Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T19:42:21.997418Z
Paper Citation Record · LEDGER
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning
As of 18 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2412.06451.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
52 of 52 outbound references displayed
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External citation measurements
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Outbound references
Observation 77769cbe-6d12-42e6-b22a-74a2dc778b90 · outbound
This paper cites Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources,
Reference 1
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Observation 7bbfe535-8d3a-4a61-a31a-d8df90de287c · outbound
This paper cites Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art,
Reference 2
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Observation 470b4280-104b-49ac-9460-c3c6b2540a20 · outbound
This paper cites Attention is All you Need,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Attention is All you Need,
Reference 3
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Observation 371367b2-6740-4e8f-ab3c-6b8313150722 · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 4
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Observation df152044-5cd0-4187-818c-5760afbfc6c4 · outbound
This paper cites Earthformer: Exploring Space-Time Transformers for Earth System Forecasting,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Earthformer: Exploring Space-Time Transformers for Earth System Forecasting,
Reference 5
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Observation ad3d6c2e-27f3-4290-8432-c5ba2dda2bf5 · outbound
This paper cites Big data in Earth science: Emerging practice and promise,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Big data in Earth science: Emerging practice and promise,
Reference 6
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Observation 54260a2c-3e20-4d27-8c8c-07aacd177424 · outbound
This paper cites Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations,
Reference 7
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Observation 40f30334-f339-4116-abaf-bf3afd3dcf32 · outbound
This paper cites Deep learning and process understanding for data-driven Earth system science,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Deep learning and process understanding for data-driven Earth system science,
Reference 8
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Source-reported events for the cited work
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Observation f610724a-ce1e-4d57-838b-bddd2c2e7573 · outbound
This paper cites Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources,
Reference 9
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Observation 55f7799b-6fa5-44aa-8a6a-58243f4164fc · outbound
This paper cites A survey of uncertainty in deep neural networks,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning A survey of uncertainty in deep neural networks,
Reference 10
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Source-reported events for the cited work
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Observation 4ef087e2-0bc6-4ca6-9b7f-c50d1e90c81d · outbound
This paper cites Mcmc using hamiltonian dynamics,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Mcmc using hamiltonian dynamics,
Reference 11
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Observation 68744ff9-3ddb-491b-b7bd-8be359637396 · outbound
This paper cites A practical bayesian framework for backpropagation networks,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning A practical bayesian framework for backpropagation networks,
Reference 12
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Observation 071fe3ab-9695-4829-aad6-5f7b729a1d65 · outbound
This paper cites Consistent inference of probabilities in layered networks: predictions and generalizations,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Consistent inference of probabilities in layered networks: predictions and generalizations,
Reference 13
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Observation b7a7c00b-9731-4350-b756-d049236be440 · outbound
This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Dropout as a bayesian approximation: Representing model uncertainty in deep learning,
Reference 14
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Observation ba71d26d-bce9-4baf-832d-c692745a9fe1 · outbound
This paper cites A general framework for ensemble distribution distillation,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning A general framework for ensemble distribution distillation,
Reference 15
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Observation 594c3376-ae7f-4400-8618-8d1d17d6f101 · outbound
This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Simple and scalable predictive uncertainty estimation using deep ensembles,
Reference 16
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Observation 3b47294d-01f8-4875-8163-bcbbfa2a3b0c · outbound
This paper cites Greedy policy search: A simple baseline for learnable test-time augmentation,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Greedy policy search: A simple baseline for learnable test-time augmentation,
Reference 17
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Observation c80a2306-4823-4027-a3f9-391ff7ed142a · outbound
This paper cites Simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network,
Reference 18
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Observation ef680756-3ff3-4c81-8ab0-a928fd392aa7 · outbound
This paper cites Evidential deep learning to quantify classification uncertainty,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Evidential deep learning to quantify classification uncertainty,
Reference 19
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Observation 4e10cd65-b903-47e8-aa2b-c01cd500be70 · outbound
This paper cites Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recur- rent Models,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recur- rent Models,
Reference 20
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Observation 23ac13f1-315b-405f-a304-95a71986a7f4 · outbound
This paper cites Ice sheet and climate processes driving the uncertainty in projections of future sea level rise: Findings from a structured expert judgement approach,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Ice sheet and climate processes driving the uncertainty in projections of future sea level rise: Findings from a structured expert judgement approach,
Reference 21
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Observation 449613ca-5f3b-42a3-9f23-896142281cbc · outbound
This paper cites Going beyond one-hot encod- ing in classification: Can human uncertainty improve model performance in earth observation?.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Going beyond one-hot encod- ing in classification: Can human uncertainty improve model performance in earth observation?
