \
  -d '{"lat_a": 48.8584, "lon_a": 2.2945, "lat_b": 48.8606, "lon_b": 2.3376}'
# {"distance_m": 3172.01, "distance_km": 3.172, "note": "Geodesic distance on WGS84 ellipsoid"}

In Claude or any MCP client, with no install: the hosted MCP server

Add a custom connector with this URL (in Claude: Settings → Connectors → Add custom connector):

https://geospark.terrascout.app/mcp

It is open, with no key, and has the same seventeen tools as the local server below (which adds three for your own files), with the same rate limits as the hosted API. It never reads files; for your own files, run the server locally.

In Claude Desktop or any MCP client, running on your computer

pip install "geospark-ai[mcp]"

Add GeoSpark to your MCP client's config. For Claude Desktop that's ~/Library/Application Support/Claude/claude_desktop_config.json on macOS, or %APPDATA%\Claude\claude_desktop_config.json on Windows:

{ "mcpServers": { "geospark": { "command": "geospark-mcp" } } }

That gives the model fifteen read-only tools. Computation: calculate_distance (between place names, which the server geocodes, or coordinates), check_spatial_relationship and spatial_operation (buffer, area, length, centroid, convex hull). Places and data: geocode, reverse_geocode, get_elevation, route, get_weather, get_air_quality, get_active_fires and overture_features. Analysis: isochrone, travel_time_matrix, location_report and select_site.

Results come back as structured content with output schemas, usually GeoJSON. They carry provenance (source, licence, attribution), units and warnings. When GeoSpark can't answer reliably (invalid geometry, unknown place, a data source down), the tool returns an error that says why, so the model can tell the user instead of estimating. The server uses MCP SDK 2.x (protocol 2026-07-28) and still works with clients on older protocol versions.

As a Python library

pip install geospark-ai              # core
pip install "geospark-ai[api,mcp]"   # + REST server and MCP server
pip install "geospark-ai[all]"       # everything

Run your own server with Docker

docker run -p 8000:8000 ghcr.io/maz2580/geospark:latest
# then open http://localhost:8000

That runs in development mode, where operator routes are open: fine on your own machine. For a public server, add -e GEOSPARK_ENV=production -e GEOSPARK_ADMIN_TOKEN=<secret> (see Self-hosting).

What it does

Planned, not built yet: slope, viewshed, raster band math and change detection. These operations exist in the protocol, but today they return a clear "not implemented yet" error rather than an answer. See the roadmap.

Quick start

Ground-truth geometry

from geospark.engine.spatial_reasoner import SpatialReasoner

park = {"type": "Polygon", "coordinates": [[[2.29, 48.85], [2.30, 48.85], [2.30, 48.86], [2.29, 48.86], [2.29, 48.85]]]}
point = {"type": "Point", "coordinates": [2.295, 48.855]}  # GeoJSON order: [lon, lat]

SpatialReasoner.check_relationship(park, point, "contains")  # True

SpatialReasoner.calculate_distance(
    {"type": "Point", "coordinates": [2.2945, 48.8584]},  # Eiffel Tower
    {"type": "Point", "coordinates": [2.3376, 48.8606]},  # Louvre
)  # 3172.01 (metres, geodesic)

Queries through the engine

from geospark import Engine
from geospark.protocol import SpatialOperation, SpatialQuery

engine = Engine(tools=["geocoder", "terrain"])

place = engine.execute(SpatialQuery(operation=SpatialOperation.GEOCODE,
                                    metadata={"query": "Federation Square, Melbourne"}))
print(place.features[0].geometry)  # {'type': 'Point', 'coordinates': [144.969..., -37.817...]}

# Inputs in another CRS are transformed to WGS84 first, including the second geometry
buffered = engine.execute(SpatialQuery(operation=SpatialOperation.BUFFER,
                                       geometry={"type": "Point", "coordinates": [2.2945, 48.8584]},
                                       radius_m=100))
print(buffered.features[0].properties["area_sq_m"])  # ≈ 31,365

Every answer is checkable, and opens in QGIS

result = engine.execute(SpatialQuery(operation=SpatialOperation.GEOCODE, metadata={"query": "Springfield"}))
result.status        # "ok" | "partial" | "cannot_answer"
result.warnings      # ["Ambiguous place name: 'Springfield, …, Illinois…'; '…Massachusetts…' …"]
result.provenance    # source, licence (ODbL-1.0), attribution, retrieved_at, method
result.spatial_context.units  # {"distance_m": "metre", ...}

import json
json.dump(result.to_geojson(), open("springfield.geojson", "w"))  # RFC 7946, drag into QGIS

GeoSpark refuses rather than guesses. A self-intersecting polygon, out-of-range coordinates or a likely [lat, lon] swap gets cannot_answer with the reason, not a confident wrong number. From the API, add ?format=geojson (or Accept: application/geo+json). From the CLI, add --geojson out.geojson to geocode, elevation, query or report. A location report becomes a map layer: the place plus every nearby amenity, with distances.

