# Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery

arXiv: https://arxiv.org/abs/2006.03267 · HTML: https://mlsanity.com/paper/2006.03267/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery](https://mlsanity.com/paper/2411.02935/) (2024)
- [Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt](https://mlsanity.com/paper/2505.05367/) (2025)
- [Investigating Imbalances Between SAR and Optical Utilization for Multi-Modal Urban Mapping](https://mlsanity.com/paper/2304.05080/) (2023)
- [Poaching Hotspot Identification Using Satellite Imagery](https://mlsanity.com/paper/2508.09812/) (2025)

## Abstract (arXiv)

Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world.The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale.This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery.A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed.The core features of the proposed model are the image patch of size 5 x 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers.The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference data-set of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness.