Make Aerial Data Actionable Insights

Gain business intelligence from your data with a suite of automated aerial data analytics housed in PrecisionMapper

Analytics Line-up

PrecisionHawk's smarter industry packages include analytics tailored to your industry.

  • Orthomosaic

    Orthomosaic

    Orthomosaic

    Output Options

    2D Map Processing GeoTiff, KML Tile Set

    3D Map File Outputs Point Cloud - .las, Triangle Mesh

    Contour Map File Output DSM

    Supported Sensors: RGB, BGNIR, Thermal, RGNIR, NIR, Thermal-IR
    Supported Resolution: 500cm/px or less
    Estimated Processing: 0.5-3 Hours
    Orthomosaic Algorithm Output Orthomosaic Algorithm Output Orthomosaic Algorithm Output

    Description

    Orthomosaic generation is the automated process for Orthorectifing the raw imagery and mosaicing them into one single image. This process will generate a Georeferenced Image and optionally a Digital surface model in various different formats.
  • Enhanced Normalized Difference Vegetation Index

    Enhanced Normalized Difference Vegetation Index

    Enhanced Normalized Difference Vegetation Index
    Supported Sensors: BGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Enhanced Normalized Difference Vegetation Index Algorithm Output Enhanced Normalized Difference Vegetation Index Algorithm Output Enhanced Normalized Difference Vegetation Index Algorithm Output

    Description

    ENDVI is a close equivalent and modified version of NDVI, designed for low altitude monitoring systems, such as UAVs. ENDVI is an indicator of live green vegetation, and can be used for crops in all growth stages.
  • Green Normalized Difference Vegetation Index

    Green Normalized Difference Vegetation Index

    Green Normalized Difference Vegetation Index
    Supported Sensors: BGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Green Normalized Difference Vegetation Index Algorithm Output Green Normalized Difference Vegetation Index Algorithm Output Green Normalized Difference Vegetation Index Algorithm Output

    Description

    GNDVI is a modified version of the NDVI to be more sensitive to the variation of chlorophyll content in the crop. It is useful for assessing the canopy variation in biomass, and is an indicator of senescence in case of stress or late maturity stage. This index can be used to analyze crops in mid to late growth stages.
  • Difference Vegetation Index

    Difference Vegetation Index

    Difference Vegetation Index
    Supported Sensors: RGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Difference Vegetation Index Algorithm Output Difference Vegetation Index Algorithm Output Difference Vegetation Index Algorithm Output

    Description

    DVI is a simple vegetation index, and distinguishes between the soil and vegetation. This index can be used for crops in all growth stages.
  • Field Uniformity Tool

    Field Uniformity Tool

    Field Uniformity Tool
    Supported Sensors: BGNIR, RGNIR, RGB
    Supported Resolution: 5cm/px or less
    Estimated Processing: 24 Hours
    Field Uniformity Tool Algorithm Output Field Uniformity Tool Algorithm Output Field Uniformity Tool Algorithm Output

    Description

    Quantify plot-level statistics

    The Field Uniformity Tool makes it possible to quantify plot-level statistics on plant count, height, vigor, leaf area and canopy cover. Drawing data from your other licensed algorithms (Row-Based Plant Counting Tool, Plant Height, Canopy Cover, Leaf Area and Vegetation Indices) , it calculates the maximum, minimum, mean and standard deviation for each plot or user-defined grid cell.

    iThe tool requires that at least one of the following algorithms be applied: any Vegetation Index, Canopy Cover, Plant Height, and/or Plant Counting

  • Green Difference Vegetation Index

    Green Difference Vegetation Index

    Green Difference Vegetation Index
    Supported Sensors: BGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Green Difference Vegetation Index Algorithm Output Green Difference Vegetation Index Algorithm Output Green Difference Vegetation Index Algorithm Output

    Description

    GDVI was designed to predict nitrogren requirements for corn. This index is recommended to analyze crops in early to mid growth stages.
  • Optimized Soil Adjusted Vegetation Index

    Optimized Soil Adjusted Vegetation Index

    Optimized Soil Adjusted Vegetation Index
    Supported Sensors: RGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Optimized Soil Adjusted Vegetation Index Algorithm Output Optimized Soil Adjusted Vegetation Index Algorithm Output Optimized Soil Adjusted Vegetation Index Algorithm Output

    Description

    OSAVI is a simplified version of SAVI to minimize the influence of soil brightness. This index is recommended to analyze crops in early to mid growth stages, in areas with relatively sparse vegetation where soil is visible through the canopy.
  • Renormalized Difference Vegetation Index

    Renormalized Difference Vegetation Index

    Renormalized Difference Vegetation Index
    Supported Sensors: RGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Renormalized Difference Vegetation Index Algorithm Output Renormalized Difference Vegetation Index Algorithm Output Renormalized Difference Vegetation Index Algorithm Output

