EXTRACTION OF BUILT-UP AREAS USING CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING FROM SENTINEL-2 SATELLITE IMAGES

9 citations as recorded by crossref.

1. A Method for Fully Automatic Building Footprint Extraction From Remote Sensing Images J. Xiong et al. 10.1080/07038992.2022.2103397
2. NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia J. Aryal et al. 10.3390/land11030351
3. A comparison of Sentinel-1 and Sentinel-2 in assessing flooded area and built-up land use: A case study of selected coastal districts in Andra Pradesh, India H. Gonuguntla et al. 10.35595/2414-9179-2020-2-26-421-435
4. Weakly-supervised domain adaptation for built-up region segmentation in aerial and satellite imagery J. Iqbal & M. Ali 10.1016/j.isprsjprs.2020.07.001
5. Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery C. Corbane et al. 10.1007/s00521-020-05449-7
6. Detecting and Assessing the Spatio-Temporal Land Use Land Cover Changes of Bahrain Island During 1986–2020 Using Remote Sensing and GIS S. Aljenaid et al. 10.1007/s41748-022-00315-z
7. Extracting built-up areas from Sentinel-1 imagery using land-cover classification and texture analysis I. Holobâcă et al. 10.1080/01431161.2019.1608391
8. An STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data L. Bu et al. 10.1098/rsos.220597
9. A deep learning classification approach using high spatial satellite images for detection of built-up areas in rural zones: Case study of Souss-Massa region - Morocco M. Wahbi et al. 10.1016/j.rsase.2022.100898