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

arXiv: https://arxiv.org/abs/2005.05652 · HTML: https://mlsanity.com/paper/2005.05652/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 1 of 1)

- [Streamlined Global and Local Features Combinator (SGLC) for High Resolution Image Dehazing](https://mlsanity.com/paper/2304.13375/) (2023)

## Abstract (arXiv)

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.