ISPRS Benchmarks,
Online2021
WWW Link.
Dataset, Urban Data.
Dataset, Building Detection.
Dataset, Object Detection.
Dataset, Point Cloud Segmentation. Multiple datasets. Some with associated benchmarks and challenges.
Includes: VAihingen/Enz, Toronto, Potsdam, UAVid, Gaofen,
EuroSDR, Urban classification.
See also ISPRS: International Society for Photogrammetry and Remote Sensing.
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Model evaluation, Accuracy assessment,
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Reflectance modelling, Hyperspectral remote sensing,
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Accuracy assessment, Object-based image analysis, OBIA,
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Connection of the Photochemical Reflectance Index (PRI) with the
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Hyperspectral imaging, High spatial resolution,
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Google Earth Engine Applications Since Inception:
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2011
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2012
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Elsevier DOI
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Land cover mapping, Support vector machine, Spatial accuracy,
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2102
Convolutional Neural Networks (CNN), Deep learning, Vegetation,
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Integrating Hierarchical Statistical Models and Machine-Learning
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2104
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Neshat, A.[Aminreza],
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2104
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The Quality of Remote Sensing Optical Images from Acquisition to
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2104
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Assessing the Effect of Training Sampling Design on the Performance
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2104
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2104
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2105
BibRef
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Padilla, F.M.[Francisco M.],
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Effect of Time of Day and Sky Conditions on Different Vegetation
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2105
BibRef
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2105
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IEEE DOI
2106
Training, Task analysis, Training data, Estimation, Monitoring,
Quality assessment, Satellites, Geospatial analysis,
remote sensing
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Maxwell, A.E.[Aaron E.],
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2107
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Zhu, X.X.[Xiao Xiang],
Multimodal remote sensing benchmark datasets for land cover
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Elsevier DOI
2108
Dataset, Remote Sensing. Benchmark datasets, Classification, Feature learning,
Hyperspectral, Land cover mapping, DSM, Multimodal, Specific features
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2108
Evaluation of the spatial model ediror.
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Valente, J.[João],
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Tekinerdogan, B.[Bedir],
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2108
BibRef
Chen, L.[Li],
Xu, Z.W.[Ze-Wei],
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An Empirical Study of Adversarial Examples on Remote Sensing Image
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IEEE DOI
2109
Remote sensing, Optical sensors, Optical imaging,
Feature extraction, Training, Radar polarimetry,
remote sensing image (RSI) scene classification
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Fayad, I.[Ibrahim],
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Quality Assessment of Acquired GEDI Waveforms:
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2109
Global Ecosystem Dynamics Investigation (LiDAR).
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Camacho, F.[Fernando],
García-Santos, V.[Vicente],
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Fiducial Reference Measurements for Vegetation Bio-Geophysical
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2109
Assess accuracy and fitness for desired analysis.
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Comparison of Phenological Parameters Extracted from SIF, NDVI and
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2301
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Papoutsis, I.[Ioannis],
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Benchmarking and scaling of deep learning models for land cover image
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PandRS(195), 2023, pp. 250-268.
Elsevier DOI
2301
Benchmark, Land use land cover image classification,
BigEarthNet, Wide Residual Networks, EfficientNet, Transfer learning
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Omia, E.[Emmanuel],
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Remote Sensing in Field Crop Monitoring: A Comprehensive Review of
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Dimitrovski, I.[Ivica],
Kitanovski, I.[Ivan],
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Elsevier DOI
2303
Deep learning (DL), Earth observation (EO),
Image classification, Benchmark study
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Which Vegetation Index? Benchmarking Multispectral Metrics to
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Radiometric and Polarimetric Quality Validation of Gaofen-3 over a
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Marelli, D.[Davide],
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ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and
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Elsevier DOI
2304
Dataset, Image matching, Photogrammetry, Local features,
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Dhillon, M.S.[Maninder Singh],
Kübert-Flock, C.[Carina],
Dahms, T.[Thorsten],
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Ullmann, T.[Tobias],
Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop
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2304
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Aleissaee, A.A.[Abdulaziz Amer],
Kumar, A.[Amandeep],
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Khan, S.[Salman],
Cholakkal, H.[Hisham],
Xia, G.S.[Gui-Song],
Khan, F.S.[Fahad Shahbaz],
Transformers in Remote Sensing: A Survey,
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DOI Link
2304
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Chen, Y.J.[Yi-Jun],
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Quality Assessment of Global Ocean Island Datasets,
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2305
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Boston, T.[Tony],
van Dijk, A.[Albert],
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Convolutional Neural Network Shows Greater Spatial and Temporal
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2305
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Joshi, A.[Abhasha],
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Gite, S.[Shilpa],
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Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and
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Pellegrino, A.[Andrea],
Fabbretto, A.[Alice],
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Braga, F.[Federica],
Pahlevan, N.[Nima],
Brando, V.E.[Vittorio Ernesto],
Kratzer, S.[Susanne],
Gianinetto, M.[Marco],
Giardino, C.[Claudia],
Assessing the Accuracy of PRISMA Standard Reflectance Products in
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2305
hyperspectral. Evaluation.
