pygeovision 2.0.0

The World-Class Geospatial AI Platform — PyGeoFetch × GeoAI × EarthNets

PyGeoVision

World-Class Geospatial AI Platform

The definitive Python framework for satellite data acquisition and geospatial AI —
unifying PyGeoFetch (22+ providers) and GeoAI (full AI stack) in one coherent API.

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What is PyGeoVision?

PyGeoVision is a production-ready geospatial AI platform that unifies satellite data acquisition, foundation model inference, and full-stack model training in a single coherent API.

Layer	Package / Module	Responsibility
🛰️ Data	PyGeoFetch	Search & download from 22+ providers (Sentinel, Landsat, Planet, Maxar, USGS, Copernicus …) with auth, caching, parallel downloads, and YAML pipeline orchestration
🤖 AI	GeoAI	Full AI stack: segmentation, detection, classification, change detection, SAM, foundation models, embeddings, cloud masking, super-resolution, ONNX export
🧠 Foundation	DINOv3 + Prithvi-EO-2.0	12 DINOv3 variants (SAT-493M pre-training) + Prithvi-EO-2.0 (600M, HLS global 10-year)
🔗 Bridge	PyGeoVision	Unified API, 119 model registry, 503 datasets, 25 production notebooks, CLI, serving, edge, cloud

Design principle: PyGeoVision never reimplements PyGeoFetch or GeoAI. All data operations delegate to PyGeoFetch. All AI operations delegate to GeoAI. PyGeoVision is the integration and extension layer.

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What's New in v2.0

Area	v1.0	v2.0
Tests passing	208	580
Model architectures	14	119
Datasets registered	—	503
Production notebooks	—	25
Foundation models	—	DINOv3 (12 variants) + Prithvi-EO-2.0
Auto-labeling sources	7	7 + SAM-auto + DynamicWorld
Serving API	—	FastAPI + WebSocket streaming
Edge deployment	—	ONNX Runtime + Jetson TensorRT
Cloud deployment	—	AWS SageMaker + Azure ML + GCP Vertex
VLM	—	CLIP + RemoteCLIP + Moondream 2
Few-shot learning	—	Prototypical networks (DINOv2 backbone)
AutoML / HPO	—	Optuna integration
Time-series analysis	—	GeoTimeSeries + anomaly detection
Point cloud	—	PointNet++ + RandLA-Net + KPConv
GeoAI subsystems	24	27
Monitoring	—	Drift detection + performance tracking + alerts

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Architecture Overview

PyGeoVision v2.0
├── pygeovision/               Core platform
│   ├── __init__.py            PyGeoVision client — unified entry point
│   ├── data/fetch.py          SatelliteFetcher — 22 providers via PyGeoFetch
│   │
│   ├── models/                119 model architectures
│   │   ├── registry.py        ModelSpec registry + download manager
│   │   ├── classification/    ViT, Swin, EfficientNet, DINOv3
│   │   ├── detection/         GeoYOLO, DETR, RF-DETR
│   │   ├── segmentation/      U-Net, SegFormer, DeepLabV3+, SAM
│   │   ├── change_detection/  ChangeFormer, ChangeSTAR, BIT, DSAMNet
│   │   ├── foundation/        dinov3.py (12 variants), prithvi.py (600M)
│   │   ├── vlm/               CLIP, RemoteCLIP, Moondream 2
│   │   └── _3d/               PointNet++, RandLA-Net, KPConv
│   │
│   ├── labeling/              Auto-labeling pipeline (7 sources)
│   ├── losses/                Geospatial mixed loss, boundary-aware, OHEM
│   ├── inference/             TiledInference (Gaussian blend), batch, stream, ensemble
│   ├── explainability/        GradCAM, attention maps, SHAP-Geo, uncertainty
│   ├── monitoring/            Drift detection, performance tracker, alerts
│   ├── training/              GeoTrainer, callbacks, metrics, distributed
│   ├── serving/               FastAPI + WebSocket + JWT auth + health checks
│   ├── pipelines/             YAML pipelines + orchestrator + scheduler
│   ├── datasets/              503 datasets, registry, loader, catalog
│   ├── cli/                   15 command groups
│   ├── edge/                  ONNX Runtime + Jetson TensorRT FP16
│   ├── cloud/                 AWSDeployer, AzureDeployer, GCPDeployer
│   └── advanced/              Few-shot, AutoML, VLM, timeseries, pointcloud
│
├── tests/                     580 passing tests
├── docs/                      44 documentation pages (6,976 lines)
└── projects/                  25 production Jupyter notebooks (357 cells)

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Installation

# Core — data + basic inference
pip install pygeovision

# + Geospatial processing (rasterio, geopandas, rioxarray)
pip install "pygeovision[geo]"

# + Training stack (PyTorch, SMP, transformers, timm)
pip install "pygeovision[train]"

# + Foundation models (DINOv3, Prithvi-EO-2.0)
pip install "pygeovision[foundation]"

# + Vision-language models (CLIP, Moondream)
pip install "pygeovision[vlm]"

# + Time-series analysis
pip install "pygeovision[timeseries]"

# + Serving API (FastAPI, uvicorn, websockets)
pip install "pygeovision[serve]"

# + Everything
pip install "pygeovision[all]"

Requirements: Python 3.10+ · PyGeoFetch · GeoAI (optional) · PyTorch 2.0+

---

Quick Start

import pygeovision as pgv

# Initialise
client = pgv.PyGeoVision()
print(client)
# PyGeoVision(v2.0.0 | pygeofetch=✓ | geoai=✓ | models=119 | datasets=503)

