GeoAI-VLM

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

GeoAI-VLM downloads street-level imagery from Mapillary (through ZenSVI) and describes it with open vision-language models (VLMs) -- any Hugging Face image-text-to-text model through Transformers or vLLM, or any model behind an OpenAI-compatible endpoint. It embeds images and descriptions (Qwen3-VL-Embedding or CLIP-family encoders) for semantic clustering, spatial autocorrelation analysis and vector search, estimates road slope (inspired by Vision2Slope), and turns image-level observations into street-segment indicators with explicit coverage, recency and evaluation tools for active mobility research. It is designed for GeoAI research.

Features

Core

• 🗺️ Geospatial Queries: Point, line, polygon, and bounding box queries with automatic buffering
• 📸 Mapillary Integration: Download street-level imagery via ZenSVI
• 🤖 Model-agnostic VLM analysis: Qwen-VL, SmolVLM, LLaVA-OneVision, InternVL and other image-text-to-text models, locally or through an OpenAI-compatible server (vLLM server, TGI, SGLang, Ollama, LM Studio)
• 🧾 Provenance on every record: backend and its version, model revision, generation settings, how the system prompt was delivered and whether decoding was schema-constrained
• 📊 GeoParquet Output: Native geometry columns for seamless GIS integration
• 📏 Distance Calculations: Automatic distance-to-query computation using haversine
• 🛣️ Road Slope Estimation: Estimate road edge angle from Mapillary semantic segmentation maps or directly from images
• 🔄 Resume Support: Skip already-processed images for incremental workflows

Embedding & Analysis

• 🧬 Embeddings: Qwen3-VL-Embedding (multimodal) or a CLIP-family dual encoder such as CLIP or SigLIP 2 (models actually run)
• 🔍 Vector Search: ChromaDB or FAISS indices, searchable by text or image
• 📈 Semantic Clustering: K-Means over embeddings with keyword extraction per cluster
• 🌐 Spatial Autocorrelation: Global and local Moran's I
• 📉 Visualization: Elbow curves, cluster maps, LISA maps, category distributions and Markdown reports

Street-level indicators (v0.4)

• 🚶 Active-mobility observation template: 19 features visible in a photograph (sidewalks, crossings, cycle infrastructure, trees, seating, light poles, ramps, barriers...), each with a state, a confidence and the visual evidence
• 🧭 Street segments: match images to street segments and summarise them per segment with coverage and capture dates
• 🗓️ Coverage and recency: how much of the network is actually observed, and how recently -- kept separate from image counts
• ✅ Evaluation tools: leakage-free sequence and spatial-block splits, kappa, ICC, Bland-Altman, cluster bootstrap, generation stability and stratified reference sampling
• 🧩 Segmentation measurements: class pixel fractions from a Cityscapes model with versioned label mappings
• 🖥️ Research demo: a small local web interface with grounded, cited answers

Requirements & Platform Support

• Python 3.10-3.12 (the core dependencies need 3.10 or later)
• The vLLM backend needs Linux and an NVIDIA GPU. The Transformers and OpenAI-compatible backends do not use vLLM; this release was tested on Linux only.
• Mapillary API key for downloading street-level imagery

Set up using Python

Create a new Python environment

It's recommended to use uv, a very fast Python environment manager, to create and manage Python environments. Please follow the documentation to install uv. After installing uv, you can create a new Python environment using the following commands:

uv venv --python 3.12 --seed
source .venv/bin/activate

Installation

Option 1: Install from PyPI

uv pip install geoai-vlm

The core install covers queries, GeoParquet I/O, parsing, clustering, spatial statistics, street segments, coverage and evaluation. Heavier parts are extras:

Option 2: Install from GitHub

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

# Install in the current environment
uv pip install .

# For development (editable mode)
uv pip install -e ".[dev]"

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

Choosing a Model and Backend

Any image-text-to-text model works the same way; only the model id and the backend change.

from geoai_vlm import ImageDescriber

# vLLM: fastest on a GPU; any model vLLM supports, via its own chat template
describer = ImageDescriber(
    model_name="Qwen/Qwen3-VL-2B-Instruct",
    backend="vllm",
    gpu_memory_utilization=0.8,
    max_model_len=8192,
)

# Transformers: CPU or GPU, no vLLM needed; batched with left padding
describer = ImageDescriber(
    model_name="HuggingFaceTB/SmolVLM2-256M-Video-Instruct",
    backend="transformers",
    device_map="auto",
    dtype="bfloat16",
    max_new_tokens=1024,
)

