PrecisionMapper

PROFESSIONAL DRONE BASED MAPPING AND ANALYTICS

Process - Analyze - Share

HOW IT WORKS

PrecisionMapper works online, automatically processes aerial data into 2D or 3D products, features a continuously expanding library of on-demand analysis tools, and makes sharing or collaborating easy.

  • Collect aerial data with your drone or satellite.
  • Upload data to your account and process 2D or 3D products.
  • Manage, collaborate, and share data with anyone.
  • Analyze data with a library of on-demand analysis tools.

VIEW WHAT YOU’VE CAPTURED BEFORE LEAVING SURVEY SITES!

PrecisionViewer is a desktop software that allows users to easily view flight path coverage, add ground control points, and attach flight logs and flight bounds to surveys.
  • COMPATIBLE WITH ANY DRONE
  • INTUITIVE USER INTERFACE
PrecisionViewer
PrecisionViewer

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TOOLS AND RESOURCES

  • Orthomosaics Orthomosaics
  • 3D Models 3D Models
  • Crop Health Analysis Tools Crop Health Analysis Tools
  • Volume Measurement Volume Measurement
Analysis Reports
Analysis Reports
  • Survey Name BGNIR_Corn_75lat_75fron_ortho
  • Survey Date 26 August 2014
  • Report Date 10 October 2016
  • Location Durham County
  • Lat/Lon 36.17503. -78.81504
  • Map Projection WGS 84 / UTM zone 17N
  • Image Resolution 122 in
  • Survey Area 39.3 ac
Analysis Reports

ORTHOMOSAIC PROCESSING

Georeferenced and GIS ready
Visual Sensor
Visual Sensor
Visual Sensor
2D Output 3D Output
Multispectral Sensor (3 and 5 channel)
Multispectral Sensor
Multispectral Sensor

Generate orthomosaics, 3D models, point clouds and digital surface models (DSM) from aerial data.

No internet? No problem.

PrecisionMapper offers a standalone desktop software that lets you process images on the go.

ANALYSIS MADE EASY WITH THE ALGORITHM MARKETPLACE

Access a continually expanding library of professional, on-demand analysis tools to gain critical insights you want, when you need.

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  • Orthomosaic

    PrecisionHawk

    Orthomosaic

    Output Options

    2D Map Processing GeoTiff, KML Tile Set

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

    Contour Map File Output DSM

    Orthomosaic

    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.
    Input Output
    Raw Images Georeferenced Orthomosaiced Image
    KML Tile Set (Compressed Folder)
    Point Cloud (LAS)
    Triangle Mesh
    Digital Surface Model (GeoTIFF)
    3D Model (Obj)
  • Enhanced Normalized Difference Vegetation Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Green Normalized Difference Vegetation Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Difference Vegetation Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Field Uniformity Tool

    PrecisionHawk

    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

    Input Output
    Plant Count Shapefile (if available)
    Plant Height Image (if available)
    Vegetation Index Image (if available)
    Canopy Cover (if available)
    Leaf Area (if available)
    Uniformity Shapefile (Compressed Folder containing ESRI Shapefile)
    Uniformity KML (Compressed Folder containing KML)
    Layered PDF
  • Green Difference Vegetation Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Optimized Soil Adjusted Vegetation Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Renormalized Difference Vegetation Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Normalized Difference Vegetation Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • NDVI - Blue

    PrecisionHawk

    NDVI - Blue

    NDVI - Blue

    Supported Sensors: BGNIR
    Supported Resolution: 20cm/px or less
    Estimated Processing: Less than 2 hours
    NDVI - Blue Algorithm Output NDVI - Blue Algorithm Output NDVI - Blue Algorithm Output

    Description

    NDVI-B calculates index based on NIR and Blue band. This index can be used to analyze crops in all growth stages. Compared to NDVI, which uses NIR and Red band, NDVI-B is less sensitive to crop stress and shows less contrast between stressed versus non-stressed crops.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Soil Adjusted Vegetation Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Select Growth Stage
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Canopy Cover

    PrecisionHawk

    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.

    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Leave "Full Scene" as default or input a shapefile
    Output file prefix
    Sensor used for canopy delineation
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
  • Green Leaf Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Green Soil Adjusted Vegetation Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Visual NDVI

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Visible Atmospherically Resistant Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Vegetation index to calculate
    Length (in meters) of the grid squares
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
    ESRI Format Geospatial Grid Cell Shapefile
    ESRI Format Geospatial Grid Cell Shapefile with Attributes
    ESRI Format Geospatial Grid Cell Centroid Shapefile with Attributes
  • Volume Measurement

    PrecisionHawk

    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.

