EXTRACTION OF BUILT-UP AREA BY COMBINING TEXTURAL FEATURES AND SPECTRAL INDICES FROM LANDSAT-8 MULTISPECTRAL IMAGE

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1. Evaluation Of The Relationship Between Spatial-Temporal Changes Of Land Use/Land Cover (Lulc) And Land Surface Temperature (Lst): A Case Study Of Nilüfer, Bursa T. ÜSTÜN TOPAL https://doi.org/10.51552/peyad.1346845
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3. The use of bands ratio derived from Sentinel-2 imagery to detect built-up area in the dry period (North-East Algeria) K. Rouibah https://doi.org/10.1007/s12518-023-00513-y
4. The urban areas classification methodology according to multi-zone images of Sentinel 2 and Landsat 8 (on the example of the city of Thanh Hoa, Vietnam) L. Trinh et al. https://doi.org/10.22389/0016-7126-2020-956-2-40-49
5. Applying Multi-Index approach from Sentinel-2 Imagery to Extract Urban Area in dry season (Semi-Arid Land in North East Algeria) K. Rouibah & M. Belabbas https://doi.org/10.4995/raet.2020.13787
6. Assessment of land cover degradation due to mining activities using remote sensing and digital photogrammetry M. Blanche et al. https://doi.org/10.1186/s40068-024-00372-5
7. Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020) F. Dubertret et al. https://doi.org/10.3390/rs14092127
8. Evaluate the level effectiveness of selected bare soil indices in Semi-Arid land using Sentinel-2 data K. Rouibah https://doi.org/10.2298/GSGD2601115R
9. Temporal and Spatial Changes in Vegetation Ecological Quality and Driving Mechanism in Kökyar Project Area from 2000 to 2021 Z. Wang et al. https://doi.org/10.3390/su14137668
10. Automated Built-Up Infrastructure Land Cover Extraction Using Index Ensembles with Machine Learning, Automated Training Data, and Red Band Texture Layers M. Maloney et al. https://doi.org/10.3390/rs16050868
11. Built-Up Area Identification in Suburban Region Using Modified PRISI Index of Sentinel-1 Data (Case Study: Surabaya-Sidoarjo, Indonesia) F. Fathimah & M. Taufik https://doi.org/10.1088/1755-1315/1551/1/012040
12. An approach for built-up area extraction using different indices and deep neural network (DNN) model W. Ismaeel & J. Satish Kumar https://doi.org/10.1016/j.infrared.2024.105558
13. Boosting Algorithm: An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings S. Mantey et al. https://doi.org/10.4236/ars.2026.151001
14. Ecosystem Service Value Decline Along a Coastal Gradient: Evidence from Zhoushan Island W. Mo et al. https://doi.org/10.3390/su18115649
15. Exploring the Use of Orthophotos in Google Earth Engine for Very High-Resolution Mapping of Impervious Surfaces: A Data Fusion Approach in Wuppertal, Germany J. Langenkamp & A. Rienow https://doi.org/10.3390/rs15071818
16. Feature-Based Evaluation of ML and DNN Models for LULC Mapping in Semi-Arid Regions S. Tahmi & X. Rui https://doi.org/10.1007/s12524-026-02501-2
17. Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2 S. Sejari & V. Mahammood https://doi.org/10.4995/raet.2027.25885