    Description

    RDVI uses advantages of the DVI and NDVI, and is insensitive to the effects of soil and sun viewing geometry. This index is not recommended for surveys with sparse vegetation. This index can be used to analyze crops in all growth stages.
  • Normalized Difference Vegetation Index

    Normalized Difference Vegetation Index

    Normalized Difference Vegetation Index
    Supported Sensors: RGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Normalized Difference Vegetation Index Algorithm Output Normalized Difference Vegetation Index Algorithm Output Normalized Difference Vegetation Index Algorithm Output

    Description

    NDVI is the most common vegetation index, and is used to detect live, green, or photosynthetic capacity of plant canopies in multispectral remote sensing data. It is one of the most successful indices to quickly identify vegetated areas and their condition. This index can be used to analyze crops in all growth stages.
  • Soil Adjusted Vegetation Index

    Soil Adjusted Vegetation Index

    Soil Adjusted Vegetation Index
    Supported Sensors: RGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Soil Adjusted Vegetation Index Algorithm Output Soil Adjusted Vegetation Index Algorithm Output Soil Adjusted Vegetation Index Algorithm Output

    Description

    SAVI was developed as a modification of the NDVI to correct for the influence of soil brightness. This index is recommended to analyze crops in early or mid growth stages. Early growth stage analysis is recommended when there are clearly separated rows or plants and where soil is very apparent. Mid season growth stage analysis is recommended when plants are not touching, are in separated rows, and where the canopies make a uniform shadow.
  • Canopy Cover

    Canopy Cover

    Canopy Cover
    Supported Sensors: RGB, BGNIR, RGNIR
    Supported Resolution: 5cm/px or less
    Estimated Processing: Less than 2 hours
    Canopy Cover Algorithm Output

    Description

    Quantify plot-level statistics

    Canopy Cover mapping tool is based of a robust vegetation index and provides an accurate delineation of vegetated area. From the canopy cover, it is possible to quantify the percentage of foliar cover per area (plots, fields). The produced layer is best used in a GIS to generate vegetation cover statistics.

  • Green Leaf Index

    Green Leaf Index

    Green Leaf Index
    Supported Sensors: RGB
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Green Leaf Index Algorithm Output Green Leaf Index Algorithm Output Green Leaf Index Algorithm Output

    Description

    GLI was designed to adjust for the greeness and yellowness of the crop, and was designed for low altitude monitoring systems, such as UAVs. This index can be used for crops in all growth stages.
  • Green Soil Adjusted Vegetation Index

    Green Soil Adjusted Vegetation Index

    Green Soil Adjusted Vegetation Index
    Supported Sensors: BGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Green Soil Adjusted Vegetation Index Algorithm Output Green Soil Adjusted Vegetation Index Algorithm Output Green Soil Adjusted Vegetation Index Algorithm Output

    Description

    GSAVI was designed to correct for the influence of soil brightness. This index is recommended to analyze crops in early to mid growth stages, when vegetation is sparse and soil is visible through the canopy.
  • Visual NDVI

    Visual NDVI

    Visual NDVI
    Supported Sensors: RGB
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Visual NDVI Algorithm Output Visual NDVI Algorithm Output Visual NDVI Algorithm Output

    Description

    Visual NDVI, also known as NGRDI, is an indicator of surface greenness and it is an index to detect live green plant canopies. This index can be used to analyze crops in all growth stages.
  • Visible Atmospherically Resistant Index

    Visible Atmospherically Resistant Index

    Visible Atmospherically Resistant Index
    Supported Sensors: RGB
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Visible Atmospherically Resistant Index Algorithm Output Visible Atmospherically Resistant Index Algorithm Output Visible Atmospherically Resistant Index Algorithm Output

    Description

    VARI was designed to introduce an atmospheric correction and is a good index to estimate the vegetation fraction from the visible range of the spectrum. This index can be used to analyze crops in all growth stages.
  • Volume Measurement

    Volume Measurement

    Volume Measurement
    Supported Sensors: All
    Supported Resolution: 5cm/px or less
    Estimated Processing: Less than 2 hours
    Volume Measurement Algorithm Output Volume Measurement Algorithm Output

    Description

    The Volume Measurement algorithm automatically calculates cut-fill volume for areas of interest delineated by the user. It takes a digital surface model and an area of interest (AOI) shapefile as inputs and generates a GIS vector file (shapefile) and a KML file showing the user-defined piles along with their respective cut, fill and total volume.

    The method uses accounts for the base terrain slope. Unlike approaches that assume a flat and horizontal base, this volume measurement interpolates the base height from the elevation under the vertices of the area of interest delineated by the user. The cut and fill values are calculated based on this interpolated base terrain height instead of simply calculating it from the lowest height (flat/horizontal).