Open map data with no API key: Overture Maps

from geospark.tools.overture.client import query   # pip install "geospark-ai[overture]"

query("divisions", 48.8606, 2.3376)   # the Louvre is in: France › Île-de-France › Paris › Paris 1er Arrondissement
query("places", -37.8180, 144.9691, radius_m=200, category="cafe")  # nearest first, geodesic metres
query("buildings", -37.8180, 144.9691, radius_m=150)  # footprints, height, geodesic footprint area
query("transportation", -37.8180, 144.9691, radius_m=150)  # road/path segments, geodesic length

GeoSpark queries Overture Maps GeoParquet directly on S3 with DuckDB. It uses the release's STAC index to read only the files covering your area, so there's no download and no key. Results report the Overture release and the licence of the records returned. The same data is available from the CLI (geospark overture places -37.818 144.969 --category cafe), the REST API (POST /api/v1/overture/{theme}) and the MCP tool overture_features.

Official datum and height conversions

from geospark import geodesy

# A drone's GNSS height at Federation Square, in the Australian Height Datum (AUSGeoid2020)
geodesy.convert_height(-37.8177, 144.9691, 40.0, "ellipsoidal", "AHD")
# {'height_m': 35.2574, 'separation_m': 4.7426, 'geoid_model': 'AUSGeoid2020', 'accuracy_m': 0.15, ...}

# GDA94 / MGA zone 55 -> GDA2020 / MGA zone 55 (a 1.55 m shift), with the EPSG operation used
geodesy.transform("EPSG:28355", "EPSG:7855", [[321242.045, 5812466.767]]).as_dict()

In our October 2026 test of two frontier models, both applied datum parameters and plate motion correctly but could not use the national grids: GPT-6.1 declined every ellipsoid-to-AHD height ("I cannot access the AUSGeoid2020 grid") and Claude Opus 5.5 estimated them 0.2-5.3 m off. PROJ itself falls back silently when a grid is missing (AHD becomes the ellipsoidal height, 4.7 m off in Melbourne; British National Grid moves about 124 m); GeoSpark refuses instead.

Checked against the agencies themselves, not only against PROJ (details): Geoscience Australia's calculators (GDA94 to GDA2020 and ATRF2014 to GDA2020 within 0.1 mm at eight points; AUSGeoid2020 heights within 2 mm in three cities), Ordnance Survey's 40 OSTN15/OSGM15 test points (grid coordinates within 8 mm, heights within 2 mm), LINZ's NZVD2016 example points (within their 1 cm rounding) and NGS's GEOID18 service (1 mm); at scale, GDA94 to GDA2020 agrees with Victoria's surveyed marks within 7 mm (416 marks) and AUSGeoid2020 with spirit-levelled AHD to 6 mm median (99 marks). Limits to know: AHD is refused outside mainland Australia and Tasmania; OSGM15 heights on Scilly, the Isle of Man, the Outer Hebrides, Orkney and Shetland are labelled with their local datum, not ODN; and NAD27 to NAD83 uses the original NADCON, about 0.27 m from NGS's current NADCON 5 in San Francisco (within NADCON 5's own stated uncertainty). The accuracy given with each result is the one EPSG states for the operation, not a measurement.

Also as POST /api/v1/transform, POST /api/v1/height and the MCP tools transform_coordinates and convert_height. Grids are cached in $GEOSPARK_DATA_DIR/proj; set GEOSPARK_PROJ_NETWORK=false to never download.

Your own data

pip install "geospark-ai[io]"
geospark layer info suburbs.gpkg                            # features, fields, CRS, geodesic area
geospark layer count-within crashes.csv suburbs.gpkg --geojson counts.geojson
geospark layer nearest schools.shp hospitals.geojson
geospark layer intersect parcels.gpkg flood_zones.geojson

Formats: GeoJSON, GeoPackage, Shapefile (or a .zip), GeoParquet, CSV with longitude/latitude columns, FlatGeobuf, KML, GML, File Geodatabase. The CRS comes from the file, not a guess:

• a projected file (e.g. MGA zone 55) is reprojected, and its original CRS is reported;
• a Shapefile without .prj is refused until you pass --crs EPSG:28355;
• a CSV has no CRS: longitude/latitude columns are read as WGS 84 and the result says so (pass --crs for GDA94 or another datum); x/y columns are refused until you name the CRS;
• invalid shapes are refused until you pass --repair.

