Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:42:36.005843Z
Paper Citation Record · LEDGER
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series
As of 19 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 2 inbound Pith citation observations for arXiv:2506.12885.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T16:26:21.096892Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T18:16:15.004306Z
Reference resolution
65 of 65 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 3f660532-7da4-476d-980f-73b3aa10782f · outbound
This paper cites Ai-aristotle: A physics-informed framework for systems biology gray- box identification.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Ai-aristotle: A physics-informed framework for systems biology gray- box identification
Reference 1
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 656e47e1-e721-4388-bda0-705d7edc87bf · outbound
This paper cites Vits for sits: vision transformers for satellite image time series.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Vits for sits: vision transformers for satellite image time series
Reference 2
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b454a715-2013-4e30-a29e-c2606bf1dcd4 · outbound
This paper cites Proba- bilistic crop type mapping for ex-ante modelling and spatial disaggregation.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Proba- bilistic crop type mapping for ex-ante modelling and spatial disaggregation
Reference 3
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3de8868b-bf11-4980-a873-1ed969ee639c · outbound
This paper cites Country- wide retrieval of forest structure from optical and sar satellite imagery with deep ensembles.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Country- wide retrieval of forest structure from optical and sar satellite imagery with deep ensembles
Reference 4
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0e195396-f2d0-4b35-9908-aef57344aaef · outbound
This paper cites Mapping of crop types and crop sequences with combined time series of sentinel-1, sentinel-2 and landsat 8 data for germany.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Mapping of crop types and crop sequences with combined time series of sentinel-1, sentinel-2 and landsat 8 data for germany
Reference 5
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8b8fbd5c-0e0e-481f-be21-4eb020b0577b · outbound
This paper cites Weight uncertainty in neural networks.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Weight uncertainty in neural networks
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8b839add-f455-4874-b39a-ba4eb1dc1645 · outbound
This paper cites Revisiting the Encoding of Satellite Image Time Series.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Revisiting the Encoding of Satellite Image Time Series
Reference 7
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 59776f9b-af89-4f46-8a17-18e0176841a9 · outbound
This paper cites Xgboost: A scalable tree boosting system.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Xgboost: A scalable tree boosting system
Reference 8
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 49084c5a-3604-44e0-b804-f4d5d82c1b16 · outbound
This paper cites Multi-year cropland mapping based on remote sensing data: A case study for the khabarovsk territory, russia.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Multi-year cropland mapping based on remote sensing data: A case study for the khabarovsk territory, russia
Reference 9
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5fea6499-f43a-4ae5-9c89-232a5cb8540d · outbound
This paper cites Masksembles for uncertainty estimation.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Masksembles for uncertainty estimation
Reference 10
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3e116107-f728-4f75-9fdb-0792723dfaa5 · outbound
This paper cites The climate of Switzerland, 2024.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series The climate of Switzerland, 2024
Reference 11
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dc5acafa-b8c5-4a3f-901e-b55a3a98060d · outbound
This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Reference 12
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 07d8f966-6fe8-4457-80c2-929db70c942f · outbound
This paper cites Training sample selection for robust multi-year within-season crop classification using machine learning.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Training sample selection for robust multi-year within-season crop classification using machine learning
Reference 13
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- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d06bd5b7-c420-4d4c-b202-b8b8aea1031e · outbound
This paper cites Panoptic segmentation of satellite image time series with convolu- tional temporal attention networks.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Panoptic segmentation of satellite image time series with convolu- tional temporal attention networks
Reference 14
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 623aca72-cf47-4700-a5e6-9993e9d61837 · outbound
This paper cites Satellite image time series clas- sification with pixel-set encoders and temporal self-attention.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Satellite image time series clas- sification with pixel-set encoders and temporal self-attention
Reference 15
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cc17812e-56d2-4089-97f7-e79c62a33995 · outbound
This paper cites White, and Michael A.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series White, and Michael A
Reference 16
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4deacc4d-150f-4416-a5cc-87fcdb57ee36 · outbound
This paper cites Imbalanced datasets.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Imbalanced datasets
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation af9290c9-abfd-42f8-9ee4-44e4243df9b8 · outbound
This paper cites Gustafsson, Martin Danelljan, and Thomas B.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Gustafsson, Martin Danelljan, and Thomas B
