VERY HIGH RESOLUTION LAND COVER MAPPING OF URBAN AREAS AT GLOBAL SCALE WITH CONVOLUTIONAL NEURAL NETWORKS

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, grassla...

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Bibliographic Details
Main Authors: Tilak, T., Braun, A., Chandler, D., David, N., Galopin, S., Lombard, A., Michaud, M., Parisel, C., Porte, M., Robert, M.
Format: Conference Proceeding
Language:English
Subjects:
Annotations
Artificial neural networks
Asphalt
Coders
Comparative analysis
Comparative studies
Construction materials
Digital imaging
Encoders-Decoders
Grasslands
Ground truth
High resolution
Image resolution
Image segmentation
Land cover
Mapping
Resolution
Soil
Soil permeability
Training
Urban areas
Vector processing (computers)
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Description
Summary: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.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLIII-B3-2020-201-2020