opengeos/segment-geospatial
Segment Geospatial
Segment Geospatial ist ein Python-Toolkit zur Isolierung geografischer Merkmale in Fernerkundungsbildern unter Verwendung des Segment Anything Model. Es fungiert als Fernerkundungs-Bildprozessor, der Kartenkacheln in georeferenzierte Formate konvertiert, um Segmentierungsmasken aus Satellitendaten zu generieren.
Das System ermöglicht die Extraktion geografischer Objekte durch automatische Maskengenerierung oder manuelle Prompts, wie Textbeschreibungen, Bounding-Boxes und interaktive Marker. Es unterstützt die Segmentierung von Zeitreihenbildern, um Objekte über Sequenzen von Bildern an verschiedenen Daten hinweg zu verfolgen oder zu identifizieren, und bietet einen Geodaten-Masken-Visualisierer für das Rendern von Ergebnissen auf interaktiven Karten.
Das Projekt deckt ein breites Spektrum an räumlichen Operationen ab, einschließlich Kartenkachel-Akquise, Raster-zu-Vektor-Konvertierung und Feature-Edge-Rekonstruktion zur Verfeinerung von Objektgrenzen. Es enthält zudem eine REST-API, die diese Segmentierungs- und Datenverarbeitungsfunktionen für Remote-Anwendungen bereitstellt.
Exportfunktionen unterstützen georeferenzierte Rasterbilder und Standard-Vektorformate, einschließlich GeoJSON, Shapefile und GeoPackage.
Features
- Geospatial Imagery Segmentation - Isolates specific geographic features in satellite and aerial imagery using the Segment Anything Model to create georeferenced masks.
- SAM-Based Implementations - Uses the Segment Anything Model (SAM) to generate high-quality binary masks from text, bounding box, or point prompts.
- Automatic Mask Generators - Produces object masks across an entire image automatically without requiring manual user input or prompts.
- Raster Export - Saves segmentation masks as spatial imagery files that maintain a coordinate reference system.
- Geospatial Adaptations - Adapts the Segment Anything Model specifically for isolating geographic features within remote sensing imagery.
- Prompt-Based Semantic Segmentations - Uses a pre-trained Segment Anything Model to generate semantic masks from bounding boxes or text descriptions.
- Remote Sensing Machine Learning - Analyzes sequences of satellite imagery over multiple dates to track geographic objects and identify changes over time.
- Prompt-Based Segmentations - Identifies map objects using text descriptions, bounding boxes, or interactive foreground and background markers.
- Timeseries Analysis - Analyzes sequences of remote sensing images over multiple dates to track geographic objects across time.
- Object Tracking - Identifies and tracks specific geographic objects across sequences of remote sensing images over different dates.
- Map Tile Downloaders - Fetches map imagery from remote tile services and converts the data into GeoTIFF files for local processing.
- Georeferenced Format Converters - Converts map tiles from remote services into georeferenced formats to enable automated object detection.
- Georeferenced Raster Transformations - Converts raw map tiles into GeoTIFF formats to maintain coordinate reference systems during the segmentation process.
- Raster-to-Vector Conversions - Transforms pixel-based segmentation masks into georeferenced vector formats like GeoJSON and Shapefiles.
- Batch Mask Generation - Generates multiple segmentation masks in a single execution cycle using collections of point coordinates from vector layers.
- Segmentation APIs - Provides a REST API to expose remote sensing segmentation and geospatial data processing capabilities to remote clients.
- Geospatial Boundary Refinements - Applies a feature edge reconstruction algorithm to refine the boundaries of segmented geospatial objects.
- Geospatial Visualizations - Provides an interactive mapping interface to render and verify segmentation masks using geographic coordinates.
- REST API Interfaces - Provides a server-side REST API to expose geospatial segmentation and data processing functions to remote clients.
- Geospatial Mask Visualizers - Ships an interactive mapping interface for rendering and verifying the spatial accuracy of extracted segmentation masks.
- RESTful Services - Exposes geospatial segmentation and data processing functions through a stateless RESTful service layer.
- Computer Vision and Image Processing - Geospatial data segmentation using foundation models.
- Geospatial Machine Learning - Adapts foundation models for geospatial segmentation.
