ACCURACY DIMENSIONS IN REMOTE SENSING

36 citations as recorded by crossref.

1. Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier T. Zhang et al. https://doi.org/10.3390/app11020543
2. Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models N. Rengma & M. Yadav https://doi.org/10.1007/s10661-024-12719-7
3. Rapid expansion of small lakes in the permafrost zone of the Qinghai-Tibetan Plateau T. Yin et al. https://doi.org/10.1080/17538947.2026.2633850
4. Lithological mapping with pseudo-labelling: Promise or overestimation in data-scarce settings? S. Szabó et al. https://doi.org/10.15201/hungeobull.74.4.1
5. Water Hyacinth (Eichhornia crassipes) Detection Using Coarse and High Resolution Multispectral Data L. Pádua et al. https://doi.org/10.3390/drones6020047
6. LULC changes to riverine flooding: A case study on the Jamuna River, Bangladesh using the multilayer perceptron model M. Hasan et al. https://doi.org/10.1016/j.rineng.2023.101079
7. Assessment of forest cover dynamics for the detection of deforestation in the Hindu Kush Himalayan region using geospatial and machine learning approaches L. Gupta et al. https://doi.org/10.1007/s12145-024-01517-x
8. Investigating the Capabilities of Various Multispectral Remote Sensors Data to Map Mineral Prospectivity Based on Random Forest Predictive Model: A Case Study for Gold Deposits in Hamissana Area, NE Sudan A. Mohamed Taha et al. https://doi.org/10.3390/min13010049
9. Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam K. Ngo et al. https://doi.org/10.3390/rs13132439
10. Application of Geographic Information Systems and Remote Sensing for Land Use/Cover Change Analysis in the Klip River Catchment, KwaZulu Natal, South Africa D. Chabalala et al. https://doi.org/10.25046/aj060509
11. The Abundance and Persistence of Temperate and Tropical Seagrasses at Their Edge-of-Range in the Western Atlantic Ocean A. Bartenfelder et al. https://doi.org/10.3389/fmars.2022.917237
12. Continuous burned area monitoring using bi-temporal spectral index time series analysis V. Fotakidis et al. https://doi.org/10.1016/j.jag.2023.103547
13. Remote sensing data quality model: from data sources to lifecycle phases Á. Barsi et al. https://doi.org/10.1080/19479832.2019.1625977
14. Assessment of time-series-derived no-flood references for sar-based Bayesian flood mapping M. Tupas et al. https://doi.org/10.1080/15481603.2024.2427304
15. Classification Assessment Tool: A program to measure the uncertainty of classification models in terms of class-level metrics S. Szabó et al. https://doi.org/10.1016/j.asoc.2024.111468
16. Spatiotemporal Monitoring of Saline Water Body Changes Using Remote Sensing Data with a Focus on Comparing Spectral Indices (Case Study: Lake Urmia) A. Rezaali et al. https://doi.org/10.61186/jgst.14.3.69
17. Automatic generation of land use maps using aerial orthoimages and building floor data with a Conv-Depth Block (CDB) ResU-Net architecture S. Yoo et al. https://doi.org/10.1016/j.jag.2022.102678
18. Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification J. da Silva Junior et al. https://doi.org/10.3390/f14010032
19. Determining the Spectral Characteristics of Fynbos Wetland Vegetation Species Using Unmanned Aerial Vehicle Data K. Musungu et al. https://doi.org/10.3390/geomatics5020017
20. Informing Thin-Layer Placement for Coastal Wetland Restoration Through Remote Sensing and Community Outreach A. Hymel et al. https://doi.org/10.3390/rs18111716
21. A New and Robust Index for Water Body Extraction from Sentinel-2 Imagery Z. Su et al. https://doi.org/10.3390/rs16152749
22. Bedrock outcropping in the accumulation zone of the largest glacier in Mexico (Glaciar Norte of Citlaltépetl), as evidence of a possible accelerated extinction V. Soto https://doi.org/10.1007/s11629-021-7216-2
23. Mapping Buildings from Semi-Informal Settlements Using Non-Parametric Classifiers: A Case of Old Naledi W. Mpoeleng et al. https://doi.org/10.54203/jceu.2024.14
24. Tiger Habitat Quality Modelling in Malaysia with Sentinel-2 and InVEST V. Louis et al. https://doi.org/10.3390/rs16020284
25. A study on automatic annotation methods for watershed environmental elements based on semantic segmentation models P. He et al. https://doi.org/10.1080/22797254.2025.2473939
26. Unraveling boreal forest composition and drivers across scales in eastern Siberia L. Enguehard et al. https://doi.org/10.1088/1748-9326/ad5742
27. Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria D. Hermle et al. https://doi.org/10.5194/nhess-21-2753-2021
28. Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model A. Mohamed Taha et al. https://doi.org/10.1007/s11053-024-10387-5
29. An integrated object-based sampling approach for validating non-contiguous forest cover maps in fragmented tropical landscapes C. Iheaturu et al. https://doi.org/10.1016/j.jag.2025.104497
30. Characterization of Irrigated Rice Cultivation Cycles and Classification in Brazil Using Time Series Similarity and Machine Learning Models with Sentinel Imagery A. Garcia et al. https://doi.org/10.3390/agriengineering7030065
31. Performance Testing of Optical Flow Time Series Analyses Based on a Fast, High-Alpine Landslide D. Hermle et al. https://doi.org/10.3390/rs14030455
32. Reconstructing Digital Terrain Models from ArcticDEM and WorldView-2 Imagery in Livengood, Alaska T. Zhang & D. Liu https://doi.org/10.3390/rs15082061
33. Improving Sentinel-1 Flood Maps Using a Topographic Index as Prior in Bayesian Inference M. Tupas et al. https://doi.org/10.3390/w15234034
34. RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment S. Krapf et al. https://doi.org/10.3390/rs14102299
35. Accuracy comparison of various remote sensing data in lithological classification based on random forest algorithm Y. Xi et al. https://doi.org/10.1080/10106049.2022.2088859
36. A super-ensemble approach to map land cover types with high resolution over data-sparse African savanna landscapes L. Song et al. https://doi.org/10.1016/j.jag.2022.103152