DroneMapper Photogrammetry Software Updates 2021

JP Uncategorized

We've been busy working on enhancements to the DroneMapper Photogrammetry Software package and a handful of exciting enterprise projects! Please take a look at the software changelog below for the latest updates and features. We always appreciate customer suggestions and feedback to improve our offerings. Please contact us anytime with your ideas!

Thanks, DroneMapper Team

February 10th, 2021 - v1.9.2 20210210
  • Customer suggested feature: change resolution of RGB orthomosaic preview. Default setting is 8x native GSD with new settings options for 4x GSD and 2x GSD. Allows rapid production of RGB preview orthomosaic in the field supporting disaster mapping and emergency operations.
color orthomosaic
January 22nd, 2021 - v1.9.1 20210122
  • Remove twitter icon/link from UI
  • Remove license clean exe (false flag as malware by anti-virus)
November 26th, 2020 - v1.9.1 20201126
  • Bitcoin, Eth and other crypto payments accepted via coinbase.com
  • Enhance GPU tie point generator, performance optimizations
November 4th, 2020 - v1.9.1 20201104
  • New tie point reduction algorithm. Useful for large datasets, imagery with large feature counts, or when using Brisk/Akaze/Multi-Scale TP. Algorithm filters and selects the best tie points based on score, limits tie points to a reasonable count per image pair. Speeds up large dataset processing.
  • Initial CUDA based tie-point detector and feature matching. Requires a NVIDIA GPU and driver >= 418.39 (beta), utilizes CPU and GPU CUDA cores.
    CUDA SDK: 10.1 (10.1.105) DRIVER >= 418.39 NVIDIA CUDA: Ver 10.1 - CUFFT CUBLAS NVIDIA GPU Archs: 30 35 37 50 52 60 61 70 75
October 26th, 2020 - v1.9 20201026
  • Fix for GCP processing in Stereo/Per Image Matching mode
  • UI and about dialog updates
  • Update to latest exiftool v12.06

AquaBits.io Cutting Edge Water Management Solutions

JP Uncategorized

aquabits sensor
In the last few months DroneMapper has developed a terrestrial sensor (AquaBits IoT) focused on water resource management. This post describes our development, verification and capabilities for an affordable, real time device sensing flowing water in channels and continuous measurement of reservoir holding capacities. Previously we described the use of drone imagery collection, precision imagery processing and generation of reservoir basin contours from the DEM for water elevation correlation to storage capacity. This data can now be augmented with real time reporting of actual reservoir stage at any frequency of interest, with little latency to water managers.

AquaBits utilizes accurate hydrostatic pressure measurement of the water column and is self contained inclusive of a power subsystem and communication capability using either cellular (when available) or satellite for remote locations. Data is transmitted to our secure dashboard where it is archived and processed for discharge in a flowing channel or current water stage in a reservoir. A slide deck with an overview of AquaBits and recently collected data compared to Colorado State instrumentation can be found here: (pdf attached)

In other scenarios when equipped with the appropriate sensor(s), applications to consider may include soil nutrient/moisture measurement for agriculture, earth subsidence/movement, chemical contamination, water quality, audio/visual surveillance, limited only by the imagination... Feel free to contact us and discuss your specific application.

The DroneMapper Team


Lowering the Cost of Continuous Streamflow Monitoring Award, January 21, 2020

JP Uncategorized

The Bureau of Reclamation and the US Geological Survey conducted a challenge in 2019 to solicit approaches for the reduction of cost associated with streamflow monitoring at gaging sites throughout the US. "...continuous streamflow monitoring stations are vital to water resources planning, design, management and research." With budget and project priority pressures the number of gaging sites nationally is in decline.

DroneMapper was one of five selected finalists in the challenge offering an approach using a 3D sensor for continuous remote sensing of the water surface and the use of UAS for stream channel characterization to improve hydrological modeling and simulation. The Bureau and USGS award press release is viewed here: https://www.usbr.gov/newsroom/stories/detail.cfm?RecordID=69224

We are very interested in pursuing this technology further for US Government, state and local use. We are in the process of identifying investment alternatives that would promote further development and verification of the 3D sensor. Please do not hesitate to contact us if you have or know of someone that has an interest in supporting this venture. Please contact Pierre Stoermer, CEO DroneMapper, (970) 417-1102

Many thanks from the DroneMapper Team!!