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Miletic, A.[Andrea],
Divjak, A.K.[Ana Kuveždic],
Donker, F.W.[Frederika Welle],
Assessment of the Croatian Open Data Portal Using User-Oriented
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2306
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Dondofema, F.[Farai],
Nethengwe, N.[Nthaduleni],
Taylor, P.[Peter],
Ramoelo, A.[Abel],
Comparison of Satellite Platform for Mapping the Distribution of
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2306
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Mountrakis, G.[Giorgos],
Heydari, S.S.[Shahriar S.],
Harvesting the Landsat archive for land cover land use classification
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PandRS(200), 2023, pp. 106-119.
Elsevier DOI
2306
Deep neural networks, Recurrent network, Convolutional network,
Long Short-Term Memory, Landsat, Random Forest
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Wang, Z.X.[Zhi-Xin],
Mountrakis, G.[Giorgos],
Accuracy Assessment of Eleven Medium Resolution Global and Regional
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RS(15), No. 12, 2023, pp. xx-yy.
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2307
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Rahman, M.M.[Md. Mostafizur],
Szabó, G.[György],
Assessing the Status of National Spatial Data Infrastructure (NSDI)
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2307
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Palanisamy, P.A.[Prathiba A.],
Jain, K.[Kamal],
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Machine Learning Classifier Evaluation for Different Input
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RS(15), No. 13, 2023, pp. 3241.
DOI Link
2307
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Bratic, G.[Gorica],
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Brovelli, M.A.[Maria Antonia],
Map of Land Cover Agreement: Ensambling Existing Datasets for
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RS(15), No. 15, 2023, pp. xx-yy.
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2308
BibRef
Dash, P.[Padmanava],
Sanders, S.L.[Scott L.],
Parajuli, P.[Prem],
Ouyang, Y.[Ying],
Improving the Accuracy of Land Use and Land Cover Classification of
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2309
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Lei, M.[Ming],
Dong, Y.F.[Yun-Feng],
Multi-Granularity Modeling Method for Effectiveness Evaluation of
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DOI Link
2310
BibRef
Jin, Z.C.[Zi-Chun],
Long, Z.Y.[Zhi-Yong],
Wang, S.F.[Shao-Fei],
Liu, Y.M.[Yun-Meng],
Performance of the Atmospheric Radiative Transfer Simulator (ARTS) in
the 600-1650 cm-1 Region,
RS(15), No. 19, 2023, pp. 4889.
DOI Link
2310
BibRef
Gong, Y.[Yali],
Xie, H.[Huan],
Liao, S.C.[Shi-Cheng],
Lu, Y.[Yao],
Jin, Y.M.[Yan-Min],
Wei, C.[Chao],
Tong, X.H.[Xiao-Hua],
Assessing the Accuracy of Multi-Temporal GlobeLand30 Products in
China Using a Spatiotemporal Stratified Sampling Method,
RS(15), No. 18, 2023, pp. 4593.
DOI Link
2310
BibRef
Cui, P.P.[Pei-Pei],
Chen, T.[Tan],
Li, Y.J.[Ying-Jie],
Liu, K.[Kai],
Zhang, D.P.[Da-Peng],
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Comparison and Assessment of Different Land Cover Datasets on the
Cropland in Northeast China,
RS(15), No. 21, 2023, pp. 5134.
DOI Link
2311
BibRef
Wang, Y.Z.[Yan-Zhao],
Sun, Y.H.[Yong-Hua],
Cao, X.[Xuyue],
Wang, Y.[Yihan],
Zhang, W.[Wangkuan],
Cheng, X.[Xinglu],
A review of regional and Global scale Land Use/Land Cover (LULC)
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PandRS(206), 2023, pp. 311-334.