# ── 1. Add credentials (stored securely in system keyring) ─────────────────
client.add_credentials("usgs",      username="user", password="pass")
client.add_credentials("planet",    api_key="PL-xxxx")
client.add_credentials("copernicus", client_id="id", client_secret="secret")

# ── 2. Search satellite imagery ─────────────────────────────────────────────
results = client.search(
    bbox        = (-0.15, 51.47, -0.10, 51.52),   # London, WGS84
    date_range  = ("2024-06-01", "2024-06-30"),
    providers   = ["planetary_computer", "copernicus"],
    cloud_cover_max = 10,
    sort_by     = "cloud_cover",
)
print(f"Found {len(results)} scenes")

# ── 3. Download with post-processing ────────────────────────────────────────
downloads = client.download(
    results[:2],
    output_dir   = "./sentinel2/",
    parallel     = 4,
    post_process = ["reproject:EPSG:4326", "cog"],
)

# ── 4. Auto-label from Microsoft Building Footprints ────────────────────────
labels = client.labeling.microsoft_buildings(
    bbox        = (-0.15, 51.47, -0.10, 51.52),
    output_path = "./labels_ms.tif",
    resolution_m= 10.0,
)

# ── 5. GeoAI building segmentation ──────────────────────────────────────────
client.geoai.segment.buildings(
    downloads[0].path,
    output_path   = "buildings.tif",
    output_vector = "buildings.geojson",
)

# ── 6. Foundation model: Prithvi-EO-2.0 land cover ──────────────────────────
from pygeovision.models.foundation.prithvi import PrithviTasks
tasks  = PrithviTasks("prithvi_eo_2_0")
result = tasks.land_cover(downloads[0].path, source="sentinel2")

# ── 7. Foundation model: DINOv3 feature extraction ──────────────────────────
from pygeovision.models.foundation.dinov3 import DINOv3Backbone
backbone   = DINOv3Backbone("dinov3_vitl16_sat")
embeddings = backbone.extract_embeddings(downloads[0].path)   # (1, 1024)

# ── 8. End-to-end pipeline ──────────────────────────────────────────────────
result = client.pipeline(
    "building_footprints",
    bbox       = (-0.15, 51.47, -0.10, 51.52),
    date       = "2024-06",
    output_dir = "./results/",
)
print(result.stats)  # {"buildings_detected": 1847, "coverage_pct": 0.312}

# ── 9. Deploy a model as a REST API ─────────────────────────────────────────
from pygeovision.serving import InferenceServer
server = InferenceServer(auth_keys={"prod": "SECRET"})
server.register("seg_v1", "model.onnx", task="segmentation", num_classes=5)
server.serve(host="0.0.0.0", port=8080)
# → POST /predict  |  POST /predict/batch  |  GET /health  |  WS /ws/stream

---

Data Layer — PyGeoFetch (22 Providers)

Provider Registry

Provider ID	Name	Auth	Key Satellites	SAR	Sub-m
planetary_computer	Microsoft Planetary Computer	🌐 Open	Sentinel-1/2, Landsat, MODIS, NAIP	✓
aws_earth	AWS Earth Open Data	🌐 Open	Sentinel-2 COGs, Landsat
element84	Element 84 Earth Search	🌐 Open	Sentinel-2, Landsat Col 2
noaa_big_data	NOAA Big Data	🌐 Open	GOES-16/17/18, NEXRAD
esa_scihub	ESA SciHub Mirror	🌐 Open	Copernicus mirrors	✓
jaxa_earth	JAXA ALOS World	🌐 Open	ALOS 30m DSM, PALSAR	✓
isro_bhuvan	ISRO Bhuvan	🌐 Open	ResourceSat, Cartosat
inpe_cbers	INPE CBERS	🌐 Open	CBERS-4/4A
digitalglobe	DigitalGlobe Open Data	🌐 Open	Disaster VHR		✓
geoserver_generic	GeoServer Generic OGC	🌐 Open	Any WMS/WCS/WFS
usgs	USGS Earth Explorer	🔐 User/Pass	Landsat 1–9, ASTER, MODIS
copernicus	Copernicus CDSE	🔐 OAuth2	Sentinel-1/2/3/5P	✓
nasa_earthdata	NASA Earthdata CMR	🔐 OAuth2	MODIS, VIIRS, ICESat-2, GEDI
nasa_earthdata_cloud	NASA Earthdata Cloud	🔐 OAuth2+S3	Cloud-hosted NASA
opentopography	OpenTopography	🔐 API Key	SRTM, Copernicus DEM, LiDAR
planet	Planet Labs	🔐 API Key	PlanetScope 3–5m, SkySat 0.5m		✓
sentinel_hub	Sentinel Hub	🔐 OAuth2	All Sentinels, Landsat, MODIS	✓
maxar_gbdx	Maxar GBDX	🔐 Token	WorldView 1–4, GeoEye-1 (30cm)		✓
airbus_oneatlas	Airbus OneAtlas	🔐 API Key	Pléiades 50cm, SPOT 1.5m		✓
alaska_satellite_facility	ASF	🔐 Earthdata	Sentinel-1, ALOS PALSAR	✓
google_earth_engine	Google Earth Engine	🔐 Service Acct	Multi-petabyte catalog	✓
terrabotics	TerraBotics	🔐 API Key	Archive + tasking		✓

Search API

# Standard search
results = client.search(
    bbox            = (-74.1, 40.6, -73.7, 40.9),
    date_range      = ("2024-01-01", "2024-06-01"),
    providers       = ["planetary_computer", "copernicus"],
    cloud_cover_max = 10,
    sort_by         = "cloud_cover",   # datetime | cloud_cover | score
    limit           = 50,
    use_cache       = True,
)

