Version 1
: Received: 25 January 2024 / Approved: 26 January 2024 / Online: 26 January 2024 (09:41:31 CET)
How to cite:
Rawat, A.; Gupta, P.K.; Persello, C. Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India. Preprints2024, 2024011879. https://doi.org/10.20944/preprints202401.1879.v1
Rawat, A.; Gupta, P.K.; Persello, C. Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India. Preprints 2024, 2024011879. https://doi.org/10.20944/preprints202401.1879.v1
Rawat, A.; Gupta, P.K.; Persello, C. Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India. Preprints2024, 2024011879. https://doi.org/10.20944/preprints202401.1879.v1
APA Style
Rawat, A., Gupta, P.K., & Persello, C. (2024). Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India. Preprints. https://doi.org/10.20944/preprints202401.1879.v1
Chicago/Turabian Style
Rawat, A., Prasun Kumar Gupta and Claudio Persello. 2024 "Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India" Preprints. https://doi.org/10.20944/preprints202401.1879.v1
Abstract
In our increasingly urbanized world, precise and up-to-date maps of human settlements are essential for sustainable urban development policies. The availability of open-access Sentinel-2 data from the Copernicus program presents an opportunity to create a comprehensive global map of human settlements, offering a detailed view of built areas on a large scale. This study estimates large-scale built-up fractions using encoder-decoder deep learning architectures like U-net, Res-U-net, and Attention-U-net in the large and complex urban area of Delhi, India. Openly available datasets like Open Street Map (OSM) and Microsoft building footprint datasets are used to derive built-up fractions at 10×10m resolution cells for over 34,000 km2. Our results show that Attention-U-net with the Huber loss function performs the best in different built-up densities (i.e., urban, semi-urban or rural) with an R2 score of 0.631, while Res-U-net and U-net obtained an R2 score of 0.623 and 0.612, respectively. The investigated networks significantly improve the accuracy over the latest Global Human Settlement Layer product (GHSL-S2), which uses a deep-CNN and reaches an R2 of 0.387 in our case study area. The result of this study yields a valuable spatial layer for examining the spatial distribution of human settlements across the entire spectrum from rural to urban areas.
Keywords
fractional mapping; built-up; deep learning; Global Human Settlement Layer; Sustainability
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