Elsevier DOI
2312
Land cover, Land use, LULC products, Satellite remote sensing,
Challenges and trends
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Liu, J.P.[Jing-Peng],
Ren, Y.[Yu],
Chen, X.[Xidong],
Regional Accuracy Assessment of 30-Meter GLC_FCS30, GlobeLand30, and
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RS(16), No. 1, 2024, pp. xx-yy.
DOI Link
2401
BibRef
Wang, C.L.[Chen-Liang],
Shi, W.J.[Wen-Jiao],
Lv, H.C.[Hong-Chen],
Construction of Remote Sensing Indices Knowledge Graph (RSIKG) Based
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RS(16), No. 1, 2024, pp. xx-yy.
DOI Link
2401
BibRef
Zhu, R.[Rui],
Tan, Y.M.[Yu-Min],
Luo, Z.Q.[Zi-Qing],
Shi, Y.Z.[Yan-Zhe],
Wang, J.[Jiale],
Jing, G.[Guifei],
Wang, X.L.[Xiao-Lu],
WenSiM: A Relative Accuracy Assessment Method for Land Cover Products
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RS(16), No. 2, 2024, pp. 257.
DOI Link
2402
BibRef
Zhang, H.Z.[Hong-Zhe],
Feng, S.[Shou],
Wu, D.[Di],
Zhao, C.H.[Chun-Hui],
Liu, X.[Xi],
Zhou, Y.[Yuan],
Wang, S.N.[Sheng-Nan],
Deng, H.T.[Hong-Tao],
Zheng, S.[Shuang],
Hyperspectral Image Classification on Large-Scale Agricultural Crops:
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RS(16), No. 3, 2024, pp. 478.
DOI Link
2402
BibRef
Krekovic, D.[Dora],
Galic, V.[Vlatko],
Tržec, K.[Krunoslav],
Žarko, I.P.[Ivana Podnar],
Kušek, M.[Mario],
Comparing Remote and Proximal Sensing of Agrometeorological
Parameters across Different Agricultural Regions in Croatia:
A Case Study Using ERA5-Land, Agri4Cast, and In Situ Stations during the
Period 2019-2021,
RS(16), No. 4, 2024, pp. 641.
DOI Link
2402
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Dong, R.[Runbo],
Guo, H.D.[Hua-Dong],
Liu, G.[Guang],
Comparison Study of Earth Observation Characteristics between
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DOI Link
2402
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Hare, T.M.[Trent M.],
Kirk, R.L.[Randolph L.],
Bland, M.T.[Michael T.],
Galuszka, D.M.[Donna M.],
Laura, J.R.[Jason R.],
Mayer, D.P.[David P.],
Redding, B.L.[Bonnie L.],
Wheeler, B.H.[Benjamin H.],
Current Status of the Community Sensor Model Standard for the
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2402
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Mountrakis, G.[Giorgos],
Heydari, S.S.[Shahriar S.],
Effect of intra-year Landsat scene availability in land cover land
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PandRS(212), 2024, pp. 164-180.
Elsevier DOI
2406
Deep learning, Time-series, Classification, Landsat, United States
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Thoreau, R.[Romain],
Risser, L.[Laurent],
Achard, V.[Véronique],
Berthelot, B.[Béatrice],
Briottet, X.[Xavier],
Toulouse Hyperspectral Data Set: A benchmark data set to assess
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PandRS(212), 2024, pp. 323-337.
Elsevier DOI
2406
Hyperspectral imaging, Land cover mapping, Benchmark data set,
Semi-supervised learning, Self-supervised learning
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Wijesingha, J.[Jayan],
Dzene, I.[Ilze],
Wachendorf, M.[Michael],
Evaluating the spatial-temporal transferability of models for
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Elsevier DOI
2406
Agricultural land cover, Crop types, Landsat,
Spatial-temporal transferability, Machine learning
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Liu, M.[Miao],
Zhan, Y.L.[Yu-Lin],
Li, J.[Juan],
Kang, Y.P.[Yu-Peng],
Sun, X.[Xiuling],
Gu, X.F.[Xing-Fa],
Wei, X.Q.[Xiang-Qin],
Wang, C.M.[Chun-Mei],
Li, L.L.[Ling-Ling],
Gao, H.L.[Hai-Liang],
Yang, J.[Jian],
Validation of Red-Edge Vegetation Indices in Vegetation
Classification in Tropical Monsoon Region:
A Case Study in Wenchang, Hainan, China,
RS(16), No. 11, 2024, pp. 1865.
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Gravity Measurements and Use .