$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series | Paper Citation Record

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

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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

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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

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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: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:37.034859Z

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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; raw_fallback, observed 2026-08-07T00:42:37.004358Z

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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: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:36.961293Z

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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

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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; raw_fallback, observed 2026-08-07T00:42:36.917942Z

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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; raw_fallback, observed 2026-08-07T00:42:36.870644Z

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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; raw_fallback, observed 2026-08-07T00:42:36.837248Z

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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: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:36.823461Z

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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: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:36.810535Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:36.796243Z

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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; raw_fallback, observed 2026-08-07T00:42:36.783619Z

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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; raw_fallback, observed 2026-08-07T00:42:36.769797Z

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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; raw_fallback, observed 2026-08-07T00:42:36.755855Z

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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; raw_fallback, observed 2026-08-07T00:42:36.743593Z

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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; raw_fallback, observed 2026-08-07T00:42:36.730938Z

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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

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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; raw_fallback, observed 2026-08-07T00:42:36.711689Z

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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; raw_fallback, observed 2026-08-07T00:42:36.698459Z

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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; raw_fallback, observed 2026-08-07T00:42:36.687625Z

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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; raw_fallback, observed 2026-08-07T00:42:36.676117Z

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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; raw_fallback, observed 2026-08-07T00:42:36.665022Z

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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

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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

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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; raw_fallback, observed 2026-08-07T00:42:36.641123Z

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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; raw_fallback, observed 2026-08-07T00:42:36.629061Z

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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; raw_fallback, observed 2026-08-07T00:42:36.616606Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+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

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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

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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

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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: verified exact; local_arxiv, observed 2026-08-07T00:42:36.073934Z

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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

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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

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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

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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; raw_fallback, observed 2026-08-07T00:42:36.552236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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

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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

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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

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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

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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

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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

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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

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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

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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 2026-08-09T06:31:02.800959+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

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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

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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

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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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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