Check a file before you use it, or find out why two layers disagree:

geospark layer check pipes.csv --crs EPSG:7855                          # CRS, datum and height caveats
geospark layer check pipes.csv --crs EPSG:7855 --against parcels.csv --against-crs EPSG:7855
# problem: pipes.csv sits 1.55 m from parcels.csv, matching the official GDA94 to GDA2020 shift here.
#          pipes.csv holds GDA94 coordinates but is treated as the same datum as parcels.csv.

check looks for the silent errors: no CRS declared, projected numbers labelled as degrees (or the reverse), swapped latitude/longitude, points outside the CRS's zone, Australian data labelled WGS 84, and heights that don't say whether they are ellipsoidal or AHD. With --against it measures the offset to a reference layer and names the cause only when the numbers match one: GDA94 data labelled GDA2020 (checked against the official shift at that place), the wrong MGA zone, feet instead of metres, swapped axes, a plate-motion epoch, or ellipsoidal against AHD heights. Anything else is reported as an unexplained offset. Tried on 2,000 of Victoria's published survey marks, it named real GDA94 numbers labelled GDA2020 (1.55 m, scatter 3 cm), a wrong zone and ellipsoidal against AHD heights, and found nothing wrong when they were labelled correctly (6 mm). The exit code is 2 when it finds a problem, so it fits in a pipeline; in Python, geospark.io.check.check_layer and compare_layers; for Claude, the local MCP tool check_dataset.

Results carry each file's SHA-256, so an analysis can be reproduced against exactly that data. In Python, use geospark.io.layers (load_layer, count_within, nearest, intersect). The local MCP server (geospark-mcp) offers the same as layer_* tools; the hosted /mcp endpoint never reads files.

Reproducible analyses

geospark record report "Federation Square, Melbourne" --out fedsq.json
geospark replay fedsq.json
# REPRODUCED: Same result as recorded.
# ... or DATA CHANGED: openstreetmap-overpass returned different data for the same request (step 6 ...)
# ... or CODE CHANGED: versions changed: {'shapely': ('2.0.0', '2.2.0')}

A run record lists:

• the environment: GeoSpark, Python, pyproj/PROJ and Shapely/GEOS versions
• every data request (Nominatim, Overpass, Overture, Valhalla, weather, elevation) and every engine computation, each with a fingerprint of its input and output
• a fingerprint of the final result

geospark replay reruns the analysis and says whether it reproduced. If not, it says which source returned different data, or that the code or a library version changed. In Python, use geospark.runs.record_run(...) and replay(...).

Natural-language questions

result = engine.ask("How far is the Eiffel Tower from Big Ben?")
print(result.spatial_context.summary)
print(result.sources)  # the model, then each tool it called

ask uses a local Ollama model first and falls back to free OpenRouter models if OPENROUTER_API_KEY is set.

Agents

from geospark.agents import AgentCoordinator, GeoAgent, SiteSelector, SpatialReport

GeoAgent().run("Find all hospitals within 2km of the Eiffel Tower").findings["nearby"]

report = SpatialReport().analyze("Federation Square, Melbourne", radius_m=1000)
print(report.weather, report.accessibility, report.data_gaps)

best = SiteSelector().find(within="Zurich", near=["hospital", "school"], facility_type="pharmacy").best

AgentCoordinator().run("Find the best location for a cafe in Melbourne CBD near schools")

Agents use an LLM to read the goal. Without one, a rule-based parser still handles requests like the one above: multi-word place names, radii such as "2km", and amenity types.

Memory, context and flows

from geospark.memory import SpatialIntelligence

intel = SpatialIntelligence()
intel.remember_fact("Eiffel Tower is at 48.8584 N, 2.2945 E", source="user")
intel.remember_episode("PM2.5 was 120 in Delhi", importance=0.8, source="tool:air_quality")
intel.recall("Where is the Eiffel Tower?", limit=5)  # semantic with Ollama embeddings, keyword match without
intel.find_contradictions()

from geospark.flows import ChatFlowSession, FlowRunner, make_ollama_chat_fn

session = ChatFlowSession(llm_fn=make_ollama_chat_fn("qwen2.5:7b"))
result = session.run("Geocode Valencia, Spain then check its elevation")
if result.flow:
    FlowRunner().run(result.flow)  # a validated DAG, built one tool call at a time

