Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 7,142

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Random forest in remote sensing: A review of applications and future directions

Belgiu, Mariana, Drăguţ, Lucian·2016·ISPRS Journal of Photogrammetry and Remote Sensing

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.isprsjprs.2016.01.011

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Research works printed but not linked: 98

  1. Abdel-Rahman, 2014, Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers, ISPRS J. Photogramm. Remote Sens., 88, 48, 10.1016/j.isprsjprs.2013.11.013 · DOI · Paper page
  2. Adelabu, 2014, Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels, ISPRS J. Photogramm. Remote Sens., 95, 34, 10.1016/j.isprsjprs.2014.05.013 · doi:10.1016/j.isprsjprs.2014.05.013
  3. Adelabu, 2014, Spectral discrimination of insect defoliation levels in mopane woodland using hyperspectral data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 177, 10.1109/JSTARS.2013.2258329 · doi:10.1109/jstars.2013.2258329
  4. Ahmed, 2015, Characterizing stand-level forest canopy cover and height using landsat time series, samples of airborne LiDAR, and the random forest algorithm, ISPRS J. Photogramm. Remote Sens., 101, 89, 10.1016/j.isprsjprs.2014.11.007 · DOI · Paper page
  5. Bechtel, 2012, Classification of local climate zones based on multiple earth observation data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 5, 1191, 10.1109/JSTARS.2012.2189873 · doi:10.1109/jstars.2012.2189873
  6. Belgiu, 2014, Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery, ISPRS J. Photogramm. Remote Sens., 96, 67, 10.1016/j.isprsjprs.2014.07.002 · doi:10.1016/j.isprsjprs.2014.07.002
  7. Belgiu, 2014, Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery, ISPRS J. Photogramm. Remote Sens., 87, 205, 10.1016/j.isprsjprs.2013.11.007 · doi:10.1016/j.isprsjprs.2013.11.007
  8. Belgiu, 2014, Ontology-based classification of building types detected from airborne laser scanning data, Remote Sens., 6, 1347, 10.3390/rs6021347 · DOI
  9. Breiman, 2001, Random forest, Mach. Learn., 45
  10. Briem, 2002, Multiple classifiers applied to multisource remote sensing data, IEEE Trans. Geosci. Remote Sens., 40, 2291, 10.1109/TGRS.2002.802476 · DOI · Paper page
  11. Cavallaro, 2015, Extended self-dual attribute profiles for the classification of hyperspectral images, IEEE Geosci. Remote Sens. Lett., 12, 1690, 10.1109/LGRS.2015.2419629 · doi:10.1109/lgrs.2015.2419629
  12. Chan, 2012, An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery, Int. J. Appl. Earth Obs. Geoinf., 18, 13, 10.1016/j.jag.2012.01.002 · DOI
  13. Chan, 2008, Evaluation of random forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery, Remote Sens. Environ., 112, 2999, 10.1016/j.rse.2008.02.011 · DOI · Paper page
  14. Chehata, 2009, Airborne lidar feature selection for urban classification using random forests, Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci., 39, 207
  15. Chen, 2014, Forested landslide detection using LiDAR data and the random forest algorithm: a case study of the Three Gorges, China, Remote Sens. Environ., 152, 291, 10.1016/j.rse.2014.07.004 · DOI
  16. Chutia, 2015, Hyperspectral remote sensing classifications: a perspective survey, Trans. GIS
  17. Colditz, 2015, An evaluation of different training sample allocation schemes for discrete and continuous land cover classification using decision tree-based algorithms, Remote Sens., 7, 9655, 10.3390/rs70809655 · DOI · Paper page
  18. Corcoran, 2013, Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota, Remote Sens., 5, 3212, 10.3390/rs5073212 · DOI · Paper page
  19. Dalponte, 2013, Tree species classification in boreal forests with hyperspectral data, IEEE Trans. Geosci. Remote Sens., 51, 2632, 10.1109/TGRS.2012.2216272 · DOI · Paper page
