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  • Deep learning geospatial training/test data collection

  • Object detection using machine learning

  • Compute accuracy for object detection

  • Image classification using deep learning

  • Image labeling

  • Classify pixels using deep learning

  • Semantic segmentation using AI machine learning

  • Instance segmentation using Artificial Intelligence

  • Change detection using machine learning (deep learning)

  • Training of deep learning models

  • Analyze data using Models

  • Generate analysis reports

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Artificial Intelligence | Machine Learning for Geospatial Data

We can help with
  • Deep learning geospatial training/test data collection

  • Object detection using machine learning

  • Compute accuracy for object detection

  • Image classification using deep learning

  • Image labeling

  • Classify pixels using deep learning

  • Semantic segmentation using AI machine learning

  • Instance segmentation using Artificial Intelligence

  • Change detection using machine learning (deep learning)

  • Training of deep learning models

  • Analyze data using Models

  • Generate analysis reports

With a large amount of data, more computing power, and advancement in algorithms, AI/ML can be used for land survey classification and monitoring. This makes locating impervious surfaces, change detection, agriculture, road and road networks, construction monitoring, real estate (ex. damaged roof detection), building maintenance, Wind turbines, solar farm autonomous inspection, object identification and tracking (ships, cars, fleet, delivery vans or trucks). 3D modeling and DTM/DSM mapping can also be enhanced with AI and machine learning.

There are a wide range of applications for machine learning with geospatial data, including:


Land cover and land use classification: Machine learning algorithms can be used to classify different types of land cover, such as forests, grasslands, and urban areas, based on features extracted from satellite imagery or other types of geospatial data.


Object detection and tracking: Machine learning algorithms can be used to detect and track objects in geospatial data, such as vehicles, buildings, or natural features like rivers and lakes.


Spatial prediction and modeling: Machine learning algorithms can be used to make predictions about spatial patterns and trends, such as predicting the spread of a disease based on geospatial data.


Geocoding and address matching: Machine learning algorithms can match addresses to geographic coordinates or geocode unstructured data (such as text descriptions of locations) into structured data with geographic coordinates.


Geospatial data visualization: Machine learning algorithms can be used to visualize and analyze geospatial data in ways that highlight patterns and trends, such as creating heat maps or choropleth maps.


Our team is equipped with advanced infrared, high definition RGB images, multispectral, and thermal sensors which help carry out effective training and test data collection. Our team of experts along with AI-based software tools add unmatched value to your required model and analysis.


We welcome your inquiries about our machine learning/ deep learning capabilities. 


"Predicting the future isn't magic, it's artificial intelligence"

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