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

• backend="auto" (default) uses vLLM when it is installed and a GPU is present, Transformers otherwise.
• system_prompt_mode="auto" (default) checks the model's chat template once: if it rejects or silently drops a system turn, the system text is put at the start of the user turn instead. "system" and "prepend" force one form. Each record says which form was used (system_prompt_mode_effective).
• structured_output=True constrains decoding to the prompt template's JSON schema where the backend supports it and otherwise parses free text as before. Each record states decoding_mode: json_schema (enforced locally by vLLM), json_schema_requested (sent to an OpenAI-compatible server that accepted it -- some servers enforce it, others ignore it, which the client cannot see) or unconstrained. Responses are validated against the schema either way (validation_issues).
• trust_remote_code is off by default. A model that ships its own code raises RemoteCodeRequiredError until you review it and opt in with trust_remote_code=True.
• The older backend arguments (device, torch_dtype, max_tokens) still work.

Every description record carries its provenance: backend, backend_version, backend_endpoint (an HTTP server's URL without credentials), model_revision (the Hugging Face commit, or None when it cannot be known), generation_params, system_prompt_mode_effective, decoding_mode and processing_id. processing_id covers the model, its revision, the prompt version, the output-affecting generation settings and the backend (with the endpoint of an HTTP server, whose model names are only labels), so resuming a run never mixes outputs from different configurations.

Check whether a model works before a long run (one synthetic image; reports load, chat template, system-role support, JSON parsing, time and memory):

geoai-vlm check-model HuggingFaceTB/SmolVLM2-256M-Video-Instruct

See docs/models.md for the models actually run for this release and those only expected to work.

OpenAI-compatible Endpoints

Describe images with a model served by a vLLM server, Hugging Face TGI or Inference Endpoints, SGLang, Ollama, LM Studio or a hosted API:

from geoai_vlm import ImageDescriber

describer = ImageDescriber(
    model_name="Qwen/Qwen3-VL-2B-Instruct",   # the name the server knows the model by
    backend="openai",
    base_url="http://localhost:8000/v1",       # e.g. `vllm serve Qwen/Qwen3-VL-2B-Instruct`
    api_key_env="MY_ENDPOINT_KEY",             # NAME of the env var holding the key
    max_concurrency=4,
    timeout=120,
)
results = describer.describe(image_dir="./my_images", output_path="descriptions.parquet")

Requests use only requests, with timeouts, bounded retries and backoff (honouring Retry-After) and a bounded thread pool; images are sent as base64 data URLs. The key is read from the environment variable you name; no key is sent by default, so a key meant for one service is never forwarded to another server. It never appears in logs, records, error messages or model inputs.

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"]
}

Active-mobility Observation Template

active_mobility_audit_v1 asks only for what a single photograph can show. For each of 19 items -- sidewalk (with a visual width class), sidewalk gap, sidewalk obstruction, pedestrian crossing (and its type), intersection, traffic calming, cycle infrastructure, street trees, other greenery, shade on the walkway, seating, street light pole (presence only), parking on the sidewalk, curb ramp, accessibility barrier, surface damage, litter, active frontage and view obstruction -- the model returns a state, a confidence and the visual evidence.

from geoai_vlm import ImageDescriber, audit_table

describer = ImageDescriber(
    model_name="Qwen/Qwen3-VL-2B-Instruct",
    prompt_template="active_mobility_audit_v1",
    structured_output=True,
)
df = describer.describe(image_dir="./my_images", output_path="audit.parquet")

print(df[["image_id", "audit_sidewalk_state", "audit_sidewalk_width_class",
          "audit_pedestrian_crossing_state", "audit_cycle_infrastructure_state"]])
long_table = audit_table(df)   # one row per image and item

An unknown is never written as absent or 0. The template asks for no lighting-adequacy, safety, health or walkability judgement and produces no score.

Street Segments and Coverage

Move image observations to street segments, keeping track of how well each segment is actually observed:

import geopandas as gpd
from geoai_vlm import (
    describe_place, prepare_segments, snap_images_to_segments, aggregate_segments,
    write_segment_parquet, network_coverage, coverage_by_area, recency_report,
)

# Image points with Mapillary metadata (capture time, sequence) and audit columns
images = describe_place(
    place_name="Your district, Your city",
    mly_api_key="YOUR_API_KEY",
    prompt_template="active_mobility_audit_v1",
)
segments = prepare_segments(gpd.read_file("streets.gpkg"), id_column="street_id")

snapped = snap_images_to_segments(images, segments, max_distance_m=20, ambiguity_margin_m=2)
summary = aggregate_segments(
    snapped,
    segments,
    state_columns=["audit_sidewalk_state", "audit_street_trees_state"],
    support_length_m=25,
)
write_segment_parquet(summary, "segments.parquet")  # analysis unit and rules in the metadata

print(network_coverage(summary))                                  # share of network length observed
print(recency_report(summary, year_edges=(2018, 2021, 2024)))      # by year of the newest image

