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Our goal was to build an accurate UAV analytics and classification system that avoided the difficult technical details typical remote sensing software. Here you can learn about how Atlasx works.



Atlas relies on the incredible data storage and geospatial analysis capacities of super computing. Atlasx requires only geo-referenced training points that identify the different classes of interest, which most often represents ecosystems or land cover classes. Each training point is then intersected with a range of training datasets (predictors) to train a random forest classifier. Once the random forest is trained, Atlasx classifies all of the pixels present in a study area into one of the classes present in the training set. Atlasx uses freely available, high resolution, ecosystem-scale, and publicly available biophysical (slope, elevation), spectral, and climatic (precipitation, temperature) datasets to inform the classification. Users can also upload their UAV orthomosaics to Atlasx. Each dataset is quickly processed into relevant indices, such as the Normalized Difference Vegetation Index (NDVI), before having to training and run the classifier.


Spectral Indicies

Spectral indices are combinations of spectral reflectance from two or more wavelengths that indicate the relative abundance of features of interest. Vegetation indices are the most popular type, but other indices are available for burned areas, man-made (built-up) features, water, and geologic features. The following topics provide definitions and equations for each spectral index, grouped by feature type:

Vegetation Indices

  • Broadband Greenness​

  • Narrowband Greenness

  • Canopy Nitrogen

  • Canopy Water Content

  • Dry or Senescent Carbon

  • Leaf Pigments

  • Light Use Efficiency

Forestry Indices

  • Burn Area & Ratio

  • Thermal Reading

Geology Indices

  • Clay​

  • Ferrous

  • Iron Oxide

  • Soil Index


Miscellaneous Indicies

  • Built-up​

  • Mud

  • Snow

  • Non Homogeneous

  • Water Index

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