  20. DeFries, 2000, Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data, Remote Sens. Environ., 74, 503, 10.1016/S0034-4257(00)00142-5 · DOI · Paper page
  21. Demarchi, 2014, Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping, ISPRS J. Photogramm. Remote Sens., 87, 166, 10.1016/j.isprsjprs.2013.10.012 · DOI
  22. Deng, 2013, The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques, ISPRS J. Photogramm. Remote Sens., 86, 100, 10.1016/j.isprsjprs.2013.09.010 · DOI
  23. Díaz-Uriarte, 2006, Gene selection and classification of microarray data using random forest, BMC Bioinformatics, 7, 3, 10.1186/1471-2105-7-3 · DOI · Paper page
  24. Dietterich, 2000, An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization, Mach. Learn., 40, 139, 10.1023/A:1007607513941 · DOI · Paper page
  25. Du, 2015, Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features, ISPRS J. Photogramm. Remote Sens., 105, 38, 10.1016/j.isprsjprs.2015.03.002 · DOI · Paper page
  26. Fassnacht, 2014, Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central European test sites, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 2547, 10.1109/JSTARS.2014.2329390 · DOI · Paper page
  27. Frazier, 2014, Characterization of aboveground biomass in an unmanaged boreal forest using landsat temporal segmentation metrics, ISPRS J. Photogramm. Remote Sens., 92, 137, 10.1016/j.isprsjprs.2014.03.003 · DOI
  28. Ghimire, 2012, An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA, GISci. Remote Sens., 49, 623, 10.2747/1548-1603.49.5.623 · DOI
  29. Ghosh, 2014, A framework for mapping tree species combining hyperspectral and LiDAR data: role of selected classifiers and sensor across three spatial scales, Int. J. Appl. Earth Obs. Geoinf., 26, 49, 10.1016/j.jag.2013.05.017 · DOI · Paper page
  30. Ghosh, 2014, A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery, Int. J. Appl. Earth Obs. Geoinf., 26, 298, 10.1016/j.jag.2013.08.011 · DOI · Paper page
  31. Gislason, 2006, Random forests for land cover classification, Pattern Recogn. Lett., 27, 294, 10.1016/j.patrec.2005.08.011 · DOI · Paper page
  32. Guan, 2013, Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests, Int. J. Remote Sens., 34, 5166, 10.1080/01431161.2013.788261 · doi:10.1080/01431161.2013.788261
  33. Guo, 2015, Classification of airborne laser scanning data using JointBoost, ISPRS J. Photogramm. Remote Sens., 100, 71, 10.1016/j.isprsjprs.2014.04.015 · DOI · Paper page
  34. Guo, 2011, Relevance of airborne lidar and multispectral image data for urban scene classification using random forests, ISPRS J. Photogramm. Remote Sens., 66, 56, 10.1016/j.isprsjprs.2010.08.007 · DOI · Paper page
  35. Haas, 2014, Urban growth and environmental impacts in Jing-Jin-Ji, the Yangtze, River Delta and the Pearl River Delta, Int. J. Appl. Earth Obs. Geoinf., 30, 42, 10.1016/j.jag.2013.12.012 · DOI
  36. Ham, 2005, Investigation of the random forest framework for classification of hyperspectral data, IEEE Trans. Geosci. Remote Sens., 43, 492, 10.1109/TGRS.2004.842481 · DOI · Paper page
  37. Han, 2015, Detection of convective initiation using meteorological imager onboard communication, ocean, and meteorological satellite based on machine learning approaches, Remote Sens., 7, 9184, 10.3390/rs70709184 · DOI
  38. Holmes, G., Donkin, A., Witten, I.H., 1994. Weka: a machine learning workbench, intelligent information systems, 1994. In: Proc. of the second Australian and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia, 29 November – 2 December, pp. 357–361.
  39. Jin, 2014, Assessing the impact of training sample selection on accuracy of an urban classification: a case study in Denver, Colorado, Int. J. Remote Sens., 35, 2067, 10.1080/01431161.2014.885152 · doi:10.1080/01431161.2014.885152