• Images go to the nearest segment within 20 m; equal distances go to the smaller segment id; a runner-up within 2 m marks the match ambiguous (kept, and reported).
• Each image is taken to observe 25 m of street centred on it; covered_length_share is the union of those stretches over the segment length. Image counts are reported separately -- many images of one corner still cover one corner.
• For audit states, present_share is present / (present + absent); not_visible, uncertain, not_assessed, invalid, failed and missing values are counted, never folded in.
• coverage_by_area(summary, areas) reports the same within your own polygons (neighbourhoods, districts). Street networks can come from any line layer, or from OpenStreetMap with load_osm_segments(place=...) (geoai-vlm[network]).

Evaluation Tools

from geoai_vlm import (
    spatial_block_split, check_split_leakage, agreement_report,
    light_condition, stratified_reference_sample,
)

# Train/test split where neither a 500 m block nor a capture sequence spans both sides
split = spatial_block_split(images, block_size_m=500, test_size=0.2, seed=0, sequence_column="sequence_id")
check_split_leakage(split, ["block_id", "sequence_id"])   # raises on any leak

# Agreement of model observations with reference labels, CIs by sequence
# (labels: one row per image with both states and its sequence id)
report = agreement_report(labels, "reference_state", "model_state", kind="categorical",
                          cluster_column="sequence_id", n_boot=1000, seed=0)

# Seeded, quota-based reference sample: area x road class x daylight proxy
images["light"] = light_condition(images["captured_at"], images["lat"], images["lon"])
sample = stratified_reference_sample(images, ["area", "road_class", "light"], n_per_stratum=20, seed=2026)

Also available: cohen_kappa (unweighted or weighted), icc (ICC(2,1), ICC(3,1)), r_squared and bland_altman (e.g. between two segmentation models), cluster_bootstrap_ci, and repeated_generation + generation_stability to measure how often repeated sampled generations agree per field. light_condition is a proxy computed from capture time and solar elevation, not an observation of the image.

Segmentation Measurements

from geoai_vlm import SemanticSegmenter

segmenter = SemanticSegmenter()   # SegFormer-B0 trained on Cityscapes
table = segmenter.measure(["a.jpg", "b.jpg"])
print(table[["image_id", "vegetation_pixel_fraction", "sky_pixel_fraction", "label_mapping"]])

Columns name the measurement (vegetation_pixel_fraction is the share of labelled pixels assigned to vegetation in that image -- not green-space exposure). Label mappings are versioned (cityscapes-19@1); a model whose labels do not match its mapping is rejected, so Cityscapes and Mapillary Vistas ids are never mixed.

Research Demo

A small local interface: describe an uploaded image (structured description plus the observation template), find similar indexed scenes on a map, list scenes near a coordinate, and ask questions answered only from retrieved descriptions -- every statement cites an image id, and the tool declines when no indexed record supports an answer.

pip install 'geoai-vlm[app,transformers]'
python examples/build_demo_index.py --n 300 --out ./demo_index   # published dataset, attribution kept
geoai-vlm app --index ./demo_index --model Qwen/Qwen3-VL-2B-Instruct

The demo binds to 127.0.0.1 and never creates a public link. It is a research demonstration: it shows model outputs and retrieval, gives no design recommendations and makes no claim about health, safety or walkability. The logic lives in geoai_vlm.service, independent of the interface.

Road Slope Estimation (v0.3)

Estimate road slope from a Mapillary Vistas semantic segmentation map:

from geoai_vlm import SlopeConfig, estimate_slope_from_semantic_map

result = estimate_slope_from_semantic_map(
    semantic_map,
    config=SlopeConfig(morphology_kernel_size=15, min_edge_points=10)
)

print(result.road_edge_line_angle)  # road edge angle in degrees

Or estimate directly from an image using the default Mask2Former Mapillary Vistas model:

from geoai_vlm import ImageSlopeEstimator

estimator = ImageSlopeEstimator()
result = estimator.estimate("street_view.jpg")

print(result.to_dict())