Command line

geospark distance -37.8136 144.9631 -33.8688 151.2093   # Melbourne → Sydney: 713.86 km
geospark check contains '<geojson A>' '<geojson B>'
geospark geocode "Tokyo Tower, Japan"
geospark elevation -37.8177 144.9691
geospark isochrone -37.8180 144.9691 --minutes 5,10,15 --mode walk
geospark ask "Is Tokyo closer to Seoul or Beijing?"

geospark agent "Find all parks within 2km of Big Ben"
geospark report "Federation Square, Melbourne"
geospark site-select --within "Paris" --near "school,hospital" --facility pharmacy
geospark multi-agent "Analyze Federation Square" --stream

geospark data weather "Melbourne, Australia"
geospark data air-quality "Delhi"        # needs OPENAQ_API_KEY
geospark data fires "Amazon Rainforest"  # needs FIRMS_MAP_KEY

geospark flow list
geospark flow build "Geocode Valencia then check its elevation" --run
geospark info                            # versions and optional extras

Benchmark results

GeoSpark Bench v2 ran 9 models under three protocols: the bare question, step-by-step reasoning (CoT), and the question with GeoSpark's MCP tools attached. The models were five open-weight ≤9B (Qwen 2.5 7B, Llama 3.1 8B, Gemma 2 9B, Mistral 7B, Phi-3.5 3.8B), gpt-oss 20B, and three frontier APIs (GPT-5.4, Claude Sonnet 4.5, Gemini 2.5 Pro). Intervals are Wilson 95%, and the per-call cost ledger (~$41) is checked in. The full analysis is in a manuscript currently under peer review.

Numeric distance (distance_absolute, n = 80 per model; correct = within 10% of the pyproj.Geod.inv() geodesic):

ᵃ The tools were available but not used properly: Llama mixed up km and m in the coordinates it got back, and Mistral answered in plain text. ᵇ Gemma's correct answers are well-known long-distance pairs, which is recall of training data, not computation. — means not evaluated with tools.

What we learned

• Tools are a leveller. The bare-LLM range spans 1–95%. With tools, every model that makes valid tool calls lands between 76% and 96%. Qwen gained 71 points; GPT-5.4 gained nothing, because it was already at 95%.
• Aggregate scores hide bias. Qwen's 56% on topology comes from saying "yes": it scores 93% on intersects (where the data skews true) and 3% on disjoint. GPT-5.4 scores 0% on touches. With GeoSpark, every predicate is 100%, because the engine computes it.
• Step-by-step prompting is not a universal fix. It helps some models and hurts others.
• Knowledge questions don't need tools. "Did the Amazon lose forest between 2015 and 2023?" is answered well at every scale without them.

Re-run it yourself with python -m geospark.bench run. The datasets regenerate identically from python -m geospark.bench.generate_datasets; see the datasets README for provenance, and the benchmark report for every table.

Re-check with current models (October 2026)

We keep testing as models improve. In October 2026 we re-ran a sample of GeoSpark Bench on today's frontier models and on open models served from a local workstation, and added a new test of datum and height conversions, where we found the next gap. Everything needed to re-run it (questions, the scripts that compute the official answers, the harness and a scored summary) is in docs/benchmarks/2026-10.

GeoSpark Bench sample (20 numeric-distance questions and 18 topology questions, 3 per predicate, coordinates given; distance correct within 10% of the pyproj geodesic, topology exact):

¹ One reply ran out of length while reasoning and gave no number. ² Most replies timed out on a busy workstation, so the run is not a fair score. ³ It never called the tools. Gemma 4 12B timed out the same way and is left out.

The finding has moved on since the first run: the newest frontier models now handle these questions on their own, and with tools a 4B model on a workstation answers them as well as they do.

Datum and height conversions (new; 32 questions in Australia, the UK, New Zealand and the US, with answers computed by PROJ's most accurate EPSG operations and the national grids (checked against Geoscience Australia's and NGS's own services where they offer one; see above): GDA94 to GDA2020, MGA zones, ITRF2014 to GDA2020 at an epoch, ellipsoidal heights to AHD, ODN, NZVD2016 and NAVD88, OSTN15, NAD27 to NAD83). Survey-grade means within 5 cm, or within the geoid model's stated accuracy for heights:

⁴ Two British National Grid answers ran past an 8,000-token reply limit while working by hand.