# Satellite shortcut
results = client.search(bbox=..., date_range=..., satellite="sentinel-2")

# STAC collection
results = client.search(bbox=..., date_range=...,
    collections=["sentinel-2-l2a", "sentinel-1-rtc"])

# SAR (cloud-independent)
results = client.search(bbox=..., date_range=...,
    collections=["sentinel-1-rtc"], cloud_cover_max=100)

# CQL2 filter
results = client.search(bbox=..., date_range=...,
    cql2_filter="eo:cloud_cover < 5 AND platform = 'sentinel-2b'")

# SearchResult properties
r = results[0]
r.id           # 'S2C_MSIL2A_20240603T153811_R001'
r.provider     # 'planetary_computer'
r.date         # '2024-06-03'
r.cloud_cover  # 0.0
r.platform     # 'Sentinel-2C'
r.resolution_m # 10.0
r.bands        # ['B02','B03','B04','B08','B11','B12']
r.bbox         # (-74.1, 40.6, -73.7, 40.9)
r.crs          # 'EPSG:32618'
r.collection   # 'sentinel-2-l2a'

Download API

downloads = client.download(
    results[:3],
    output_dir       = "./data/",
    parallel         = 4,
    overwrite        = False,        # resume interrupted downloads
    post_process     = [
        "reproject:EPSG:32618",      # UTM Zone 18N
        "cog",                       # Cloud-Optimized GeoTIFF
        "cloud_mask",                # Apply SCL cloud mask
    ],
    bands            = ["B02","B03","B04","B08"],   # Subset bands
    retry_attempts   = 3,
    on_failure       = "skip",       # skip | raise | warn
)

# DownloadResult
d = downloads[0]
d.success          # True
d.path             # './data/S2C_MSIL2A_...tif'
d.size_mb          # 245.3
d.scene_id         # 'S2C_MSIL2A_20240603...'
d.error            # None (or error message)

Cache and Pipeline

# Cache
client.cache_stats()                  # {entries, size_mb, location}
client.clear_cache()
client.clear_cache(older_than="7d")

# YAML pipeline
pipeline = (
    client.create_pipeline("weekly-sentinel2")
    .search(bbox=..., providers=["planetary_computer"],
             date_range="last_7_days", cloud_cover_max=10)
    .download(parallel=4, post_process=["reproject:EPSG:4326","cog"])
    .schedule(cron="0 6 * * 1")
)
pipeline.save("pipeline.yaml")
pipeline.run(dry_run=True)   # validate without executing
pipeline.run()

---

Foundation Models

PyGeoVision v2.0 ships native integration with the two leading geospatial foundation models.

DINOv3 (12 Variants)

from pygeovision.models.foundation.dinov3 import (
    DINOv3Backbone, get_transform, finetune_dinov3,
    WEB_MEAN, WEB_STD, SAT_MEAN, SAT_STD,
)

# CRITICAL: use the correct normalisation transform
# Web pre-training (LVD-1689M) → ImageNet stats
# SAT pre-training (SAT-493M)  → Satellite stats (different mean/std!)
transform = get_transform("dinov3_vitl16_sat")   # auto-selects correct stats

Model	Params	Pre-training	Embedding	Best for
dinov3_vits16	21M	Web LVD-1689M	384	Fast inference
dinov3_vitb16	86M	Web LVD-1689M	768	Balanced
dinov3_vitl16	300M	Web LVD-1689M	1024	High accuracy
dinov3_vitl16_sat	300M	SAT-493M	1024	Satellite imagery
dinov3_vit7b16_sat	6.7B	SAT-493M	4096	State-of-the-art
dinov3_convnext_base	89M	Web	1024	Convolutional
(+ 6 more variants)

backbone = DINOv3Backbone("dinov3_vitl16_sat")

# Feature extraction
features   = backbone.extract_features(scene_path)      # (H_p, W_p, 1024) spatial
embeddings = backbone.extract_embeddings(scene_path)    # (1, 1024) global CLS
attention  = backbone.get_attention_maps(scene_path)    # (H_p, W_p) saliency

# Build classifiers
clf = backbone.build_classifier(num_classes=10, freeze_backbone=True)

# Canopy height (DINOv3 CHMv2 — 1m global, GEDI calibrated)
from pygeovision.models.foundation.dinov3 import CHMv2Model
chm    = CHMv2Model()
result = chm.predict_canopy_height(scene_path)    # mean_m, max_m, coverage_pct
biomass= chm.estimate_biomass(scene_path)          # t DM/ha

# Fine-tuning
result = finetune_dinov3(
    model_name    = "dinov3_vitl16_sat",
    dataset       = my_dataset,
    task          = "segmentation",    # classification | segmentation | detection
    num_classes   = 7,
    epochs        = 50,
    learning_rate = 1e-5,             # Recommended for SAT pre-trained
    weight_decay  = 0.05,
    mixed_precision = True,            # BF16 — critical for ViT-L
)

Prithvi-EO-2.0 (600M)

from pygeovision.models.foundation.prithvi import (
    Prithvi, PrithviTasks, PrithviMultiTemporal,
    map_bands, normalise_hls, finetune_prithvi,
    HLS_SCALE_FACTOR,
)

CRITICAL: Band ordering. Prithvi always expects HLS format:

Position	HLS Band	Sentinel-2	Landsat
0	Blue	B02	B2
1	Green	B03	B3
2	Red	B04	B4
3	NIR	B08	B5
4	SWIR1	B11	B6
5	SWIR2	B12	B7

# Always remap before inference
data_hls  = map_bands(data, source="sentinel2")
data_norm = normalise_hls(data_hls)   # divides by 10000.0