The complete Vision2Slope pipeline is also available inside GeoAI-VLM:

from geoai_vlm import (
    PipelineConfig,
    ProcessingConfig,
    VisualizationConfig,
    Vision2SlopePipeline,
)

config = PipelineConfig(
    input_dir="street_view_images",
    output_dir="slope_output",
    processing_config=ProcessingConfig(
        is_panorama=True,
        panorama_fov=90,
        panorama_phi=0.0,
        panorama_aspects=(10, 10),
    ),
    viz_config=VisualizationConfig(
        save_visualizations=True,
        save_corrected_images=True,
        save_intermediate_results=True,
    ),
)

pipeline = Vision2SlopePipeline(config)
slope_results = pipeline.process_batch()

All original Vision2Slope modules are vendored under geoai_vlm.vision2slope, including panorama transformation, semantic segmentation, skew correction, road-edge fitting, visualization, CLI, and optional Google Street View downloading.

For panorama left/right perspective outputs, aggregate image-level measurements into a panorama-level slope:

from geoai_vlm import aggregate_pano_slopes

slope_df = aggregate_pano_slopes(results_df, angle_threshold=10)
print(slope_df[["pano_id", "road_estimated_slope"]].drop_duplicates())

Multimodal Embeddings

Generate dense vectors from descriptions and images with Qwen3-VL-Embedding (default) or a CLIP-family encoder:

from geoai_vlm import ImageEmbedder

# Qwen3-VL-Embedding (auto-selects vLLM or Transformers)
embedder = ImageEmbedder(model_name="Qwen/Qwen3-VL-Embedding-2B", backend="auto")

# Or a CLIP-family dual encoder. Run for this release: SigLIP 2 and CLIP ViT-B/32;
# MetaCLIP, StreetCLIP and other checkpoints use the same path but were not run.
embedder = ImageEmbedder(backend="clip", model_name="google/siglip2-base-patch16-224")

# Embed text descriptions
vectors = embedder.embed_texts(["A busy commercial street with shops"])
print(vectors.shape)  # (1, embedding_dim)

# Embed images directly
img_vectors = embedder.embed_images(["path/to/image.jpg"])

# Multimodal: one vector for an image together with its description
mm_vectors = embedder.embed_multimodal(
    [{"image": "path/to/image.jpg", "text": "A quiet residential area"}]
)

All vectors are L2-normalised, so inner product equals cosine similarity. With the CLIP backend an image-plus-text input is the normalised mean of its two vectors (a simple fusion, kept in the same space as text-only and image-only vectors).

Semantic Clustering

Cluster geotagged descriptions by semantic similarity and extract per-cluster keywords:

from geoai_vlm import SemanticClusterer, ClusterConfig

config = ClusterConfig(
    n_clusters=8,
    embedding_columns=["scene_narrative", "semantic_tags"],
    n_keywords=10
)
clusterer = SemanticClusterer(embedder=embedder, config=config)

# Cluster a GeoDataFrame of VLM descriptions
gdf = clusterer.cluster(gdf)
print(gdf["cluster"].value_counts())

# Find the optimal number of clusters (k from 2 to 19)
k_values, inertias = clusterer.find_optimal_k(gdf, k_range=(2, 20))

# Extract TF-IDF keywords per cluster
keywords = clusterer.extract_keywords(gdf)
for cluster_id, words in keywords.items():
    print(f"Cluster {cluster_id}: {words}")

Spatial Autocorrelation

Detect whether semantic clusters are spatially random or form significant patterns:

from geoai_vlm import SpatialAnalyzer

analyzer = SpatialAnalyzer(k_neighbors=8)

# Global Moran's I per cluster -- {cluster_id: MoranResult}
global_results = analyzer.moran_global(gdf, column="cluster")
for cluster_id, result in global_results.items():
    print(f"cluster {cluster_id}: Moran's I = {result.I:.3f}, p = {result.p_value:.4f}")

# Local Moran's I (LISA) -- where are the hot/cold spots?
gdf = analyzer.moran_local(gdf, column="cluster")
# Adds 'lisa_cluster', 'lisa_p_value' and 'lisa_significant' columns

Vector Similarity Search

Build a searchable index over your geotagged descriptions and find semantically similar places:

from geoai_vlm import VectorDB

# Build an index from a GeoDataFrame
vdb = VectorDB(embedder=embedder, store_backend="chromadb")
vdb.build(
    gdf,
    text_column="scene_narrative",
    image_dir="./images",
    metadata_columns=["land_use_primary", "cluster"]
)

# Search by natural language (best match first)
results = vdb.search(query_text="tree-lined residential street", n_results=5)
print(results[["id", "similarity", "land_use_primary"]])

# Search by image
results = vdb.search(query_image="query_photo.jpg", n_results=5)

Rank by similarity (higher is always closer); distance is the backend's raw score, whose direction depends on the metric.