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9a45595d-98ed-4b95-a109-98debc586af7 · outbound
This paper cites Lora-ensemble: Efficient uncertainty modelling for self-attention networks.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Lora-ensemble: Efficient uncertainty modelling for self-attention networks
Reference 19
- Resolution
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Source-reported events for the cited work
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Observation b26ef598-bcb0-4a84-91ee-161489710ae8 · outbound
This paper cites Hopcroft, and Kilian Q.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Hopcroft, and Kilian Q
Reference 20
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dd005cc9-d2b6-4a38-9fb1-fe9badad40d3 · outbound
This paper cites What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8a97fa0d-e536-4414-b545-80a753c87086 · outbound
This paper cites General- ization enhancement strategies to enable cross-year cropland mapping with convolutional neural networks trained using historical samples.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series General- ization enhancement strategies to enable cross-year cropland mapping with convolutional neural networks trained using historical samples
Reference 22
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2b3e8944-3658-45a8-9db9-b5e96f3c06de · outbound
This paper cites Simple and scalable predictive uncertainty estima- tion using deep ensembles.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Simple and scalable predictive uncertainty estima- tion using deep ensembles
Reference 23
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 833a1596-6cd9-4c65-bd0d-7b304d6492c9 · outbound
This paper cites A high-resolution canopy height model of the earth.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series A high-resolution canopy height model of the earth
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 96deca38-37ce-49ed-b28a-05b153fbfe17 · outbound
This paper cites Feature-Wise Bias Amplification.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Feature-Wise Bias Amplification
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:42:35.859510Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d1c045b-f968-4db4-83b6-b084799cdc1e · outbound
This paper cites Multi-year crop type mapping using sentinel-2 imagery and deep seman- tic segmentation algorithm in the hetao irrigation district in china.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Multi-year crop type mapping using sentinel-2 imagery and deep seman- tic segmentation algorithm in the hetao irrigation district in china
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.653275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation ceb9f728-4bcc-4d86-82fd-0f362d22151f · outbound
This paper cites Cropsight: Towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and planetscope satellite imagery.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Cropsight: Towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and planetscope satellite imagery
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7b8dc69c-c196-46ab-ba00-fab33a32b7e0 · outbound
This paper cites Refinement of cropland data layer with effective confidence layer interval and image filtering.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Refinement of cropland data layer with effective confidence layer interval and image filtering
Reference 28
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5d314873-e98d-4693-ace0-eb82c0fbfa5a · outbound
This paper cites Crop classification under varying cloud cover with neural ordinary differential equations.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Crop classification under varying cloud cover with neural ordinary differential equations
Reference 29
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a508332f-b8fd-462b-9cea-b0c017039c1f · outbound
This paper cites The need for biases in learning generaliza- tions.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series The need for biases in learning generaliza- tions
Reference 30
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e464b478-4324-4b91-a6dc-851658488730 · outbound
This paper cites Gener- alized classification of satellite image time series with ther- mal positional encoding.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Gener- alized classification of satellite image time series with ther- mal positional encoding
Reference 31
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a389791b-5974-4208-b9c8-4b0a55539860 · outbound
This paper cites Multi-year mapping of cropping systems in regions with smallholder farms from sentinel-2 images in google earth en- gine.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Multi-year mapping of cropping systems in regions with smallholder farms from sentinel-2 images in google earth en- gine
Reference 32
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0adcade9-3a97-4346-b348-00d959355f87 · outbound
This paper cites SITSMamba for Crop Classification based on Satellite Image Time Series.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series SITSMamba for Crop Classification based on Satellite Image Time Series
Reference 33
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 47188e5b-e567-484d-9540-0424747bb6b9 · outbound
This paper cites Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T00:42:36.058335Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 65c51008-ed9a-4609-b964-9322a21cd4ea · outbound
This paper cites The eurocropsml time series benchmark dataset for few-shot crop type classification in europe.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series The eurocropsml time series benchmark dataset for few-shot crop type classification in europe
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.569672Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 839d8c4b-2053-4983-8eb5-c2adb3420962 · outbound
This paper cites U- net: Convolutional networks for biomedical image segmen- tation.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series U- net: Convolutional networks for biomedical image segmen- tation