# Available tasks
tasks = PrithviTasks("prithvi_eo_2_0")
tasks.land_cover(scene_path, source="sentinel2")          # 10-class ESA-compatible
tasks.crop_mapping(scene_path, source="sentinel2")         # 10 crop types
tasks.flood_detection(scene_path, source="sentinel2")      # binary flood mask
tasks.deforestation_detection(scene_path, source="sentinel2")

# Multi-temporal (4 frames simultaneously)
mt = PrithviMultiTemporal("prithvi_eo_2_0")
mt.process_time_series(images, dates=dates, source="sentinel2")
mt.detect_change(before_path, after_path)

# Fine-tuning
result = finetune_prithvi(
    model_name    = "prithvi_eo_2_0",
    task          = "land_cover",
    num_classes   = 9,
    epochs        = 50,
    learning_rate = 5e-5,   # Paper recommendation
    batch_size    = 8,       # Memory-limited for 600M
    mixed_precision = True,
)

---

Model Registry — 119 Architectures

from pygeovision.models import get_model, list_models
from pygeovision.models.registry import ModelRegistry

# List and load
list_models(task="segmentation")
model = get_model("segformer-b2", num_classes=5, in_channels=6, pretrained=True)

# Registry
registry = ModelRegistry()
registry.list(task="change_detection")
registry.list(task="foundation")
registry.info("prithvi_eo_2_0")

Segmentation (24): U-Net variants, SegFormer-B0→B5, DeepLabV3+, PSPNet, Mask2Former, SAM, SAM2
Detection (18): GeoYOLO v5/v8/v9/v10/v11, DETR, RT-DETR, RF-DETR, FCOS, RetinaNet, Faster R-CNN
Classification (16): ViT variants, Swin Transformer, EfficientNet B0→B7, ResNet, DenseNet, ConvNeXt
Change Detection (12): ChangeFormer, ChangeSTAR, BIT, DSAMNet, SNUNet, DTCDSCN, FC-EF, FC-Siam
Foundation (12): DINOv3 (6 ViT + 4 ConvNeXt), Prithvi-EO-1.0, Prithvi-EO-2.0
VLM (9): CLIP ViT-B/32, CLIP ViT-L/14, OpenCLIP L/14, RemoteCLIP L/14, GeoRSCLIP, Moondream 2, LLaVA-Geo, GeoChat, RSGPT
3D / Point Cloud (8): PointNet, PointNet++, RandLA-Net, KPConv, Point Transformer, PointMamba, PointBERT, Point-MAE

---

Dataset Registry — 503 Datasets

from pygeovision.datasets import DatasetRegistry, DatasetLoader

registry = DatasetRegistry()
registry.list(task="segmentation", sensor="sentinel2")
registry.list(task="change_detection")
registry.info("inria_aerial")
registry.download("deepglobe_roads", output_dir="./datasets/")

loader   = DatasetLoader(registry)
train_ds = loader.load("spacenet_buildings", split="train", chip_size=512)

Domains covered: building extraction · road networks · land cover · change detection · crop mapping · flood detection · wildfire · SAR · very-high-resolution · hyperspectral · point cloud · VQA · time series

Notable datasets: SpaceNet 1–8, DOTA v1/v2, iSAID, DIOR, RESISC-45, UC Merced, DeepGlobe, Inria Aerial, Potsdam, Vaihingen, FloodNet, xBD (disaster damage), BreizhCrops, BigEarthNet, SEN12MS, DynamicEarthNet, PASTIS, TreeSatAI, NEON, MDAS, GAMUS, SatlasPretrain, MillionAID, RS5M, and 450+ more.

---

GeoAI Integration — 27 Subsystems

ga = client.geoai   # GeoAIEngine proxy — lazy-loaded

Segmentation — buildings, solar panels, agriculture fields, water bodies, roads, coastline, oil spills, glaciers, wetlands, custom, SAM, SAM2, timm, HuggingFace Hub
Detection — cars, ships, parking, grounded (natural language), RF-DETR, multi-class, instance segmentation
Classification — scene classification, CLIP zero-shot land cover, batch
Change Detection — ChangeSTAR bi-temporal, multi-temporal
Training — segmentation, land cover, detection, instance segmentation, timm, pixel regression, RF-DETR, chip generation
Foundation — Prithvi inference, SAM masks, GroundedSAM, DINOv3 analysis and fine-tuning
Embeddings — patch/pixel embeddings, clustering, similarity, UMAP visualisation, Tessera downloads
Cloud — cloud mask prediction, batch, statistics
Super-resolution — ESRGAN 4×/8× upscaling
ONNX — export, segmentation inference
Caption / VLM — Moondream caption, VQA, object detection by text
Utilities — raster/vector conversion, clipping, mosaicking, stacking, smoothing, metrics, device management

---

Auto-Labeling Pipeline

# 7 sources, no manual annotation required

# Microsoft Building Footprints (~1.4B global buildings)
client.labeling.microsoft_buildings(bbox, output_path, resolution_m=10.0)

# Google Open Buildings (~1.8B global)
client.labeling.google_buildings(bbox, output_path)

# OpenStreetMap
client.labeling.osm(bbox, categories=["buildings","roads","water"], output_path)

# ESA WorldCover 2021 (10m, 11 classes)
client.labeling.esa_worldcover(bbox, output_path)

# Google Dynamic World (near-real-time, 10m)
client.labeling.dynamic_world(bbox, date_range=["2024-01","2024-12"])

# SAM automatic mask generation (zero-shot)
client.labeling.sam_auto(scene_path, output_path, points_per_side=32)

# DINOv2 unsupervised clustering
from pygeovision.labeling.foundation import FoundationModelLabeler
labeler = FoundationModelLabeler("dinov2-base")
labeler.cluster(scene_path, output_path, n_clusters=7)

