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

by   Thomas Tilak, et al.

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.



There are no comments yet.


page 1

page 2

page 3

page 4

page 6

page 7


LandCoverNet: A global benchmark land cover classification training dataset

Regularly updated and accurate land cover maps are essential for monitor...

A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases

Land use as contained in geospatial databases constitutes an essential i...

A Deep Learning Approach to Mapping Irrigation: IrrMapper-U-Net

Accurate maps of irrigation are essential for understanding and managing...

An automatic water detection approach based on Dempster-Shafer theory for multi spectral images

Detection of surface water in natural environment via multi-spectral ima...

Human-Machine Collaboration for Fast Land Cover Mapping

We propose incorporating human labelers in a model fine-tuning system th...

Deep Convolutional Neural Networks for Map-Type Classification

Maps are an important medium that enable people to comprehensively under...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.