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:42:35.901001Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31eda540-5533-456a-b518-aedd1235dc1d · outbound
This paper cites Dos Santos, Maria Vakalopoulou, Ronny H ¨ansch, Stine Hansen, Keiller Nogueira, Jonathan Prexl, and Devis Tuia.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Dos Santos, Maria Vakalopoulou, Ronny H ¨ansch, Stine Hansen, Keiller Nogueira, Jonathan Prexl, and Devis Tuia
Reference 37
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 309b3d97-56e8-4236-9d5d-b325eacc8596 · outbound
This paper cites Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satel- lite images.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satel- lite images
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.541074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4554fe56-dd04-4d07-a8c4-a8d7322082f3 · outbound
This paper cites Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satel- lite images.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satel- lite images
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.528624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7d2cbf68-8097-4c68-90ff-f7861e15a9a8 · outbound
This paper cites Multi-temporal land cover classification with sequential recurrent encoders.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Multi-temporal land cover classification with sequential recurrent encoders
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.518422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f665169a-a2ce-40e7-9d30-73a3ffcdee28 · outbound
This paper cites Self-attention for raw optical satellite time series classification.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Self-attention for raw optical satellite time series classification
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.507972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 976df691-abef-4977-a2bd-041e4b1c0e0f · outbound
This paper cites Breizhcrops: A satellite time series dataset for crop type identification.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Breizhcrops: A satellite time series dataset for crop type identification
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.498183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 61b55197-e006-438a-9817-0cbe788d1ee4 · outbound
This paper cites End-to-end learned early classification of time series for in-season crop type mapping.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series End-to-end learned early classification of time series for in-season crop type mapping
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.488128Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cacfba48-0f74-496f-a2a3-aa660afa4b51 · outbound
This paper cites Lightweight temporal self-attention for classifying satellite images time series.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Lightweight temporal self-attention for classifying satellite images time series
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.478441Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation bcfa961f-9d29-4999-bb87-72e46d6a245a · outbound
This paper cites Leveraging Class Hierarchies with Metric-Guided Prototype Learning.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Leveraging Class Hierarchies with Metric-Guided Prototype Learning
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T00:42:36.042634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cedcca26-5b68-453a-94e7-44d74e664546 · outbound
This paper cites Eurocrops: The largest harmonized open crop dataset across the european union.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Eurocrops: The largest harmonized open crop dataset across the european union
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.468644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4d7a75a8-2703-4206-8fbc-5e9b5ab7de74 · outbound
This paper cites A sentinel-2 multiyear, multicountry benchmark dataset for crop classification and segmentation with deep learning.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series A sentinel-2 multiyear, multicountry benchmark dataset for crop classification and segmentation with deep learning
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.457701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 08d997d7-c762-4106-a181-4dd8f6db91e2 · outbound
This paper cites Vits for sits: Vision transformers for satellite image time series.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Vits for sits: Vision transformers for satellite image time series
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.447258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2b08832b-24d8-4eef-9d75-4c1496686a11 · outbound
This paper cites Attention mechanism-based deep learning approach for wheat yield estimation and uncer- tainty analysis from remotely sensed variables.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Attention mechanism-based deep learning approach for wheat yield estimation and uncer- tainty analysis from remotely sensed variables
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.437378Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation da206ba7-69bd-45fb-add0-7760dd08385c · outbound
This paper cites Deep learning for vegetation clas- sification from optical satellite image time series.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Deep learning for vegetation clas- sification from optical satellite image time series
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.426958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c910253f-490f-4f87-9ccd-4b5038a31f3f · outbound
This paper cites Crop mapping from image time series: deep learning with multi-scale label hierarchies.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Crop mapping from image time series: deep learning with multi-scale label hierarchies
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.416774Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation ba5e647f-b1d9-42f8-99d2-63f17f65b27b · outbound