  40. Juel, 2015, Spatial application of random forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data, Int. J. Appl. Earth Obs. Geoinf., 42, 106, 10.1016/j.jag.2015.05.008 · DOI
  41. Junshi, 2014, Hyperspectral remote sensing image classification based on rotation forest, IEEE Geosci. Remote Sens. Lett., 11, 239, 10.1109/LGRS.2013.2254108 · DOI · Paper page
  42. Junshi, 2015, Improving random forest with ensemble of features and semisupervised feature extraction, IEEE Geosci. Remote Sens. Lett., 12, 1471, 10.1109/LGRS.2015.2409112 · doi:10.1109/lgrs.2015.2409112
  43. Karlson, 2015, Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using landsat 8 and random forest, Remote Sens., 7, 10017, 10.3390/rs70810017 · DOI · Paper page
  44. Körting, 2013, GeoDMA—geographic data mining analyst, Comput. Geosci., 57, 133, 10.1016/j.cageo.2013.02.007 · DOI
  45. Křížek, P., Kittler, J., Hlaváč, V., 2007. Improving stability of feature selection methods, computer analysis of images and patterns. In: Proc. of the 12th International Analysis on Computer Analysis of Images and Patters, Vienna, Austria, 27–29 August, pp. 929–936. · doi:10.1007/978-3-540-74272-2_115
  46. Kulkarni, V.Y., Sinha, P.K., 2012. Pruning of random forest classifiers: a survey and future directions. In: Proc. of International Conference on Data Science Engineering (ICDSE) Cochin, Kerala, India, 18–20 July, pp. 64–68. · DOI · Paper page
  47. Kuncheva, 2007, A stability index for feature selection, Artif. Int. Appl., 421
  48. Kursa, 2010, Feature selection with the Boruta package, J. Stat. Softw., 36, 10.18637/jss.v036.i11 · DOI · Paper page
  49. Lawrence, 2006, Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest), Remote Sens. Environ., 100, 356, 10.1016/j.rse.2005.10.014 · DOI · Paper page
  50. Leutner, 2012, Modelling forest α-diversity and floristic composition — on the added value of LiDAR plus hyperspectral remote sensing, Remote Sens., 4, 2818, 10.3390/rs4092818 · DOI
  51. Li, 2015, Identification of forested landslides using LiDar data, object-based image analysis, and machine learning algorithms, Remote Sens., 7, 9705, 10.3390/rs70809705 · DOI
  52. Liu, 2011, A fuzzy topology-based maximum likelihood classification, ISPRS J. Photogramm. Remote Sens., 66, 103, 10.1016/j.isprsjprs.2010.09.007 · doi:10.1016/j.isprsjprs.2010.09.007
  53. Lu, 2007, A survey of image classification methods and techniques for improving classification performance, Int. J. Remote Sens., 28, 823, 10.1080/01431160600746456 · DOI · Paper page
  54. Ma, 2015, Training set size, scale, and features in geographic object-based image analysis of very high resolution unmanned aerial vehicle imagery, ISPRS J. Photogramm. Remote Sens., 102, 14, 10.1016/j.isprsjprs.2014.12.026 · DOI · Paper page
  55. Mansour, 2012, Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA eagle resolution, ISPRS J. Photogramm. Remote Sens., 70, 56, 10.1016/j.isprsjprs.2012.03.006 · doi:10.1016/j.isprsjprs.2012.03.006
  56. Marceau, 1994, Remote Sensing and the measurement of geographical entities in a forested environment. 1. The scale and spatial aggregation problem, Remote Sens. Environ., 49, 93, 10.1016/0034-4257(94)90046-9 · doi:10.1016/0034-4257(94)90046-9
  57. Mas, 2007, The application of artificial neural networks to the analysis of remotely sensed data, Int. J. Remote Sens., 29, 617, 10.1080/01431160701352154 · DOI · Paper page
  58. Mellor, 2015, Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin, ISPRS J. Photogramm. Remote Sens., 105, 155, 10.1016/j.isprsjprs.2015.03.014 · DOI · Paper page
  59. Menze, B.H., Kelm, B.M., Splitthoff, D.N., Koethe, U., Hamprecht, F.A., 2011. On oblique random forests. In: Proc. of the European Conference Machine Learning and Knowledge Discovery in Databases, Athens, Greece, 5–9 September, pp. 453–469. · DOI · Paper page