    Input Output
    Orthorectified Image (GeoTIFF)
    Select from list of available survey orthomosaics that include DSM output
    Indicate the boundaries for your stockpiles and/or pits
    Shapefile with Volume Estimations
    KML File with Volume Estimations
    PDF report depicting 2D map of results
  • Row Based Plant Counting

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile) - This algorithm assumes a single, constant row orientation identified by user input. Rows with a different orientation angle should be considered as separate AOIs.
    Four (4) points on adjacent rows
    Length (in inches) of approximate spacing between seeds
    Timestamp for survey (in Unix time)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    ESRI Format Geospatial Grid Cell Shapefile
  • ScoutView Report

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF) ScoutView Report in PDF format
  • Waterpooling

    PrecisionHawk

    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.

    Input Output
    Orthorectified Image (GeoTIFF) Georeferenced Water Image (Compressed Folder containing GeoTIFF)
    KML Tile Set (Compressed Folder)
    Text file with total area in acres covered by water
    ESRI Format Geospatial Shapefile
  • Tree Crown Delineation

    PrecisionHawk

    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)

    Input Output
    Orthorectified Image (GeoTIFF)
    DSM Geotiff Image
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Open or Closed Canopy?
    Option of RGB / BGNIR / RGNIR / Micasense5
    Output file prefix
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Timestamp for survey (in Unix time)
    Tree Crown Shapefile
    Tree Crown kml file
    PDF report depicting 2D map of results
  • Triangular Greenness Index

    PrecisionHawk

    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.
    Input Output
    Orthorectified Image (GeoTIFF)
    Sensor used
    Region of Interest (Shapefile)
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Output file prefix
    Timestamp for survey (in Unix time)
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
    PDF report depicting 2D map of results
  • Nitrogen In-Crop Map

    Leonardo - Absolute Sense™

    Nitrogen In-Crop Map

    Nitrogen In-Crop Map

    Supported Sensors: Micasense RedEdge, Parrot Sequoia
    Supported Resolution: 50cm/px or less
    Estimated Processing: Less than 2 hours
    Nitrogen In-Crop Map Algorithm Output Nitrogen In-Crop Map Algorithm Output

    Description

    Absolute Sense™ - In Crop Mapping - Nitrogen - Winter Wheat*

    A new layer of information to support agronomic decision-making.

    • Instant view of nitrogen distribution in your field.
      • See in-field N variation.
      • See best/worst areas for nitrogen uptake.
      • Combine with other in-crop nutrient maps and/or soil maps to get a full layered picture of information.
      • Supports planning decisions.
    • Values in kilogrammes of nitrogen per hectare.
      • Additional information to support agronomic decisions for calculating fertiliser applications.

    Two colour profiles are supported:

    1. Equal Area uses a dynamic legend, enabling users to see more detail across the field, highlighting where the variation in the field is at one time. This option is best for supporting fertiliser management decisions.
    2. Equal Spacing visibly useful when comparing against other fields and also previous in-season maps of the same field across growth stages.

    Required Conditions for Optimal 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).

    *Tested on feed winter wheat

    Download more information about the Leonardo field trials.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Select Growth Stage
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
  • Potassium In-Crop Map

    Leonardo - Absolute Sense™

    Potassium In-Crop Map

    Potassium In-Crop Map

    Supported Sensors: Micasense RedEdge
    Supported Resolution: 50cm/px or less
    Estimated Processing: Less than 2 hours
    Potassium In-Crop Map Algorithm Output Potassium In-Crop Map Algorithm Output

    Description

    Absolute Sense™ - In Crop Mapping - Potassium - Winter Wheat*

    A new layer of information to support agronomic decision-making.

    • Instant view of potassium distribution in your field.
      • See in-field K variation.
      • See best/worst areas for potassium uptake.
      • Combine with other in-crop nutrient maps and/or soil maps to get a full layered picture of information.
      • Supports planning decisions.
    • Values in kilogrammes of potassium per hectare.
      • Additional information to support agronomic decisions for calculating fertiliser applications.

    Two colour profiles are supported:

    1. Equal Area uses a dynamic legend, enabling users to see more detail across the field, highlighting where the variation in the field is at one time. This option is best for supporting fertiliser management decisions.
    2. Equal Spacing visibly useful when comparing against other fields and also previous in-season maps of the same field across growth stages.

