Reference health
Object-Based Multi-Temporal and Multi-Source Land Cover Mapping Leveraging Hierarchical Class Relationships
46/46 checkable references clean · checked 2026-07-23
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
8 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 46 checked references that resolve
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Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series
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Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
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Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series
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A graph-based approach to detect spatiotemporal dynamics in satellite image time series
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Object-oriented satellite image time series analysis using a graph-based representation
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Deep Recurrent Neural Networks for Winter Vegetation Quality Mapping via Multitemporal SAR Sentinel-1
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Toward Spatio–Spectral Analysis of Sentinel-2 Time Series Data for Land Cover Mapping
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DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
resolves10.1080/19475705.2018.1549112
Estimation of flood land use/land cover mapping by regional modelling of flood hazard at sub-basin level case study: Marand basin
resolves10.1016/j.agsy.2018.05.010
A comparison of global agricultural monitoring systems and current gaps
resolves10.1109/TGRS.2006.872081
On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance
resolves10.1016/j.isprsjprs.2019.09.016
Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture
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Combining Sentinel-1 and Sentinel-2 Time Series via RNN for Object-Based Land Cover Classification
resolves10.1016/j.rse.2018.07.006
Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery
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Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium
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Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes
resolves10.1109/LGRS.2018.2843886
Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion
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Hierarchical mapping of Northern Eurasian land cover using MODIS data
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A Hierarchical Object-oriented Urban Land Cover Classification Using WorldView-2 Imagery and Airborne LiDAR data
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Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product
resolves10.1109/MGRS.2017.2762307
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
resolves10.1109/LGRS.2017.2728698
Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks
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Unsupervised Spectral–Spatial Feature Learning via Deep Residual Conv–Deconv Network for Hyperspectral Image Classification
resolves10.3390/ijgi7040129
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders
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Mapping land cover on Reunion Island in 2017 using satellite imagery and geospatial ground data
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health
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$M^3\text{Fusion}$: A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion
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Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
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The 8 references without a DOI — listed, not checked
no DOI — not checkedCombining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions
no DOI — not checkedDi Gregorio, A. (2005). Land Cover Classification System: Classification Concepts and User Manual: LCCS, Food & Agriculture Organization.
no DOI — not checkedLillesand, T., Kiefer, R.W., and Chipman, J. (2015). Remote Sensing and Image Interpretation, John Wiley & Sons.
no DOI — not checkedRouse, J.W., Hass, R.H., Schell, J., and Deering, D. (1973, January 10–14). Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite (ERTS) symposium, Washington, DC, USA.
no DOI — not checkedBahdanau, D., Cho, K., and Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv.
no DOI — not checkedScikit-learn: Machine Learning in Python
no DOI — not checkedKingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv.
no DOI — not checkedVaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4–9). Attention is All you Need. Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA.
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