Visualization

from geoai_vlm import (
    plot_elbow_curve,
    plot_cluster_map,
    plot_lisa_map,
    plot_category_distribution,
    generate_report
)

# Elbow curve for choosing k
plot_elbow_curve(k_values, inertias, save_path="elbow.png")

# Map of clusters
plot_cluster_map(gdf, cluster_column="cluster", save_path="clusters.png")

# LISA significance map
plot_lisa_map(gdf, save_path="lisa.png")

# Category breakdown
plot_category_distribution(gdf, category_columns=["land_use_primary"])

# Full report (Markdown)
generate_report(gdf, output_path="./report/summary.md")

One-Line Pipeline

Run the entire workflow — download, describe, embed, cluster, analyze — in a single call:

from geoai_vlm import embed_place, cluster_descriptions, analyze_spatial

# 1. Download + embed
gdf = embed_place(
    place_name="Sultanahmet, Istanbul",
    mly_api_key="YOUR_API_KEY",
    model_name="Qwen/Qwen3-VL-Embedding-2B",
    # "multimodal" (default) encodes image + description jointly;
    # use "text" for description-only or "image" for image-only.
    embedding_modality="multimodal",
)

# 2. Cluster
gdf = cluster_descriptions(gdf, n_clusters=8)

# 3. Spatial analysis
# Returns {"global": {cluster_id: MoranResult}, "gdf": <GeoDataFrame with LISA columns>}
spatial = analyze_spatial(gdf, column="cluster", k_neighbors=8)
gdf = spatial["gdf"]

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

Limitations: Measurements, Not Claims

• Every value is an image observation: what one photograph shows, from one viewpoint, at one moment, as read by a model. vegetation_pixel_fraction is a share of pixels, not green-space exposure; a street light pole being visible says nothing about lighting at night.
• Model outputs are not ground truth. Check them against reference labels (see the evaluation tools) before using them as measurements, and report agreement with its uncertainty.
• Street-segment summaries depend on coverage: report covered_length_share and capture dates with them. Few or old images describe a segment poorly, however many indicators they produce.
• Nothing in this package establishes an effect of the built environment on walking, cycling, physical activity or health, and it computes no composite walkability or health score.
• Imagery and derived data keep their original licences (Mapillary imagery is CC BY-SA 4.0); keep attribution when you redistribute.

Dependencies

• Core: geopandas, pandas, shapely, pyarrow, haversine, requests, scikit-learn, libpysal, esda
• Downloading: zensvi (Mapillary integration)
• VLM: vLLM and/or Transformers + torch; any OpenAI-compatible server needs nothing extra
• Embedding & Analysis: chromadb, faiss-cpu
• Networks / segmentation / demo: osmnx, transformers + torch, gradio
• Slope Estimation / Vision2Slope: transformers, torch, Pillow, scikit-learn, scikit-image, opencv-python, zensvi, streetlevel

License

GNU General Public License v3.0 - see LICENSE for details.

Citation

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

@software{geoai_vlm,
  author  = {B{\i}{\c{c}}ak{\c{c}}{\i}, Yunus Serhat},
  title   = {GeoAI-VLM: Geospatial Vision-Language Model Analysis},
  year    = {2026},
  publisher = {Zenodo},
  doi     = {10.5281/zenodo.18169685},
  url     = {https://github.com/yunusserhat/GeoAI-VLM}
}

Related publications and data:

• Bıçakçı, Y. S., Shingleton, J., Wang, Y., & Basiri, A. (2026). Mapping the Semantics of the Street: A VLM-Driven Geospatial Analysis in Fatih, Istanbul. 1st International Conference on Geospatial Artificial Intelligence (GeoAI 2026). doi:10.5281/zenodo.19390648
• Fatih Mapillary street-level images with derived layers (dataset, CC BY-SA 4.0). doi:10.57967/hf/10144
• Bıçakçı, Y. S. (2026). yunusserhat/alphaearth_asprs: v1.0.0 [Software], code accompanying an article accepted in Photogrammetric Engineering & Remote Sensing. doi:10.5281/zenodo.22818970

Acknowledgments

• ZenSVI for Mapillary integration
• Vision2Slope for the road slope estimation workflow
• Qwen-VL for vision-language models
• Qwen3-VL-Embedding for multimodal embeddings
• vLLM and Transformers for inference
• ChromaDB and FAISS for vector search
• PySAL for spatial statistics
• OSMnx and OpenStreetMap contributors for street networks