1. Very High Resolution Land Cover Mapping of Urban Areas at Global Scale with Convolutional Neural Networks
- Author
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Tilak, Thomas, Braun, Arnaud, Chandler, David, David, Nicolas, Galopin, Sylvain, Lombard, Amélie, Michaud, Michaël, Parisel, Camille, Porte, Matthieu, and Robert, Marjorie
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - 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., Comment: 8 pages, 14 figures, ISPRS Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
- Published
- 2020
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