  60. Miao, 2012, Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data, Int. J. Remote Sens., 33, 1823, 10.1080/01431161.2011.602651 · DOI
  61. Millard, 2015, On the importance of training data sample selection in random forest image classification: a case study in peatland ecosystem mapping, Remote Sens., 7, 8489, 10.3390/rs70708489 · DOI · Paper page
  62. Mishra, 2014, Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with random forest, Int. J. Remote Sens., 35, 1175, 10.1080/01431161.2013.876120 · DOI
  63. Naidoo, 2012, Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a random forest data mining environment, ISPRS J. Photogramm. Remote Sens., 69, 167, 10.1016/j.isprsjprs.2012.03.005 · DOI · Paper page
  64. Niemeyer, 2014, Contextual classification of lidar data and building object detection in urban areas, ISPRS J. Photogramm. Remote Sens., 87, 152, 10.1016/j.isprsjprs.2013.11.001 · DOI · Paper page
  65. Nitze, 2015, Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series, Int. J. Appl. Earth Obs. Geoinf., 34, 136, 10.1016/j.jag.2014.08.001 · doi:10.1016/j.jag.2014.08.001
  66. Pal, 2005, Random forest classifier for remote sensing classification, Int. J. Remote Sens., 26, 217, 10.1080/01431160412331269698 · DOI · Paper page
  67. Pedergnana, 2013, A novel technique for optimal feature selection in attribute profiles based on genetic algorithms, IEEE Trans. Geosci. Remote Sens., 51, 3514, 10.1109/TGRS.2012.2224874 · DOI
  68. Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825
  69. Peerbhay, 2015, Random forests unsupervised classification: the detection and mapping of solanum mauritianum infestations in plantation forestry using hyperspectral data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 3107, 10.1109/JSTARS.2015.2396577 · DOI
  70. Räsänen, 2013, What makes segmentation good? A case study in boreal forest habitat mapping, Int. J. Remote Sens., 34, 8603, 10.1080/01431161.2013.845318 · doi:10.1080/01431161.2013.845318
  71. Reese, 2014, Combining airborne laser scanning data and optical satellite data for classification of alpine vegetation, Int. J. Appl. Earth Obs. Geoinf., 27, 81, 10.1016/j.jag.2013.05.003 · doi:10.1016/j.jag.2013.05.003
  72. Rodriguez-Galiano, 2012, An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS J. Photogramm. Remote Sens., 67, 93, 10.1016/j.isprsjprs.2011.11.002 · DOI · Paper page
  73. Rodriguez, 2006, Rotation forest: a new classifier ensemble method, IEEE Trans. Pattern Anal. Mach. Int., 28, 1619, 10.1109/TPAMI.2006.211 · DOI · Paper page
  74. Saeys, 2007, A review of feature selection techniques in bioinformatics, Bioinformatics, 23, 2507, 10.1093/bioinformatics/btm344 · DOI · Paper page
  75. Schmidt, 2014, Contextual classification of full waveform lidar data in the Wadden Sea, IEEE Geosci. Remote Sens. Lett., 11, 1614, 10.1109/LGRS.2014.2302317 · doi:10.1109/lgrs.2014.2302317
  76. Schwarz, 2010, On safari to random jungle: a fast implementation of random forests for high-dimensional data, Bioinformatics, 26, 1752, 10.1093/bioinformatics/btq257 · DOI · Paper page
  77. Sesnie, 2010, The multispectral separability of Costa Rican rainforest types with support vector machines and random forest decision trees, Int. J. Remote Sens., 31, 2885, 10.1080/01431160903140803 · DOI
  78. Shang, 2014, Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 2481, 10.1109/JSTARS.2013.2282166 · DOI
  79. Shao, 2015, An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data, Int. J. Appl. Earth Obs. Geoinf., 38, 78, 10.1016/j.jag.2014.12.017 · DOI
  80. Stefanski, 2013, Optimization of object-based image analysis with random forests for land cover mapping, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6, 2492, 10.1109/JSTARS.2013.2253089 · DOI