# Quality assessment
report = client.labeling.quality(label_path)
report["quality_grade"]   # 'A' | 'B' | 'C'
report["quality_score"]   # 0.0–1.0

# Multi-source label fusion
pipeline = client.labeling.pipeline(
    bbox=bbox,
    sources=[
        {"type": "microsoft_buildings", "weight": 1.5},
        {"type": "osm",                 "weight": 1.0},
        {"type": "sam",                 "weight": 0.8},
    ],
    fusion="weighted_vote",
    output_path="labels_fused.tif",
)

---

Training

from pygeovision.models import get_model
from pygeovision.training.trainer import GeoTrainer
from pygeovision.training.callbacks import EarlyStopping, ModelCheckpoint
from pygeovision.losses.segmentation import GeospatialMixedLoss

model  = get_model("segformer-b2", num_classes=5, in_channels=6, pretrained=True)
loss   = GeospatialMixedLoss(weights={"combo":0.5,"boundary":0.3,"ohem":0.2})

trainer = GeoTrainer(
    model            = model,
    task             = "segmentation",
    num_classes      = 5,
    epochs           = 100,
    learning_rate    = 6e-5,
    weight_decay     = 0.01,
    mixed_precision  = "bf16",         # "fp32" | "fp16" | "bf16"
    device           = "cuda",
    loss_fn          = loss,
    callbacks        = [
        EarlyStopping(monitor="val_iou", patience=15, mode="max"),
        ModelCheckpoint("./checkpoints/", monitor="val_iou", save_top_k=3),
    ],
)

history = trainer.fit(train_dl, val_dl)
print(f"Best val_iou: {history['best_metrics']['val_iou']:.4f}")

Available losses: DiceLoss · FocalLoss · TverskyLoss · GeospatialMixedLoss (Dice+Boundary+OHEM) · ChangeDetectionLoss · BoundaryAwareLoss · WeightedCrossEntropyLoss

Supported optimisers: AdamW · SGD · Lion · RMSProp
Supported schedulers: CosineAnnealing · ReduceLROnPlateau · OneCycleLR · WarmupCosine
Supported precisions: FP32 · FP16 · BF16 (recommended for A100/H100)

---

Tiled Inference

from pygeovision.inference.tiled import TiledInference

inf    = TiledInference(
    model       = model,
    chip_size   = 512,
    overlap     = 64,
    blend_mode  = "gaussian",   # "gaussian" | "average"
    num_classes = 5,
    device      = "cuda",
    batch_size  = 8,
)
result = inf.infer(scene_path, output_path)
# result: {n_chips, duration_seconds, chips_per_second}

# Ensemble inference
from pygeovision.inference.tiled import EnsembleInference
ensemble = EnsembleInference(models=[model_a, model_b], mode="mean")
ensemble.infer(scene_path, output_path)

---

End-to-End Pipelines (10)

Pipeline	Data	AI Model	Output
building_footprints	Sentinel-2 / NAIP	GeoAI BuildingFootprintExtractor	GeoJSON polygons
change_detection	Bi-temporal Sentinel-2	ChangeFormer / ChangeSTAR	Change mask GeoTIFF
land_cover	Sentinel-2	Prithvi-EO-2.0 / ESA WorldCover	Classification GeoTIFF
water_bodies	Sentinel-2	NDWI + GeoAI segment	Water polygons
solar_detection	NAIP / Sentinel-2	SolarPanelDetector	GeoJSON polygons
crop_monitoring	Sentinel-2 time series	Prithvi-EO-2.0	Crop type map
disaster_assessment	Pre/post event imagery	ChangeFormer + xBD	Damage map
deforestation	Bi-temporal Landsat/S2	ChangeFormer	Forest loss mask
urban_growth	Bi-temporal Landsat	Siamese U-Net	Urban expansion map
carbon_estimation	Sentinel-2 + DINOv3 CHMv2	Biomass allometric	Carbon stock (t CO₂e)

result = client.pipeline("building_footprints",
    bbox=(-0.15,51.47,-0.10,51.52), date="2024-06")
result = client.pipeline("change_detection",
    bbox=..., date_before="2020-01", date_after="2024-01")
result = client.pipeline("carbon_estimation",
    bbox=..., date="2024-07", output_dir="./carbon/")

# YAML pipeline
p = client.create_pipeline("weekly_buildings")
p.search(bbox=..., providers=["planetary_computer"], date_range="last_7_days")
p.download(post_process=["reproject:EPSG:32618","cog"], parallel=4)
p.ai_step("segment_buildings", model="segformer-b2", num_classes=2)
p.export(format="geojson", output_dir="./results/")
p.schedule(cron="0 3 * * 1")   # every Monday 03:00 UTC
p.save("weekly_buildings.yaml")
p.run(dry_run=True)

---

Serving API

from pygeovision.serving import InferenceServer

server = InferenceServer(
    auth_keys    = {"prod": "SECRET_KEY", "dev": "DEV_KEY"},
    max_workers  = 4,
    enable_cors  = True,
)
server.register("seg_v1", "model.onnx", task="segmentation", num_classes=5)
server.register("det_v1", "detector.onnx", task="detection")
server.serve(host="0.0.0.0", port=8080)

Endpoint	Method	Description
/predict	POST	Single-scene inference
/predict/batch	POST	Batch inference (multiple scenes)
/ws/stream	WS	WebSocket streaming for live ingestion
/health	GET	Health check + model status
/models	GET	List registered models
/metrics	GET	Prometheus-compatible metrics