Reference 22
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Observation 57346d85-32c9-4699-8495-b57ba86fd76f · outbound
This paper cites Categorising the world into local climate zones: towards quantifying labelling uncertainty for machine learning models,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Categorising the world into local climate zones: towards quantifying labelling uncertainty for machine learning models,
Reference 23
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Observation 8df56924-2d4d-4985-986f-43a27580f2ed · outbound
This paper cites Uncrtaints: Uncertainty quantification for cloud removal in optical satellite time series,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Uncrtaints: Uncertainty quantification for cloud removal in optical satellite time series,
Reference 24
- Resolution
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Source-reported events for the cited work
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Observation 2bc9faa9-60f7-488f-995a-2420687a4f89 · outbound
This paper cites Quantifying uncertainty in slum detection: Advancing transfer learning with limited data in noisy urban environments,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Quantifying uncertainty in slum detection: Advancing transfer learning with limited data in noisy urban environments,
Reference 25
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Observation fbb772e0-674f-4d82-88ff-3bb12bb992d9 · outbound
This paper cites Deepglobe 2018: A challenge to parse the earth through satellite images,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Deepglobe 2018: A challenge to parse the earth through satellite images,
Reference 26
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Observation 5ec5065c-0c7d-468f-ab1f-8524877ae5c3 · outbound
This paper cites SpaceNet on Amazon Web Services (AWS),.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning SpaceNet on Amazon Web Services (AWS),
Reference 27
- Resolution
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Observation dd02efbb-f30a-4d89-a774-6aa0ad83943e · outbound
This paper cites Dota: A large-scale dataset for object detection in aerial images,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Dota: A large-scale dataset for object detection in aerial images,
Reference 28
- Resolution
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Observation 88d26726-112f-4107-aa10-0416a57a51b0 · outbound
This paper cites EarthNets: Empowering AI in Earth Observation.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning EarthNets: Empowering AI in Earth Observation
Reference 29
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This paper cites So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets],.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets],
Reference 30
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Observation 80efe2b0-243f-4541-9308-6c6932854d08 · outbound
This paper cites Drone-Based RGB-Infrared Cross- Modality Vehicle Detection Via Uncertainty-Aware Learning,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Drone-Based RGB-Infrared Cross- Modality Vehicle Detection Via Uncertainty-Aware Learning,
Reference 31
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Observation eef3d4ae-4d03-40a0-a524-673259647c00 · outbound
This paper cites INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
Reference 32
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This paper cites What uncertainties do we need in bayesian deep learning for computer vision?.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning What uncertainties do we need in bayesian deep learning for computer vision?
Reference 33
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This paper cites [Online].
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Observation a89c0efd-30b4-4950-bbda-bd7af3f11914 · outbound
This paper cites [Online].
Reference 35
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Observation cecf21b8-e8df-4c0c-a265-133e13d8ae24 · outbound
This paper cites Improved allometric models to estimate the aboveground biomass of tropical trees,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Improved allometric models to estimate the aboveground biomass of tropical trees,
Reference 36
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This paper cites A general framework for uncertainty estimation in deep learning,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning A general framework for uncertainty estimation in deep learning,
Reference 37
- Resolution
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Observation 75b0841a-78e4-4362-a12d-51b03953928f · outbound
This paper cites Tractable Inference for Complex Stochastic Processes.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Tractable Inference for Complex Stochastic Processes
Reference 38
- Resolution
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Observation 6603a90f-3a90-4e07-8153-d1f55f40fcef · outbound
This paper cites Biomass esti- mation and uncertainty quantification from tree height,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Biomass esti- mation and uncertainty quantification from tree height,
Reference 39
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Observation 9c30cc14-7010-40da-9a0d-605b7802a315 · outbound
This paper cites [Online].
Reference 40
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Source-reported events for the cited work
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Observation e40cb07f-8c48-4b96-83ee-492443a32a6d · outbound
This paper cites Noise estimation in remote sensing imagery using data masking,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Noise estimation in remote sensing imagery using data masking,
Reference 41
- Resolution
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Observation 4b1efbc9-3afd-404a-8ad1-ea5e346dc409 · outbound
This paper cites Image quality of sparse-aperture designs for remote sensing,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Image quality of sparse-aperture designs for remote sensing,
Reference 42
- Resolution
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Source-reported events for the cited work
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Observation 2de6fa57-1ba5-45cc-9414-fa6ab3d44979 · outbound
This paper cites A deeper look into aleatoric and epistemic uncertainty disentanglement,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning A deeper look into aleatoric and epistemic uncertainty disentanglement,
Reference 43
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 51105395-6f4f-4bfc-8ba0-7592a1bfa188 · outbound
This paper cites Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,
Reference 44
- Resolution
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Source-reported events for the cited work
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Observation e6c52d9e-73c3-4b84-9fee-2e5933a3290e · outbound
This paper cites Local climate zones: Origins, development, and application to urban heat island studies,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Local climate zones: Origins, development, and application to urban heat island studies,
Reference 45
- Resolution
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Source-reported events for the cited work
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Observation 91478ce0-868a-4af0-a19d-101dd48e6dbe · outbound
This paper cites A “local climate zone.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning A “local climate zone
Reference 46
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 2d24a5b5-ebfb-471e-8205-f2b4717ff687 · outbound
This paper cites Mapping local climate zones for a worldwide database of the form and function of cities,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning Mapping local climate zones for a worldwide database of the form and function of cities,
Reference 47
- Resolution
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- raw_fallback, observed 2026-08-11T19:42:22.231870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2815b9e0-fa5c-490f-8196-bcefdc897900 · outbound
This paper cites The urban morphology on our planet–global perspec- tives from space,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning The urban morphology on our planet–global perspec- tives from space,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T19:42:22.221081Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c2fb413c-f890-48f0-ba99-f51b99fae29c · outbound
This paper cites A global map of local climate zones to support earth system modelling and urban-scale environmental science,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning A global map of local climate zones to support earth system modelling and urban-scale environmental science,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T19:42:22.210723Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 799e7af4-08ec-43c1-b57d-04e3d9a36ee7 · outbound
This paper cites So2sat lcz42: a benchmark data set for the classification of global local climate zones [software and data sets],.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning So2sat lcz42: a benchmark data set for the classification of global local climate zones [software and data sets],
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T19:42:22.200366Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 18ff008f-e1de-41e5-98f2-1cb754acd285 · outbound
This paper cites A framework for large-scale mapping of human settlement extent from sentinel-2 images via fully convolutional neural networks,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning A framework for large-scale mapping of human settlement extent from sentinel-2 images via fully convolutional neural networks,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T19:42:22.189530Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1ca5f95a-1644-4c0d-bf48-78950f0bdc9e · outbound
This paper cites On calibration of modern neural networks,.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning On calibration of modern neural networks,
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T19:42:21.997418Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.