    This approach provides good results when the AOI is delineated directly on the base terrain. It is not recommended to attempt isolating the volumes from closely adjacent piles or piles adjacent to a cliff.

  • Row Based Plant Counting

    Row Based Plant Counting

    Row Based Plant Counting
    Supported Sensors: RGB
    Supported Resolution: 2.5cm/px or less
    Estimated Processing: Less than 4 hours
    Row Based Plant Counting Algorithm Output Row Based Plant Counting Algorithm Output

    Description

    Working from high-resolution images of post-emergence crops and employing innovative rules-based reasoning, the Row-Based Plant Counting Tool is able to deliver accurate plant counts from your survey. This algorithm is only intended to be used on straight rows, and assumes a single, constant row orientation identified by user input. Rows with a different orientation angle should be considered as separate AOIs.
  • ScoutView Report

    ScoutView Report

    ScoutView Report
    Supported Sensors: All
    Supported Resolution: 1000cm/px or less
    Estimated Processing: Less than 4 hours
    ScoutView Report Algorithm Output ScoutView Report Algorithm Output

    Description

    Get a snapshot of your survey. ScoutView allows you to create and retain an intuitive single-page report of your aerial survey. Site-specific weather information is included to provide you a record of conditions the day of and week before your survey.
  • Waterpooling

    Waterpooling

    Waterpooling
    Supported Sensors: BGNIR
    Supported Resolution: 100cm/px or less
    Estimated Processing: Less than 2 hours
    Waterpooling Algorithm Output Waterpooling Algorithm Output

    Description

    Identify standing water in pre-emergent agriculture fields

    Using only high-resolution imagery from your NIR modified sensor, areas of standing water in agriculture fields can be accurately identified and measured. This algorithm was developed to work in pre-emergent agricultural fields and quantify areas that cannot be planted due to standing water. Additional uses could include determining flood damage immediately after an extreme rain event or monitoring water levels of permanent water features.

    Users should be aware that outside of pre-emergent agriculture conditions there is potential to misclassify non-water features as standing water. Typically, dark surfaces (shadows, asphalt, etc.) can be confused with water in this algorithm.

  • Tree Crown Delineation

    Tree Crown Delineation

    Tree Crown Delineation
    Supported Sensors: BGNIR, RGB, RGNIR
    Supported Resolution: 5cm/px or less
    Estimated Processing: 10 - 12 hours
    Tree Crown Delineation Algorithm Output Tree Crown Delineation Algorithm Output Tree Crown Delineation Algorithm Output

    Description

    Tree Crown Delineation automatically identifies individual tree crowns in the survey. It also generates a geospatial layer of tree crowns with overall health level, crown diameter, as well as the mean vegetation index values.

    Index values calculated include:

    • RGB (GLI, VARI, NGRDI)
    • BGNIR (ENDVI, GNDVI, GSAVI)
    • RGNIR (NDVI, MNLI, OSAVI)
    • Micasense (MTVI-1, LAI, GARI)

  • Triangular Greenness Index

    Triangular Greenness Index

    Triangular Greenness Index
    Supported Sensors: RGB
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Triangular Greenness Index Algorithm Output Triangular Greenness Index Algorithm Output Triangular Greenness Index Algorithm Output

    Description

    This is a simple triangular greenness index for sensing leaf chlorophyll content based on true color imagery. The index was the result of a study to create an index that is sensitive to differences in leaf chlorophyll content at leaf and canopy scales, when limited to true color.
  • Absolute Nitrogen Content

    Absolute Nitrogen Content

    Absolute Nitrogen Content
    Supported Sensors: Micasense RedEdge, Parrot Sequoia
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    Absolute Nitrogen Content Algorithm Output Absolute Nitrogen Content Algorithm Output

    Description

    Decision Support That Makes Absolute Sense™

    Absolute Sense ™ - Absolute Nitrogen Content in Wheat uses data-modelling expertise developed through Leonardo’s military data analysis activities to generate a wheat yield increase of up to 14% while at the same time reducing the overall fertiliser usage by up to 7%. Early field trials have demonstrated the success of the concept.

    The app calculates the actual amount of nitrogen currently contained in each pixel and displays this in kilograms per hectare. Absolute Sense™ can be used to support nitrogen application recommendations and can be used with any platform that supports the widely used MicaSense RedEdge or Parrot Sequoia sensors.

    Supporting N application recommendations

    When creating a fertilizer prescription, knowing the Absolute N content present in the crop will help optimise your N fertilizer application maps;

    • In comparison to other methods where only relative values are given, this algorithm provides the actual, absolute N content.
    • Created for use during 21 and 31 growth stages.
    • Once you have captured data more than once you can also compare against previous data sets to monitor nitrogen uptake in the crop.