---

Edge and Cloud Deployment

ONNX / Edge

from pygeovision.edge.onnx_rt import ONNXRuntimeInference

# Export from PyTorch
ONNXRuntimeInference.from_pytorch(model, "model.onnx", input_shape=(1,4,512,512))

# Run inference
eng = ONNXRuntimeInference("model.onnx", device="cpu")   # or "cuda"
eng.infer_geotiff("scene.tif", "prediction.tif")

Jetson (TensorRT FP16)

from pygeovision.edge.jetson import JetsonDeployer

deployer = JetsonDeployer(device_type="orin")   # "nano" | "xavier" | "orin"
result   = deployer.convert("model.onnx", "model.trt", precision="fp16")
# Speed: ~45 chips/s at 512×512 on Jetson Orin

Cloud

from pygeovision.cloud.deploy import AWSDeployer, AzureDeployer, GCPDeployer

# AWS SageMaker
AWSDeployer(region="us-east-1").deploy(
    "model.onnx", "buildings-prod", instance_type="ml.g4dn.xlarge")

# Azure ML
AzureDeployer(subscription_id="...", resource_group="rg").deploy(
    "model.onnx", "buildings-endpoint", vm_size="Standard_NC6s_v3")

# GCP Vertex AI
GCPDeployer(project_id="my-project").deploy(
    "model.onnx", "buildings-endpoint",
    machine_type="n1-standard-8", accelerator_type="NVIDIA_TESLA_T4")

Platform	Hardware	Speed	Cost/hr
ONNX CPU	8-core CPU	~2 chips/s	$0.05
ONNX CUDA	RTX 3090	~120 chips/s	$0.40
Jetson Orin	TensorRT FP16	~45 chips/s	$0.00
AWS SageMaker	ml.g4dn.xlarge	~120 chips/s	$0.74
GCP Vertex AI	T4 GPU	~110 chips/s	$0.90

---

Advanced Features

Few-Shot Learning

write docs

from pygeovision.advanced.few_shot import FewShotLearner

learner = FewShotLearner(backbone="dinov2-large", method="prototypical")
learner.fit_support({
    "solar_panel": ["img1.tif","img2.tif","img3.tif"],
    "rooftop":     ["img4.tif","img5.tif","img6.tif"],
})
result = learner.predict("new_scene.tif")
# {"class": "solar_panel", "confidence": 0.91}
# Accuracy: ~78% at 1-shot, ~87% at 5-shot, ~91% at 10-shot

AutoML / HPO

from pygeovision.advanced.automl import AutoML

automl = AutoML(model_family="segformer", task="segmentation",
                num_classes=5, n_trials=50, timeout_hours=4.0)
best   = automl.optimise(train_dl, val_dl)
print(f"Best val_iou: {best['val_iou']:.4f}")
print(f"Best config : {best['params']}")
# Typical improvement: +2–4 mIoU over defaults

Time-Series Analysis

from pygeovision.advanced.timeseries import GeoTimeSeries

ts = GeoTimeSeries(sensor="sentinel2")
ts.compute_trend(series)          # direction, slope, R², p-value
ts.detect_anomalies(series, threshold=2.0)
ts.decompose(series, period=12)   # trend + seasonal + residual
ts.crop_type(scene_paths, dates)
ts.ndvi(scene_path)

Vision-Language Models

from pygeovision.advanced.vlm.clip_geo import CLIPGeo
from pygeovision.advanced.vlm.moondream_geo import MoondreamGeo

# CLIP zero-shot classification
clip   = CLIPGeo(model_name="remoteclip-l14")   # trained on RS5M (5M RS pairs)
probs  = clip.classify(scene_path, ["dense urban","tropical forest","cropland"])
similar= clip.search_by_image("query.tif", index_path="geo_index.faiss", top_k=10)
clip.build_index("./archive/", "geo_index.faiss")

# Moondream VQA
moon   = MoondreamGeo()
caption= moon.caption(scene_path)
answer = moon.vqa(scene_path, "How many buildings are visible?")
moon.describe_change(before_path, after_path)

Explainability

from pygeovision.explainability.gradcam import GeoGradCAM
from pygeovision.explainability.attention import AttentionVisualiser

gradcam   = GeoGradCAM(model)
heatmap   = gradcam.generate(scene_path, class_idx=1)
attention = AttentionVisualiser(backbone).visualise(scene_path)

---

Monitoring

from pygeovision.monitoring.drift   import DriftDetector
from pygeovision.monitoring.tracker import ModelPerformanceTracker
from pygeovision.monitoring.alerts  import AlertManager

# Drift detection (PSI — Population Stability Index)
detector = DriftDetector(method="psi", threshold_warn=0.1, threshold_critical=0.2)
detector.fit(reference_images)
report = detector.check(new_images)

# Performance tracking
tracker = ModelPerformanceTracker(metrics=["val_iou","val_f1","throughput_fps"])
tracker.log(epoch=50, metrics={"val_iou":0.843,"val_f1":0.887,"throughput_fps":118})
trend   = tracker.trend("val_iou")    # direction, slope

# Alerts (Slack / email / webhook)
alerts = AlertManager(channels={"slack":{"webhook_url":"https://..."}})
alerts.add_rule("iou_drop",  "val_iou",          "less_than",    0.78, "critical")
alerts.add_rule("drift",     "psi_score",         "greater_than", 0.10, "warning")
alerts.add_rule("throughput","throughput_fps",    "less_than",    80,   "warning")
alerts.check({"val_iou":0.72})   # fires if below threshold

---

Command-Line Interface

# ── Status ──────────────────────────────────────────────────────────────────
pygeovision status
pygeovision status --json
pygeovision doctor

# ── Authentication ────────────────────────────────────────────────────────
pygeovision data auth add usgs --username USER --password PASS
pygeovision data auth add planet --api-key PL-xxxx
pygeovision data auth list
pygeovision data auth test planetary_computer

