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arXiv · 2005.05652

Very High Resolution Land Cover Mapping of Urban Areas at Global Scale with Convolutional Neural Networks

Thomas Tilak·Arnaud Braun·David Chandler·Nicolas David·Sylvain Galopin·Amélie Lombard·Michaël Michaud·Camille Parisel·Matthieu Porte·Marjorie Robert
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Abstract

This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. The objective is to make a segmentation map of a large area (a french department) with the following classes: asphalt, bare soil, building, grassland, mineral material (permeable artificialized areas), forest and water from 20cm aerial images and Digital Height Model. We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class. A comparative study of different encoder-decoder architectures (U-Net, U-Net with Resnet encoders, Deeplab v3+) is presented with different loss functions. The final product is a highly valuable land cover map computed from model predictions stitched together, binarized, and refined before vectorization.

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Thomas Tilak, Arnaud Braun, David Chandler, Nicolas David, Sylvain Galopin, Amélie Lombard, Michaël Michaud, Camille Parisel, Matthieu Porte, Marjorie Robert. 2020-05-12. Very High Resolution Land Cover Mapping of Urban Areas at Global Scale with Convolutional Neural Networks. https://doi.org/10.5194/isprs-archives-xliii-b3-2020-201-2020

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