Because GeoSpark's tools also use PROJ, the with-tools column shows that the models used the tools correctly; whether PROJ's answers are right is the separate agency check above. For the two NAD27 questions the reference itself is only good to about 0.27 m (NGS's stated uncertainty), so "survey-grade" there means agreement with PROJ's NADCON, not centimetre truth.

Both models apply datum parameters, plate motion and map projections to the millimetre. What they can't do, answering directly, is read the national grids that heights and some datum conversions depend on: GPT-6.1 declines ("I cannot access the AUSGeoid2020 grid"), and Opus 5.5 estimates from memory. That is the gap GeoSpark's transform_coordinates and convert_height close.

Fairness checks (details): the gap holds with the providers' own code execution at high effort (their sandboxes have PROJ but no internet; GPT-6.1 declined all 17 grid questions, Opus 5.5 estimated with errors up to 9.9 m) and across three repeated runs. It does not hold when these frontier models write a script that you run with internet access: Opus 5.5 got all 8 everyday GIS scripting tasks right, AHD included, and GPT-6.1 got 7 and failed safely on the eighth. The silently wrong scripts came from the smaller local models (gpt-oss 20B printed the ellipsoidal height unchanged as "AHD"; Qwen 3.5 4B got 1 of 8). Not tested: web browsing. The whole October 2026 re-check cost US$8.46 in API calls.

Self-hosting

git clone https://github.com/Maz2580/geospark && cd geospark
cp .env.example .env     # set GEOSPARK_ENV=production and GEOSPARK_ADMIN_TOKEN
docker compose -f docker-compose.prod.yml up -d

The hosted API is designed to stay open without a key, with guard rails:

• An admin token protects operator routes (audit, usage, flow storage). The LLM gateway needs it or a revocable partner key.
• Public callers can only choose the local model or free models, so nobody can spend your paid API credit.
• Each visitor gets a private, size-capped, auto-expiring sandbox for the memory and context demos.
• Rate limits use the real client IP, and LLM work has a concurrency cap.
• Inputs are bounded, and the audit log stores hashed IPs.

All state lives under GEOSPARK_DATA_DIR. To lift Nominatim's public limits, point GEOSPARK_NOMINATIM_URL at your own server. Details are in deploy/DEPLOY.md and .env.example.

Roadmap

• v0.7 (released): verifiable answers.
  • Every result carries its source, licence, timestamp, CRS and units.
  • A "cannot answer" response instead of a guess.
  • Data-quality flags, such as geocoder confidence and DEM accuracy.
  • GeoJSON FeatureCollections that load straight into QGIS.
  • The MCP server updated to the 2026 spec, with structured GeoJSON output and a hosted HTTP endpoint.
• v0.8 (released): more data, more analysis.
  • Overture Maps buildings, places and roads through DuckDB, with no key.
  • Real isochrones and travel-time matrices.
  • Bring your own GeoJSON, GeoPackage or GeoParquet.
  • Reproducible run manifests.
  • A redesigned website with a Proof panel (sources, caveats, units, GeoJSON download) on every result, and Ask answers that never use coordinates the model typed from memory.
• Ongoing: external benchmarks (GPSBench, GeoBenchX), a QGIS bridge, and more MCP tools.

Data attribution

GeoSpark computes geometry locally, but it fetches data from public services. Results built on them keep the providers' licences, so please credit them when you publish:

• OpenStreetMap (geocoding, amenities, map tiles): © OpenStreetMap contributors, ODbL
• Copernicus DEM GLO-30 (elevation): © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018, provided under COPERNICUS by the European Union and ESA
• Open-Meteo (weather): CC BY 4.0
• OpenAQ (air quality) and NASA FIRMS (active fires): see each provider's terms. Air-quality results include each station's licence.

Elevation results include an attribution field you can copy directly. The full list is in NOTICE.

Development

git clone https://github.com/Maz2580/geospark && cd geospark
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev,api,mcp]"
pytest                       # 1003 tests; offline, never touch ~/.geospark
ruff check geospark tests

Contributions are welcome. See CONTRIBUTING.md, and report security issues privately as described in SECURITY.md. The architecture overview is in docs/ARCHITECTURE.md, and release notes are in CHANGELOG.md.

Citing

If GeoSpark or GeoSpark Bench helps your research, please cite it. GitHub's "Cite this repository" button uses CITATION.cff.

Author

Created by Mazdak Ghasemi Tootkaboni (University of Melbourne) · ORCID 0000-0001-8084-5270 · GitHub @Maz2580

License

Apache 2.0. Copyright 2024-2026 Mazdak Ghasemi Tootkaboni.