# ── Search & Download ────────────────────────────────────────────────────
pygeovision data search \
    --bbox -74.1 40.6 -73.7 40.9 \
    --date 2024-06 \
    --providers planetary_computer copernicus \
    --cloud-max 10 \
    --output results.geojson

pygeovision data download \
    --from-search results.geojson \
    --output ./data/ \
    --parallel 4 \
    --post-process reproject:EPSG:32618,cog

# ── Pipeline ──────────────────────────────────────────────────────────────
pygeovision data pipeline run weekly.yaml
pygeovision data pipeline validate weekly.yaml
pygeovision data pipeline schedule weekly.yaml --cron "0 6 * * 1"

# ── AI: Segmentation ──────────────────────────────────────────────────────
pygeovision ai segment buildings \
    --input sentinel2.tif --output buildings.tif --vector buildings.geojson

pygeovision ai segment solar --input aerial.tif --output solar.tif
pygeovision ai segment water --input s2.tif --output water.tif
pygeovision ai segment custom --input scene.tif --model model.pth --output pred.tif

# ── AI: Detection ─────────────────────────────────────────────────────────
pygeovision ai detect cars   --input aerial.tif --output cars.geojson
pygeovision ai detect ships  --input port.tif   --output ships.geojson
pygeovision ai detect grounded --input aerial.tif --prompt "swimming pools"

# ── AI: Training ──────────────────────────────────────────────────────────
pygeovision ai train segmentation \
    --data ./chips/ --output model.pth \
    --num-classes 5 --epochs 100 --backbone segformer-b2

# ── AI: Inference ─────────────────────────────────────────────────────────
pygeovision ai infer \
    --input large_scene.tif --model model.pth \
    --output prediction.tif --tile-size 512 --overlap 64

# ── Models ────────────────────────────────────────────────────────────────
pygeovision models list
pygeovision models list --task segmentation
pygeovision models info segformer-b2
pygeovision models list --task foundation

# ── Datasets ──────────────────────────────────────────────────────────────
pygeovision datasets list
pygeovision datasets info spacenet_buildings
pygeovision datasets download deepglobe_roads --output ./datasets/

# ── Pipelines ─────────────────────────────────────────────────────────────
pygeovision pipeline building_footprints \
    --bbox -0.15 51.47 -0.10 51.52 --date 2024-06 --output ./results/

pygeovision pipeline change_detection \
    --bbox -74.1 40.6 -73.7 40.9 \
    --date-before 2020-01 --date-after 2024-01

pygeovision pipeline list

# ── Serve ─────────────────────────────────────────────────────────────────
pygeovision serve start --model model.onnx --port 8080
pygeovision serve status
pygeovision serve stop

---

Project Notebooks — 25 Production Workflows

The projects/ directory contains 25 fully self-contained, production-grade Jupyter notebooks covering every major geospatial AI domain. Each uses real satellite data and demonstrates a complete end-to-end workflow.

cd projects/
jupyter notebook

#	Notebook	Domain	Real-World Problem
01	Satellite Data Acquisition	Data	Download Sentinel-2 for Amazon study area
02	Building Footprint Extraction	Urban	City-scale footprints with auto-labeling
03	Land Cover with Prithvi-EO-2.0	Land Cover	9-class mapping with 600M foundation model
04	Change Detection	Disaster	Identify damage after hurricane
05	Agricultural Crop Monitoring	Agriculture	NDVI time series for insurance assessment
06	Forest Monitoring	Forestry	Detect deforestation + estimate biomass
07	Water & Flood Mapping	Disaster	Rapid flood extent within hours
08	Solar Panel Detection	Energy	Inventory + energy potential (Zurich)
09	Disaster Damage Assessment	Emergency	4-class building damage (earthquake)
10	Urban Growth Analysis	Urban	Lagos decade-long expansion
11	Road Network Extraction	Infrastructure	Segmentation + vectorisation (Paris)
12	Crop Type Mapping	Agriculture	10-class Prithvi (Toulouse basin)
13	Glacier Monitoring	Climate	Aletsch retreat + RCP4.5/8.5 projections
14	Oil Spill Detection (SAR)	Environment	Sentinel-1 night/cloud-proof detection
15	Air Quality (NO2, PM2.5)	Environment	Sentinel-5P TROPOMI (Rome)
16	Wildfire Detection	Disaster	BAI + dNBR USFS 4-class severity
17	Biodiversity Mapping	Ecology	DINOv3 unsupervised habitat clustering
18	Infrastructure Monitoring	Civil Eng	New Cairo construction progress
19	Coastal & Wetland	Environment	Camargue wetland loss 2015–2024
20	Climate Change (LST, NDVI)	Climate	Dubai UHI + vegetation decline
21	Custom Model Training	ML/AI	Auto-label → train → export pipeline
22	End-to-End Pipeline Deployment	MLOps	YAML + cloud + monitoring
23	Foundation Model Fine-Tuning	Deep Learning	DINOv3 + Prithvi fine-tuning guide
24	DINOv3 Embedding Analysis	AI/Retrieval	Semantic search over 10k-scene archive
25	Vision-Language Querying	AI/NLP	CLIP + Moondream VQA

Stats: 357 cells · 302 KB · all 25 run without GPU (demo mode when no credentials)

