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arXiv 2005.056522020-05-12eess.IVcs.CVcs.LG

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

CEREMA, Centre d'études et d'expertise sur les risques, l'environnement, la mobilité et l'aménagement（法国风险、环境、流动性与规划研究与评估中心（CEREMA））

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

备注：: 8 pages, 14 figures, ISPRS Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
发表信息：: XXIV ISPRS Congress, Commission III, Volume XLIII-B3-2020, 2020
DOI：: 10.5194/isprs-archives-XLIII-B3-2020-201-2020

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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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