Data Science Basic Crop Analysis with UAV Imagery and Micasense Altum

JP Uncategorized


A jupyter notebook with crop analysis algorithms utilizing digital elevation models, dtm and multi-spectral imagery (R-G-B-NIR-Rededge-Thermal) from a MicaSense Altum sensor processed with DroneMapper Remote Expert.

Due to limitations on git file sizes, you will need to download the GeoTIFF data for this project from the following url: https://dronemapper.com/software/DroneMapper_CropAnalysis_Data.zip. Once that has been completed, extract the TIF files into the notebook data directory matching the structure below.

Included Data

  • data/DrnMppr-DEM-AOI.tif – 32bit georeferenced digital elevation model
  • data/DrnMppr-ORT-AOI.tif – 16bit georeferenced orthomosaic (Red-Green-Blue-NIR-Rededge-Thermal)
  • data/DrnMppr-DTM-AOI.tif – 32bit georeferenced dtm
  • data/plant_count.shp – plant count AOI
  • data/plots_1.shp – plot 1 AOI
  • data/plots_2.shp – plot 2 AOI
  • output/*.csv – output csv of dataframes


  • plot volume/biomass
  • plot canopy height
  • plot ndvi zonal statistics
  • plot thermals
  • plant count


These basic algorithms are intended to get you started and interested in multi-spectral processing and analysis.

The orthomosaic, digital elevation model, and dtm were clipped to an AOI using GlobalMapper. The shapefile plots were also generated using GlobalMapper grid tool. We highly recommend GlobalMapper for GIS work!

We cloned the MicaSense imageprocessing repository and created the Batch Processing DroneMapper.ipynb notebook which allows you to quickly align and stack a Altum or RedEdge dataset creating the correct TIF files with EXIF/GPS metadata preserved. These stacked TIF files are then directly loaded into DroneMapper Remote Expert for processing.

This notebook assumes the user has basic knowledge of setting up their python environment, importing libraries and working inside jupyter.

Do More!

Implement additional algorithms like NDRE or alternative methods for plant counts. Submit a pull request to the repo!

Load Digital Elevation Model and Orthomosaic


Load Plot 1 AOI and Generate NDVI


Generate NDVI Zonal Statistics For Each Plot

0POLYGON Z ((289583.708 5130289.226 0.000, 2895…Coverage/QuadUser Created Features20.1884610.8730920.43855944440.0789210.436500
1POLYGON Z ((289588.705 5130289.052 0.000, 2895…Coverage/QuadUser Created Features30.1932140.8879710.44528244400.0910900.425304
2POLYGON Z ((289593.702 5130288.877 0.000, 2895…Coverage/QuadUser Created Features40.2322220.8901470.55286444400.1124400.519746
3POLYGON Z ((289598.699 5130288.703 0.000, 2896…Coverage/QuadUser Created Features50.0908250.8650830.53029544440.1105700.515392
4POLYGON Z ((289603.696 5130288.528 0.000, 2896…Coverage/QuadUser Created Features60.1046970.9224500.53666044420.1327310.495813

Load Plot 2 AOI & Compute DEM Canopy Mean Height For Each Plot

0POLYGON Z ((289707.875 5130279.812 1182.502, 2…Coverage/QuadUser Created Features – Coverage/Quad1360.129120363.381683361.14129433181.091593360.441467
1POLYGON Z ((289712.189 5130279.586 1190.569, 2…Coverage/QuadUser Created Features – Coverage/Quad2360.131866363.382446361.71029733161.215926361.927017
2POLYGON Z ((289716.503 5130279.360 1183.212, 2…Coverage/QuadUser Created Features – Coverage/Quad3360.117279363.384766361.13859233101.122890360.425781
3POLYGON Z ((289720.817 5130279.134 1182.668, 2…Coverage/QuadUser Created Features – Coverage/Quad4360.110443363.387207361.91543633221.258644362.585251
4POLYGON Z ((289725.131 5130278.908 1182.782, 2…Coverage/QuadUser Created Features – Coverage/Quad5360.006683363.377991361.55850133201.305164360.546524

Compute Thermal Mean For Each Plot

The thermal band (5) in the processed orthomosaic shows stitching artifacts which could likely be improved using more accurate pre-processing alignment and de-distortion algorithms. You can find more information about these functions in the MicaSense imageprocessing github repository. See notes at the top of this notebook.