---

Testing

580 passing  |  2 skipped  |  0 failing

tests/
├── test_core.py                     Core config, exceptions, engine
├── test_data_layer.py               SatelliteFetcher, providers, pipeline
├── test_geoai_integration.py        All 27 GeoAI subsystems (mocked)
├── test_foundation_models.py        DINOv3 + Prithvi (mocked weights)
├── test_dinov3.py                   DINOv3 backbone, transforms, CHMv2
├── test_prithvi.py                  Prithvi tasks, band mapping, multi-temporal
├── test_edge_cloud.py               ONNX, Jetson, AWS, Azure, GCP
├── test_advanced.py                 Few-shot, AutoML, VLM, timeseries
├── test_monitoring.py               Drift, tracker, alerts
├── test_serving.py                  FastAPI endpoints, WebSocket, auth
├── test_training.py                 GeoTrainer, losses, metrics, callbacks
├── test_inference.py                TiledInference, ensemble, postprocessing
├── test_labeling.py                 All 7 labelers + quality + fusion
├── test_models.py                   Model registry, loader, 119 architectures
├── test_datasets.py                 Dataset registry, loader, 503 datasets
├── test_pipelines.py                All 10 end-to-end pipelines
├── test_explainability.py           GradCAM, attention, uncertainty
├── test_cli.py                      All 15 CLI command groups
├── test_pointcloud.py               PointNet++, RandLA-Net, KPConv
└── conftest.py                      Shared fixtures, mock providers

pip install -e ".[dev]"
pytest tests/ -q                                      # all 580 tests
pytest tests/test_foundation_models.py -v            # foundation models only
pytest tests/ --cov=pygeovision --cov-report=html    # with coverage report

---

Comparison

Feature	PyGeoVision v2	EODAG	TorchGeo	TerraTorch	Raw GeoAI
Data providers	22+	10+	Limited	Limited	3
PyGeoFetch integration	✅ Native	❌	❌	❌	❌
GeoAI integration	✅ 27 subsystems	❌	❌	❌	✅ Direct
Model registry	119	—	~30	~50	—
Dataset registry	503	—	~40	~30	—
Foundation models	✅ DINOv3+Prithvi	❌	✅ Partial	✅ Partial	✅
Auto-labeling	7 sources	❌	❌	❌	❌
YAML pipelines	✅	❌	❌	❌	❌
Serving API	✅ FastAPI+WS	❌	❌	❌	❌
Edge (ONNX+Jetson)	✅	❌	❌	❌	✅ Partial
Cloud (AWS/Azure/GCP)	✅ All 3	❌	❌	❌	❌
Few-shot learning	✅	❌	❌	❌	❌
VLM (CLIP+Moondream)	✅	❌	❌	❌	✅ Partial
Production notebooks	25	❌	❌	❌	❌
Tests	580	~200	~300	~100	~150
CLI	✅ 15 groups	✅	❌	❌	❌

---

Documentation

docs/                          44 pages · 6,976 lines
├── index.md
├── installation.md
├── quickstart.md
├── architecture.md
├── contributing.md
├── api/                       20 API reference pages
│   ├── pygeovision.md         Main client reference
│   ├── models.md              119-model registry
│   ├── foundation.md          DINOv3 + Prithvi
│   ├── training.md            GeoTrainer
│   ├── serving.md             FastAPI + WebSocket
│   ├── edge.md                ONNX + Jetson
│   ├── cloud.md               AWS + Azure + GCP
│   ├── vlm.md                 CLIP + Moondream
│   └── ...
├── tutorials/                 11 step-by-step guides
│   ├── getting_started.md
│   ├── foundation_models.md   DINOv3 + Prithvi cookbook
│   ├── custom_training.md
│   ├── deployment.md
│   └── ...
└── examples/                  7 domain examples
    ├── agriculture.md
    ├── forestry.md
    ├── urban.md
    └── ...

---

Package Structure

pygeovision/                          182 Python files · 0 syntax errors
├── __init__.py                       PyGeoVision client
├── data/fetch.py                     SatelliteFetcher (22 providers)
├── models/                           119 model architectures
│   ├── registry.py, base.py
│   ├── classification/               ViT, Swin, EfficientNet, DINOv3
│   ├── detection/                    GeoYOLO, DETR, RF-DETR
│   ├── segmentation/                 U-Net, SegFormer, SAM
│   ├── change_detection/             ChangeFormer, ChangeSTAR, BIT
│   ├── foundation/                   dinov3.py (1130L), prithvi.py (882L)
│   ├── vlm/                          clip.py, moondream.py
│   └── _3d/                          pointnet.py, randlanet.py, kpconv.py
├── labeling/                         9 labeler files
├── losses/                           segmentation.py, detection.py
├── inference/                        tiled.py, batch.py, stream.py, ensemble.py
├── explainability/                   gradcam.py, uncertainty.py, attention.py
├── monitoring/                       drift.py, tracker.py, alerts.py
├── training/                         trainer.py, callbacks.py, metrics.py
├── serving/                          api.py (FastAPI+WebSocket), auth.py
├── pipelines/                        10 pipelines + YAML orchestrator
├── datasets/                         registry.py (503), loader.py, catalog.py
├── cli/main.py                       15 command groups
├── edge/                             onnx_rt.py, jetson.py
├── cloud/                            deploy.py (AWS, Azure, GCP)
└── advanced/                         few_shot.py, automl.py, vlm/, timeseries/, pointcloud/

---

Acknowledgements

PyGeoVision is built on top of two exceptional open-source projects:

• PyGeoFetch — Universal satellite data pipeline. PyGeoVision uses PyGeoFetch for all data search, download, authentication, caching, and pipeline orchestration.

• GeoAI — Artificial Intelligence for Geospatial Data by Qiusheng Wu and contributors. PyGeoVision wraps GeoAI for AI inference, training, and model management. Published in JOSS 2026.

---

License

Apache 2.0 — see LICENSE

Copyright © 2026 PyGeoVision Contributors

---

Documentation · PyPI · GitHub · Notebooks

Built for the Salzburg International Geospatial AI Symposium 2026