English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Monitoring the spatiotemporal dynamics of building footprints (BF) is necessary for understanding urbanization growth. It is a difficult task to extract residential sites, mainly BF, because of the complexity of their makeup and spectral variety. Additionally, conventional methods for building mapping typically rely on abundant training data and expertise from human operates. This study presents a new unsupervised Feature-Based Building Footprint Extraction (F2BFE) strategy using Sentinel-1&amp;2 satellite images and the SRTM Digital Elevation Model (DEM). The newly developed radar index (NRI) from Sentinel-1 images was utilized to extract the Primary Building Footprints (PBF) through histogram analysis and thresholding techniques, based on the mean of annual Sentinel-1 VV and VH Backscatter channels in the Ascending orbit. In this research, the integration of the Otsu and Unimodal thresholding technique was developed as an optimal thresholding method for feature extraction. Furthermore, Sentinel-2 images were applied to extract spectral indices related to vegetation (NDVI, GNDVI, RDVI indices), water (NDWI index), and residential/built-up (NDBI, BuEI). The qualitative and quantitative validation results indicate that the NRI-based BF map achieved higher Overall Accuracy (OA) values of 98.14%, 90%, and 91% in Region of Interest-1 (ROI-1), ROI-2, and ROI-3, respectively. Additionally, the Kappa Coefficients (KC) for these regions were 0.96, 0.97, and 0.85, respectively. The NRI index provides an excellent OA result when vegetation, water, and slope features are carefully eliminated. Finally, it can be inferred that the simultaneous use of the sentinel-1&amp;2 and slope data in feature space leads to increased BF accuracy.

F2BFE: development of feature-based building footprint extraction by remote sensing data and GEE