Automated analysis of overhead imagery for habitat segmentation
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Richard, Antoine
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Abstract
The overall objective of our project is to be able to classify the evolution of land usage since
the advent of aerial imagery. In practice our aim is to bring automatic
habitat classification to the level achieved by a human expert performing a fine-scale
classification of habitat at resolutions covering from hedges and lake to fields, pastures or
forest. Relying on the recent progresses in machine learning algorithm and in particular
convolutional neural networks trained using deep learning (e.g. SegNet, DeepLab), our
approach trains a machine to segment an overhead imagery into a dozen of expert-specified
land use classes.
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