CanoClass is a python module created to process large amounts of NAIP
imagery and create accurate canopy classifications in an open source
framework. Need for an open source classification system arose during the
creation of the Georgia canopy dataset as tools that were being used
, ArcMap and Textron's Feature Analyst, will be phased out within the next
few years. Additionally need for open source arose out of the lack of
insight to the algorithms that were being used by the software to
process our data and no true method to tweak it to suit our needs.
At its core CanoClass is optimized to to solve canopy classification problems. It is designed to be data agnostic with batch processing functions created to work with NAIP imagery, as scalable processing for NAIP imagery is necessary.
- GDAL
- NumPy
- Scikit-learn
- Rindcalc
Owen Smith, Huidae Cho, August 2021. An Open-Source Canopy Classification System Using Machine-Learning Techniques Within a Python Framework. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI-4/W2-2021, 175–182. doi:10.5194/isprs-archives-XLVI-4-W2-2021-175-2021.
Owen Smith, Huidae Cho, September 30, 2021. CanoClass: Creation of an Open Framework for Tree Canopy Monitoring. Free and Open Source Software for Geospatial (FOSS4G) 2021 Conference. The Open Source Geospatial Foundation (OSGeo). Online.