geoai-vlm 0.1.0

Geospatial Vision-Language Model analysis for street-level imagery. Download Mapillary images by location and generate structured descriptions using VLMs.

GeoAI-VLM

Geospatial Vision-Language Model analysis for street-level imagery.

GeoAI-VLM combines ZenSVI's Mapillary downloading capabilities with Vision-Language Models (VLMs) to generate structured descriptions of street-level images. It's designed for urban analytics, spatial clustering, and GeoAI research.

Features

• 🗺️ Geospatial Queries: Point, line, polygon, and bounding box queries with automatic buffering
• 📸 Mapillary Integration: Download street-level imagery via ZenSVI
• 🤖 VLM Analysis: Generate structured descriptions using Qwen-VL, LLaVA, and other models
• 📊 GeoParquet Output: Native geometry columns for seamless GIS integration
• 📏 Distance Calculations: Automatic distance-to-query computation using haversine
• ⚡ High Performance: VLLM backend for fast batch inference (Transformers fallback available)
• 🔄 Resume Support: Skip already-processed images for incremental workflows

Requirements

• Python 3.9 or higher
• CUDA-compatible GPU (recommended for VLM inference)
• Mapillary API key for downloading street-level imagery

Installation

Option 1: Install from PyPI (when published)

pip install geoai-vlm -c https://raw.githubusercontent.com/yunusserhat/geoai-vlm/main/constraints.txt

Option 2: Install from GitHub

Using uv (recommended, fastest)

# Clone the repository
git clone https://github.com/yunusserhat/geoai-vlm.git
cd geoai-vlm

# Create virtual environment and install
uv venv
source .venv/bin/activate  # Linux/macOS
# or: .venv\Scripts\activate  # Windows

uv pip install .

# For development
uv pip install -e ".[dev]"

Using pip + venv

# Clone the repository
git clone https://github.com/yunusserhat/geoai-vlm.git
cd geoai-vlm

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # Linux/macOS
# or: venv\Scripts\activate  # Windows

# Install with constraints (required for dependency resolution)
pip install . -c constraints.txt

# For development (includes testing tools)
pip install -e ".[dev]" -c constraints.txt

Using conda

# Clone the repository
git clone https://github.com/yunusserhat/geoai-vlm.git
cd geoai-vlm

# Create and activate conda environment
conda create -n geoai-vlm python=3.11 -y
conda activate geoai-vlm

# Install with constraints (required for dependency resolution)
pip install . -c constraints.txt

# For development
pip install -e ".[dev]" -c constraints.txt

Verify Installation

python -c "import geoai_vlm; print('GeoAI-VLM installed successfully!')"

Quick Start

Basic Usage

from geoai_vlm import describe_place

# Describe images from a place name
results = describe_place(
    place_name="Sultanahmet, Istanbul",
    mly_api_key="YOUR_MAPILLARY_API_KEY",
    buffer_m=100,
    output_path="sultanahmet_descriptions.parquet"
)

print(results.head())

Point Query with Distance

from geoai_vlm import describe_point

# Query images near a specific coordinate
results = describe_point(
    lat=41.0082,
    lon=28.9784,
    buffer_m=50,
    mly_api_key="YOUR_API_KEY",
    output_path="hagia_sophia.parquet"
)

# Results include distance_to_query_m column
print(results[['image_id', 'distance_to_query_m', 'scene_narrative']].head())

Line Query (Street/Route Analysis)

from geoai_vlm import describe_line
from shapely.geometry import LineString

# Analyze images along a street
street_line = LineString([
    (28.9700, 41.0100),  # Start point (lon, lat)
    (28.9750, 41.0120),  # Midpoint
    (28.9800, 41.0080),  # End point
])

results = describe_line(
    geometry=street_line,
    buffer_m=25,
    mly_api_key="YOUR_API_KEY"
)

# Results include distance_to_line_m and distance_along_line_m

Bounding Box Query

from geoai_vlm import describe_bbox

results = describe_bbox(
    minx=28.970, miny=41.005,
    maxx=28.985, maxy=41.015,
    mly_api_key="YOUR_API_KEY",
    model_name="Qwen/Qwen3-VL-2B-Instruct"
)

Custom Prompts

from geoai_vlm import ImageDescriber, describe_place

# Use custom system/user prompts
custom_system = """You are an urban safety analyst. Describe safety-relevant features."""
custom_user = """Analyze this street image for: lighting, visibility, foot traffic, escape routes."""

results = describe_place(
    query="Fatih, Istanbul",
    mly_api_key="YOUR_API_KEY",
    system_prompt=custom_system,
    user_prompt=custom_user,
    output_path="safety_analysis.parquet"
)

Using Different Backends

from geoai_vlm import ImageDescriber

# VLLM backend (default, fastest)
describer = ImageDescriber(
    model_name="Qwen/Qwen3-VL-2B-Instruct",
    backend="vllm",
    gpu_memory_utilization=0.8
)

# Transformers backend (fallback)
describer = ImageDescriber(
    model_name="Qwen/Qwen3-VL-2B-Instruct",
    backend="transformers",
    device="cuda"
)

# Describe images
results = describer.describe(
    image_dir="./my_images",
    output_path="descriptions.parquet",
    batch_size=8
)

Output Schema

The default GeoAI schema extracts structured urban features:

{
    "scene_narrative": "80-120 word description of the urban scene",
    "land_use_character": {"primary": "commercial", "intensity": "high"},
    "urban_morphology": {"street_type": "pedestrian", "enclosure_ratio": "high"},
    "streetscape_elements": {"sidewalk_quality": "good", "street_trees": "moderate"},
    "mobility_infrastructure": {"modes_visible": ["pedestrian", "bicycle"]},
    "place_character": {"dominant_activity": "shopping", "human_presence": "crowded"},
    "environmental_quality": {"greenery_coverage": "moderate", "cleanliness": "good"},
    "semantic_tags": ["historic", "tourist", "commercial", "pedestrian", "busy"]
}

GeoParquet Output

Results are saved as GeoParquet with native geometry:

import geopandas as gpd

# Load results
gdf = gpd.read_parquet("results.parquet")

# Native geometry column preserved
print(gdf.geometry)  # POINT geometries
print(gdf.crs)       # EPSG:4326

# Easy GIS operations
gdf.to_file("results.geojson", driver="GeoJSON")
gdf.explore()  # Interactive map in Jupyter

Requirements

• Python 3.9+
• Mapillary API key (get one here)
• GPU recommended for VLM inference

Dependencies

• Core: geopandas, pandas, shapely, pyarrow, haversine
• Downloading: zensvi (Mapillary integration)
• VLM (choose one):
  • VLLM + qwen-vl-utils (recommended)
  • Transformers + torch + accelerate

License

MIT License - see LICENSE for details.

Citation

If you use GeoAI-VLM in your research, please cite:

@software{geoai_vlm,
  title = {GeoAI-VLM: Geospatial Vision-Language Model Analysis},
  year = {2026},
  url = {https://github.com/geoai-research/geoai-vlm}
}

Acknowledgments

• ZenSVI for Mapillary integration
• Qwen-VL for vision-language models
• VLLM for high-performance inference