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Geological structural index

Geological structural index

Radar based mapping to identify faults and lineaments. Effective for areas covered by thin to medium vegetation.

Outcrop index

Outcrop index

Outcrop exposures on a survey area applying machine learning to high resolution multispectral satellite imagery.

Mineral mapping index

Mineral mapping index

Spectral mapping (VNIR + SWIR) of iron oxide, alteration clay, and pegmatite features. Interpretation of signature distributions and identification of priority targets.

Water quality index

Water quality index

Water quality variables are derived from hyper- and multi-spectral optical satellite imagery using leading-edge machine learning models.

Water runoff index

Water runoff index

Produced by merging multiple layers of information about the AOI, including precipitation, soil types, DEM, and evapotranspiration

Snow depth index

Snow depth index

Snow depth measurement processed from SAR satellite images.

Soil moisture index

Soil moisture index

By applying Neural Network models to a combination of SAR-derived and numerically-modeled soil moisture data, we produce a soil moisture layer with high accuracy over different land cover, soil, and crop types.

Precipitation index

Precipitation index

Climate variables (e.g. precipitation and air temperature), produced from numerical models, using government-hosted datasets.

Deformation index

Deformation index

InSARderived deformation velocity used to produce an alerting system. This measure enables proactive planning to mitigate environmental hazards.

Digital elevation models:

Digital elevation models:

High-resolution DEM generated from InSAR processing.

Radar based mapping to identify faults and lineaments. Effective for areas covered by thin to medium vegetation.

Geological structural index

Geological structural index

Outcrop exposures on a survey area applying machine learning to high resolution multispectral satellite imagery.

Outcrop index

Outcrop index

Spectral mapping (VNIR + SWIR) of iron oxide, alteration clay, and pegmatite features. Interpretation of signature distributions and identification of priority targets.

Mineral mapping index

Mineral mapping index

Water quality variables are derived from hyper- and multi-spectral optical satellite imagery using leading-edge machine learning models.

Water quality index

Water quality index

Produced by merging multiple layers of information about the AOI, including precipitation, soil types, DEM, and evapotranspiration

Water runoff index

Water runoff index

Snow depth measurement processed from SAR satellite images.

Snow depth index

Snow depth index

By applying Neural Network models to a combination of SAR-derived and numerically-modeled soil moisture data, we produce a soil moisture layer with high accuracy over different land cover, soil, and crop types.

Soil moisture index

Soil moisture index

Climate variables (e.g. precipitation and air temperature), produced from numerical models, using government-hosted datasets.

Precipitation index

Precipitation index

InSARderived deformation velocity used to produce an alerting system. This measure enables proactive planning to mitigate environmental hazards.

Deformation index

Deformation index

High-resolution DEM generated from InSAR processing.

Digital elevation models:

Digital elevation models:

Custom

AI Solutions

Solve the most pressing mapping and monitoring problems using our end-to-end consulting services

AI Feasibility Study

  • Brainstorming possibilities and assessing the feasibility from business and technical standpoint.  

 ​

  • Identify the project under Assistive, Augmented or/and  Autonomous Intelligence

 ​

  • Conduct business, technical assessment and define acceptance criteria  

Structure pilot project 

  • User-problem mapping: Define current situation, problem, desired situation, success criteria, & pilot project scope ​

  • Exploratory data analysis ​

    • Data availability, data cleaning, data quality checks & study of distribution ​

    • Evaluate the data collection systems and infrastructure 

Conduct the pilot project

  • Explore potential solutions ​

  • Reiterate  ​

    • Data & feature engineering ​

    • Model development ​

    • Model accuracy and validation ​

  • Continuous feedback from domain experts 

Validate value & findings

  • Establish technical performance & business value achievement

  ​

  • Gain acceptance by participating teams and domain experts​

  • Engage decision makers on integration/roll out plan

Production deployment

  • Determine technology stack, data requirements and deployment infrastructure (cloud or on-premise) ​

  • Evaluate ease of integration and plan roll out ​

  • Establish internal and external AI team and stakeholders​

  • Provide continuous support and improvements   

Custom

AI Solutions

Solve the most pressing mapping and monitoring problems using our end-to-end consulting services

AI Feasibility Study

  • Brainstorming possibilities and assessing the feasibility from business and technical standpoint.  

 ​

  • Identify the project under Assistive, Augmented or/and  Autonomous Intelligence

 ​

  • Conduct business, technical assessment and define acceptance criteria  

Structure pilot project 

  • User-problem mapping: Define current situation, problem, desired situation, success criteria, & pilot project scope ​

  • Exploratory data analysis ​

    • Data availability, data cleaning, data quality checks & study of distribution ​

    • Evaluate the data collection systems and infrastructure 

Conduct the pilot project

  • Explore potential solutions ​

  • Reiterate  ​

    • Data & feature engineering ​

    • Model development ​

    • Model accuracy and validation ​

  • Continuous feedback from domain experts 

Validate value & findings

  • Establish technical performance & business value achievement

  ​

  • Gain acceptance by participating teams and domain experts​

  • Engage decision makers on integration/roll out plan

Production deployment

  • Determine technology stack, data requirements and deployment infrastructure (cloud or on-premise) ​

  • Evaluate ease of integration and plan roll out ​

  • Establish internal and external AI team and stakeholders​

  • Provide continuous support and improvements   

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