    This product is currently being sold as a beta, updates will be issued to include 39 and 61 growth stages. Field trials to date have demonstrated that a farmer could achieve a yield increase of up to 14% while at the same time reducing the overall fertiliser usage by up to 7%. This can represent real cost savings at a time of economic uncertainty.

    Optimal Conditions For Data Capture

    • Good sunlight - best times of day between 10:00 through to 15:00
    • Clear skies (cloud patches over the capture area may cast shadows and degrade data)
    • The sensor must be calibrated. (i.e. using panels of reflection).
    Download more information about the Leonardo field trials.
  • Weed Pressure

    Weed Pressure

    Weed Pressure
    Supported Sensors: BGNIR
    Supported Resolution: 5cm/px or less
    Estimated Processing: less than 2 hours
    Weed Pressure Algorithm Output Weed Pressure Algorithm Output Weed Pressure Algorithm Output

    Description

    Weed Pressure identifies the non-crops or unwanted plants known as weeds within the survey.

    This algorithm generates a weed pressure index which has range of 0 - 20 for each 2m X 2m grid cell along with percentage weeds present and area in square meters.

  • Roof Report

    Roof Report

    Roof Report
    Supported Sensors: RGB
    Supported Resolution: 10cm/px or less
    Estimated Processing: Less than 4 hours
    Roof Report Algorithm Output Roof Report Algorithm Output Roof Report Algorithm Output

    Description

    From accelerating the insurance claims process to optimizing your facility management workflow, Roof Report helps you measure a roof without the time, risk, and bias of traditional inspections.

    Creating your report is easy. Just crop and annotate (roof facets, eaves, rakes, ridges, and valleys) an existing PrecisionMapper 3D model, and Roof Report will produce a PDF featuring:

    • Images of the roof from North, South, East, and West compass directions
    • Survey information, including: date, location, latitude/longitude, map projection, resolution, and weather
    • Measurements, by facet, including: dimensions, square feet, and pitch

    To read more about how PrecisionHawk is helping the Insurance industry, visit our blog.

  • Roof Report Lite

    Roof Report Lite

    Roof Report Lite
    Supported Sensors: RGB
    Supported Resolution: 10cm/px or less
    Estimated Processing: Less than 4 hours
    Roof Report Lite Algorithm Output Roof Report Lite Algorithm Output Roof Report Lite Algorithm Output

    Description

    Roof Report Lite makes it easy to create a report featuring aerial survey images. Just upload and crop the images you captured with your drone, and roof report will produce a PDF featuring:

    • Images from North, South, East, and West compass directions
    • Survey information, including date, location, latitude/longitude, map projection, resolution, and weather

    To read more about how PrecisionHawk is helping the Insurance industry, visit our blog.

  • Hail Damage Report

    Hail Damage Report

    Hail Damage Report
    Supported Sensors: RGB
    Supported Resolution: 10cm/px or less
    Estimated Processing: Less than 4 hours
    Hail Damage Report Algorithm Output Hail Damage Report Algorithm Output Hail Damage Report Algorithm Output

    Description

    Assess the damage to a roof caused by hail without the time, risk, and bias of traditional inspections.

    Hail Damage Report makes it easy to create a report featuring aerial images and measurements of a roof. Just crop and annotate (hail damage, roof facets, eaves, rakes, ridges, and valleys) an existing PrecisionMapper 3D model, and Roof Report will produce a PDF featuring:

    • Images of the roof from North, South, East, and West compass directions
    • Survey information, including: date, location, latitude/longitude, map projection, resolution, and weather
    • Measurements, by facet, including: dimensions, square feet, and pitch
    • Percent damage, by facet

    To read more about how PrecisionHawk is helping the Insurance industry, visit our blog.

  • Progress Monitoring Report

    Progress Monitoring Report

    Progress Monitoring Report
    Supported Sensors: RGB
    Supported Resolution: 10cm/px or less
    Estimated Processing: Less than 4 hours
    Progress Monitoring Report Algorithm Output Progress Monitoring Report Algorithm Output Progress Monitoring Report Algorithm Output

    Description

    Create a visual summary of the differences in up to five surveys of the same site.

    Creating your report is easy. Just select the surveys, identify an area of interest in the first survey, and the Progress Monitoring algorithm will produce a PDF featuring:

    • Images of the site from North, South, East, and West compass directions
    • Survey information, including: date, location, latitude/longitude, map projection, resolution, and weather

    For the best results, fly sites in both orbital and grid patterns.

    This algorithm is helpful for:

    • Builders monitoring progress on their construction site (without having to fly traditional aircraft or rely on satellite imagery)
    • Insurance adjusters accelerating the claims cycle by reducing the time required to assess changes in, and damage to, assets
    • Energy professionals preventing “high risk, low probability” events by frequently inspecting infrastructure and equipment
    • And more!

    To read more about how PrecisionHawk is helping the Construction industry, visit our blog.

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