  81. Stumpf, 2011, Object-oriented mapping of landslides using random forests, Remote Sens. Environ., 115, 2564, 10.1016/j.rse.2011.05.013 · DOI · Paper page
  82. Sun, 2015, The improvement of land cover classification by thermal remote sensing, Remote Sens., 7, 8368, 10.3390/rs70708368 · DOI
  83. Thenkabail, 2013, Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and hyperion/EO-1 data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6, 427, 10.1109/JSTARS.2013.2252601 · doi:10.1109/jstars.2013.2252601
  84. Topouzelis, 2012, Oil spill feature selection and classification using decision tree forest on SAR image data, ISPRS J. Photogramm. Remote Sens., 68, 135, 10.1016/j.isprsjprs.2012.01.005 · doi:10.1016/j.isprsjprs.2012.01.005
  85. Touw, 2012, Data mining in the life sciences with random forest: a walk in the park or lost in the jungle?, Briefings Bioinform.
  86. Tsutsumida, 2015, Measures of spatio-temporal accuracy for time series land cover data, Int. J. Appl. Earth Obs. Geoinf., 41, 46, 10.1016/j.jag.2015.04.018 · DOI
  87. Uhlmann, 2014, Integrating color features in polarimetric SAR image classification, IEEE Trans. Geosci. Remote Sens., 52, 2197, 10.1109/TGRS.2013.2258675 · DOI
  88. Vetrivel, 2015, Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images, ISPRS J. Photogramm. Remote Sens., 105, 61, 10.1016/j.isprsjprs.2015.03.016 · DOI
  89. Wang, 2015, Mapping Robinia pseudoacacia forest health conditions by using combined spectral, spatial, and textural information extracted from IKONOS imagery and random forest classifier, Remote Sens., 7, 9020, 10.3390/rs70709020 · DOI
  90. Waske, 2009, Classifier ensembles for land cover mapping using multitemporal SAR imagery, ISPRS J. Photogramm. Remote Sens., 64, 450, 10.1016/j.isprsjprs.2009.01.003 · DOI · Paper page
  91. Waske, 2012, ImageRF – a user-oriented implementation for remote sensing image analysis with random forests, Environ. Modell. Softw., 35, 192, 10.1016/j.envsoft.2012.01.014 · DOI
  92. Weinmann, 2015, Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers, ISPRS J. Photogramm. Remote Sens., 105, 286, 10.1016/j.isprsjprs.2015.01.016 · DOI · Paper page
  93. Woodcock, 1988, The use of variograms in remote sensing: I. Scene models and simulated images, Remote Sens. Environ., 25, 323, 10.1016/0034-4257(88)90108-3 · doi:10.1016/0034-4257(88)90108-3
  94. Zhang, 2014, Combining hyperspectral and LiDAR data for vegetation mapping in the Florida Everglades, Photogramm. Eng. Remote Sens., 80, 733, 10.14358/PERS.80.8.733 · DOI
  95. Zhang, 2009, Willows: a memory efficient tree and forest construction package, BMC Bioinformatics, 10, 130, 10.1186/1471-2105-10-130 · DOI
  96. Zhang, 2014, Random forests with ensemble of feature spaces, Pattern Recogn., 47, 3429, 10.1016/j.patcog.2014.04.001 · DOI
  97. Zhong, 2014, Efficient corn and soybean mapping with temporal extendability: a multi-year experiment using Landsat imagery, Remote Sens. Environ., 140, 1, 10.1016/j.rse.2013.08.023 · DOI · Paper page
  98. Xu, 2014, A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery, Remote Sens. Environ., 141, 14, 10.1016/j.rse.2013.10.012 · DOI · Paper page

Cited by

See all 7,142

Metadata

Authors
Belgiu, M., & Drăguţ, L.
Venue
ISPRS Journal of Photogrammetry and Remote Sensing
Publisher
Elsevier BV
Published
2016-04
Volume
114
Pages
24-31
ISSN
0924-2716 (ISSN-L)
OpenAlex
W2261059368
Cited by
7,142
References
4
FWCI
269.43
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Random forest in remote sensing: A review of applications and future directions. https://app.cochat.ai/papers/10.1016/j.isprsjprs.2016.01.011
The paper
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2016.01.011

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.