Mapping Human Settlements with Multi-seasonal Sentinel-2 Imagery and Attention-based ResNeXt

This paper explores the potential of multi-spectral Sentinel-2 imagery for human settlement mapping, using deep learning based methods. We show first results of a study area in central Europe, with an attention-based ResNeXt to better exploit the spectral information. Reasonable mapping accuracy has been achieved, compared to the state-of-the-art products. Based on the results and comparison with the existing products, we discuss two interesting questions: how can human settlement mapping be made consistent with or complementary to the existing human settlement maps and how can further improvement in human settlement mapping be achieved by exploring deep learning-based approachesƒ

[1]  Xiao Xiang Zhu,et al.  Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets , 2018, Remote. Sens..

[2]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[3]  Ferri Stefano,et al.  Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014 , 2016 .

[4]  Xiao Xiang Zhu,et al.  So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification , 2019, ArXiv.

[5]  Thomas Esch,et al.  Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission , 2013, IEEE Geoscience and Remote Sensing Letters.

[6]  Cláudia M. Viana,et al.  The Value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi-Temporal Land Use/Cover Maps , 2019, ISPRS Int. J. Geo Inf..

[7]  Thomas Esch,et al.  Where We Live - A Summary of the Achievements and Planned Evolution of the Global Urban Footprint , 2018, Remote. Sens..

[8]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Pierre Soille,et al.  Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas , 2016, Remote. Sens..

[10]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[11]  Sérgio Freire,et al.  Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer , 2018, Remote. Sens..

[12]  Huadong Guo,et al.  A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Linda See,et al.  TOWARDS CONSISTENT MAPPING OF URBAN STRUCTURES – GLOBAL HUMAN SETTLEMENT LAYER AND LOCAL CLIMATE ZONES , 2016 .

[14]  Un Desa.  Transforming our world : The 2030 Agenda for Sustainable Development , 2016 .