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Vicmap Vegetation Tree Extent provides the most detailed statewide picture of tree cover available to date and is an excellent source of data for any applications requiring the identification of small patches of remnant tree cover such as connectivity analyses and habitat modelling. The dataset also lends itself well to map presentation and makes an excellent backdrop for other thematic information shown on maps.
Tree Extent was derived from a machine learning output. The dataset was created by assigning each pixel in the aerial photography into two classes: tree or not tree. The data was created using a machine learning method called semantic segmentation. In semantic segmentation, the machine learning model is trained on aerial images, learning from a corresponding raster mask that indicates which pixels represent trees. From the training examples, the machine learning model learns to distinguish pixels that belong to woody vegetation from those that belong to all other features, such as ground cover, roads, and human-built structures. The model was trained on 20cm aerial photography. After processing the state-wide tree extent a waterbody and crop mask were applied to remove any incorrectly classified pixels as tree cover found over those areas. No additional human intervention post processing was performed on the data.
For more information: Vicmap Vegetation (land.vic.gov.au)
Vicmap Vegetation Tree Extent provides the most detailed statewide picture of tree cover available to date and is an excellent source of data for any applications requiring the identification of small patches of remnant tree cover such as connectivity analyses and habitat modelling. The dataset also lends itself well to map presentation and makes an excellent backdrop for other thematic information shown on maps.
Tree Extent was derived from a machine learning output. The dataset was created by assigning each pixel in the aerial photography into two classes: tree or not tree. The data was created using a machine learning method called semantic segmentation. In semantic segmentation, the machine learning model is trained on aerial images, learning from a corresponding raster mask that indicates which pixels represent trees. From the training examples, the machine learning model learns to distinguish pixels that belong to woody vegetation from those that belong to all other features, such as ground cover, roads, and human-built structures. The model was trained on 20cm aerial photography. After processing the state-wide tree extent a waterbody and crop mask were applied to remove any incorrectly classified pixels as tree cover found over those areas. No additional human intervention post processing was performed on the data.
For more information: Vicmap Vegetation (land.vic.gov.au)