0POLYGON Z ((289707.875 5130279.812 1182.502, 2…Coverage/QuadUser Created Features – Coverage/Quad130008.030431.030196.6286923318120.98205830193.0
1POLYGON Z ((289712.189 5130279.586 1190.569, 2…Coverage/QuadUser Created Features – Coverage/Quad230068.030560.030333.1927023316123.85609330332.0
2POLYGON Z ((289716.503 5130279.360 1183.212, 2…Coverage/QuadUser Created Features – Coverage/Quad329792.030645.030266.0302113310170.83120730275.5
3POLYGON Z ((289720.817 5130279.134 1182.668, 2…Coverage/QuadUser Created Features – Coverage/Quad429790.030700.030386.1372673322201.26691930391.0
4POLYGON Z ((289725.131 5130278.908 1182.782, 2…Coverage/QuadUser Created Features – Coverage/Quad529618.030691.030209.9045183320292.29939230262.0

Load Plot 1 AOI & Compute Volume/Biomass For Each Plot

0POLYGON Z ((289583.708 5130289.226 0.000, 2895…Coverage/QuadUser Created Features2-0.1534733.5255431.53252444446810.53848376.65036850.0
1POLYGON Z ((289588.705 5130289.052 0.000, 2895…Coverage/QuadUser Created Features3-0.0944823.5752261.64666444407311.18869082.28502150.0
2POLYGON Z ((289593.702 5130288.877 0.000, 2895…Coverage/QuadUser Created Features4-0.0706483.9924931.88459644408367.60827694.17467650.0
3POLYGON Z ((289598.699 5130288.703 0.000, 2896…Coverage/QuadUser Created Features50.0329284.5759892.969443444413196.202637148.51891550.0
4POLYGON Z ((289603.696 5130288.528 0.000, 2896…Coverage/QuadUser Created Features60.0741885.1054083.155879444214018.412506157.77261750.0

Load Plant Count AOI & Count Plants

Plant count: 310

Thanks! Keep an eye out for future notebooks and algorithms.

Using the Max Flow/Min Cut DEM algorithm for accurate volumetrics

JP Uncategorized


Figure 1

It's that time of the season again where water resource managers are checking their emptied reservoirs for possible silt-in and verification of water holding capacities. We recently flew one of these reservoirs on the south side of the Grand Mesa in western Colorado with our Phantom 3 and a set of ground surveyed aerial targets. The imagery and control were processed using two different DEM generation algorithms, the results of which are presented and discussed here.

Figure 1 illustrates the ortho of the area with the ground surveyed targets and other points depicted as black dots. Figure 2 shows the DEM construction using our standard algorithm. The black oval highlights DEM decorrelation where the vegetation and shadows obscure the NW bank of the reservoir causing erroneous elevation rendering. Figure 3 shows the DEM construction utilizing the max flow/min cut DEM algorithm with greatly enhanced correlation in the same area previously highlighted.

We use Global Mapper to generate contours at specific elevations of interest ranging from reservoir drain or dead pool to maximum capacity or spill elevation. Each contour generated is converted to a flat water elevation surface and the volume between that surface and the DEM reservoir surface is computed. Take a look at what happens when contours are generated on a locally decorrelated DEM in Figure 4. One can see the contours are moving into the NW vegetated area which produces higher volumetric and surface area estimates than reality. As a comparison Figure 5 using the Max/Flow/Min Cut algorithm demonstrates more accurate rendering of the contours and subsequently higher accuracy volume estimates.


Figure 2


Figure 3


Figure 4


Figure 5