    Required Conditions for Optimal 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).

    *Tested on feed winter wheat

    Download more information about the Leonardo field trials.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Select Growth Stage
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
  • Sulphur In-Crop Map

    Leonardo - Absolute Sense™

    Sulphur In-Crop Map

    Sulphur In-Crop Map

    Supported Sensors: Micasense RedEdge
    Supported Resolution: 50cm/px or less
    Estimated Processing: Less than 2 hours
    Sulphur In-Crop Map Algorithm Output Sulphur In-Crop Map Algorithm Output

    Description

    Absolute Sense™ - In Crop Mapping - Sulphur - Winter Wheat*

    A new layer of information to support agronomic decision-making.

    • Instant view of sulphur distribution in your field.
      • See in-field S variation.
      • See best/worst areas for sulphur uptake.
      • Combine with other in-crop nutrient maps and/or soil maps to get a full layered picture of information.
      • Supports planning decisions.
    • Values in kilogrammes of sulphur per hectare.
      • Additional information to support agronomic decisions for calculating fertiliser applications.

    Two colour profiles are supported:

    1. Equal Area uses a dynamic legend, enabling users to see more detail across the field, highlighting where the variation in the field is at one time. This option is best for supporting fertiliser management decisions.
    2. Equal Spacing visibly useful when comparing against other fields and also previous in-season maps of the same field across growth stages.

    Required Conditions for Optimal 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).

    *Tested on feed winter wheat

    Download more information about the Leonardo field trials.
    Input Output
    Orthorectified Image (GeoTIFF)
    Region of Interest (Shapefile)
    Select Growth Stage
    Georeferenced Index Image (Compressed Folder containing GeoTIFF)
    KML and TMS Tile Set (Compressed Folder)
  • Weed Pressure

    PrecisionHawk

    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.

    Input Output
    Orthorectified Image (GeoTIFF)
    Select an area(s) where there is probability of weed
    Option of NOT_ORDERED, FULL_EXTENT or shapefile.shp
    Option of BGNIR or Micasense5
    Weed Type (Pre or Post Emergence)
    Output file prefix
    Latitude of centroid of survey area (decimal degrees; negative for south)
    Longitude of centroid of survey area (decimal degrees; negative for west)
    Timestamp for survey (in Unix time)
    Weed Pressure Statistics Shapefile
    Weed Pressure Index KML file
  • Roof Report

    PrecisionHawk

    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.

    Input Output
    Orthorectified Image (GeoTIFF)
    Digital Surface Model Image (GeoTIFF)
    Survey source imagery (including oblique imagery)
    Roof facet polygons
    PDF report depicting basic information, maps of roof with mesurements
    Roof facet Shapefile with measurements
  • Roof Report Lite

    PrecisionHawk

    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.

    Input Output
    Orthorectified Image (GeoTIFF)
    Survey source imagery (including oblique imagery)
    Region of Interest (Shapefile)
    PDF report depicting survey information and maps of roof.
  • Hail Damage Report

    PrecisionHawk

    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.

    Input Output
    Orthorectified Image (GeoTIFF)
    Survey source imagery (including oblique imagery)
    Region of Interest (Shapefile)
    PDF report depicting survey information and maps of roof.
  • 3D Structure

    PrecisionHawk

    3D Structure

    3D Structure

    Supported Sensors: RGB
    Supported Resolution: 50cm/px or less
    Estimated Processing: 0.5-3 Hours

    Description

    Generates 2d and 3d outputs based on a SFM process.

    Input Output
    Orthorectified Image (GeoTIFF) Georeferenced Orthomosaiced Image
    KML Tile Set (Compressed Folder)
    3D Model (Obj)
  • Progress Monitoring Report

    PrecisionHawk

    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.

    Input Output
    Orthorectified Image (GeoTIFF)
    Survey source imagery (including oblique imagery)
    Draw the boundary of your area of interest
    Pick up to four additional surveys to be included in your report
    PDF report depicting basic information, maps of roof with mesurements

SHARE AND COLLABORATE WITH ANYONE YOU CHOOSE

Share your survey data and collaborate on projects. PrecisionMapper is responsive so users can view what you share on any device.

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PrecisionMapper Works With Data From All Drones

  • DJI
  • Parrot
  • 3DR
  • Yuneec
  • Ascending Technologies
  • UAV Solutions
  • Ehang
  • Autel
  • GoPro
  • And more

* Must provide images plus telemetry data.

Insight from Above and Beyond

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