This paper cites Gating revisited: Deep multi- layer rnns that can be trained.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Gating revisited: Deep multi- layer rnns that can be trained
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.406358Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 230096a6-f436-4b42-a1d7-f336de8f9581 · outbound
This paper cites FiLM-Ensemble: Probabilistic deep learning via feature- wise linear modulation.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series FiLM-Ensemble: Probabilistic deep learning via feature- wise linear modulation
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.396229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 87e64404-efc9-42ed-ba51-47c808f67913 · outbound
This paper cites Country-wide cross-year crop map- ping from optical satellite image time series.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Country-wide cross-year crop map- ping from optical satellite image time series
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.385958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6d208ba8-4b5b-48a3-b975-1ab932fa9481 · outbound
This paper cites Hierarchical crop map- ping from satellite image sequences with recurrent neural networks.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Hierarchical crop map- ping from satellite image sequences with recurrent neural networks
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.376038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3728aad6-9cfb-4930-bbd6-b3c58cd2f406 · outbound
This paper cites Attention is all you need.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Attention is all you need
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.365416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation bf9e4b9a-f0cc-416c-a518-0de641246e64 · outbound
This paper cites Equivariance and invariance inductive bias for learning from insufficient data.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Equivariance and invariance inductive bias for learning from insufficient data
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.355125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f8e3723c-a37b-46b9-9c49-f7e0110e91db · outbound
This paper cites Remote sensing for agricultural applications: A meta-review.Remote sensing of environment, 236:111402, 2020.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Remote sensing for agricultural applications: A meta-review.Remote sensing of environment, 236:111402, 2020
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.343536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 052d3ecf-98cf-4d0c-839c-05c4582afa51 · outbound
This paper cites Bayesian learning via stochastic gradient langevin dynamics.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Bayesian learning via stochastic gradient langevin dynamics
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.332561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9e29570a-577b-481a-8c60-f4832a050e17 · outbound
This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Segformer: Simple and efficient design for semantic segmentation with transform- ers
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.320430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cc5a9722-7aa7-4252-aeca-ad17aa803d97 · outbound
This paper cites [44] then proposed the lightweight Temporal Atten- 12 Figure 11.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series [44] then proposed the lightweight Temporal Atten- 12 Figure 11
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.309281Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d4acca88-89c0-40dd-8825-1996530f6151 · outbound
This paper cites [13] investigated ways to optimize the use of multi-year samples in a within-season crop classification model.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series [13] investigated ways to optimize the use of multi-year samples in a within-season crop classification model
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.299069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b5581bce-afcd-4410-93ca-3557ae1bd4c2 · outbound
This paper cites Very recently, [22] shows improved results for cross-year crop mapping combining a U-Net with photometric augmenta- tion, Tversky-Focal loss, and Monte Carlo (MC) Dropout.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Very recently, [22] shows improved results for cross-year crop mapping combining a U-Net with photometric augmenta- tion, Tversky-Focal loss, and Monte Carlo (MC) Dropout
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.288615Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5057863b-2095-434a-8353-e23953ea57b9 · outbound
This paper cites In the context of crop yield and type mapping, several re- cent studies have adopted stochastic inference techniques.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series In the context of crop yield and type mapping, several re- cent studies have adopted stochastic inference techniques
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:42:36.277905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 08fa2f32-e62f-4063-92dc-9eb9596330ff · outbound
This paper cites an unresolved cited work.
$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series Unresolved cited work
Reference 65
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T00:42:36.265833Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
Observation 1400ecb0-0913-4522-8b7d-b022abe9c94d · inbound
Think with Extra-Image: A Farmland Segmentation Agent Driven by Spatio-Temporal Information Gain cites this paper.
Think with Extra-Image: A Farmland Segmentation Agent Driven by Spatio-Temporal Information Gain $T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T15:18:55.319438Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ad7e658a-a4e9-4820-ba12-1a7a6f34020e · inbound
SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping cites this paper.
SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping $T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series
Reference 46
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T16:26:21.300092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.