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+91 88513 31380
We Offer Revolution Of Geospatial Engineering
+91 88513 31380
Apex supports the development of intelligent infrastructure, autonomous mobility, and spatial analytics platforms through specialized geospatial annotation and GeoAI data services.
We create reliable training datasets through structured processes designed for machine learning, computer vision, and geospatial AI applications, ensuring consistency, accuracy, and scalability.
Our geospatial annotation services generate high-accuracy training data for AI and machine learning by extracting and labeling information from imagery, LiDAR, and spatial datasets.
Object annotation using satellite, aerial, and street-level imagery
Labeling of roads, buildings, utilities, and infrastructure-related assets
Pixel-level image segmentation and feature classification
Development of training datasets for computer vision and AI applications
Quality-assured manual and semi-automated annotation processes
Typical Inputs:
Satellite data, aerial captures, street-level visuals, LiDAR point clouds
Typical Outputs:
Tagged datasets, labeled visuals, segmentation outputs, AI-ready training datasets
We focus on labeling and categorizing point cloud datasets to enable AI-based analysis and automation across infrastructure and environmental use cases.
Key Services:
Typical Inputs:
LiDAR point clouds (LAS/LAZ), mobile mapping (MLS) data, imagery
Typical Outputs:
Segmented point clouds, annotated datasets, AI-ready training data
We develop structured, high-quality datasets designed for training, validation, and testing of GeoAI and computer vision models.
Typical Inputs:
Annotated source data, imagery, point clouds, GIS layers
Typical Outputs:
Training, validation, and test datasets with documentation
Geospatial features are derived and organized to support automated detection, classification, and analysis through AI models.
Typical Inputs:
Imagery, LiDAR data, GIS layers
Typical Outputs:
Vector datasets, structured feature layers, enriched spatial data
We maintain high accuracy and consistency in annotated datasets through robust QA processes, supporting improved model performance.
Typical Inputs:
Annotated datasets, model outputs, validation data
Typical Outputs:
Quality-checked datasets, QA reports, refined training data
