Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product | Paper Citation Record

Observation 7cf37b1c-6524-4fb8-bc3c-de6064f6c13f · outbound

This paper cites Long-term perspective on wildfires in the western usa,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Long-term perspective on wildfires in the western usa,

Reference 1

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Observation da16f9ca-f374-437b-96b1-36f6936ed147 · outbound

This paper cites Landslide erosion coupled to tectonics and river incision,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Landslide erosion coupled to tectonics and river incision,

Reference 2

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This paper cites Global glacial isostasy and the surface of the ice-age earth: the ice-5g (vm2) model and grace,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Global glacial isostasy and the surface of the ice-age earth: the ice-5g (vm2) model and grace,

Reference 3

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Observation 81c227e4-d802-4d8b-9587-7984bbc44415 · outbound

This paper cites Classifying drivers of global forest loss,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Classifying drivers of global forest loss,

Reference 4

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This paper cites High-resolution global maps of 21st-century forest cover change,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product High-resolution global maps of 21st-century forest cover change,

Reference 5

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Observation 6e9d5c09-e699-4c87-9e0a-7f0f21d4ebe2 · outbound

This paper cites Ten ways remote sensing can contribute to conservation,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Ten ways remote sensing can contribute to conservation,

Reference 6

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Observation 893479a1-a40c-4952-9b3b-ede7a8b8c872 · outbound

This paper cites The impacts of climate change on terrestrial earth surface systems,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product The impacts of climate change on terrestrial earth surface systems,

Reference 7

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Observation cd6f974b-0af3-4a99-adca-1bdeebf58329 · outbound

This paper cites Assessing sustainable development prospects through remote sensing: A review,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Assessing sustainable development prospects through remote sensing: A review,

Reference 8

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Observation f4e3dc69-446f-449b-89d6-5bc9d9933750 · outbound

This paper cites Human population growth and global land-use/cover change,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Human population growth and global land-use/cover change,

Reference 9

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Observation fc3a832c-c9af-4972-adde-9af4b08c4a5a · outbound

This paper cites Deep learning-based damage mapping with insar coherence time series,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Deep learning-based damage mapping with insar coherence time series,

Reference 11

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Observation b43f3ce4-5e88-43ca-9569-f9dd4425a8c6 · outbound

This paper cites Urban flood mapping with bitemporal multispectral imagery via a self-supervised learning framework,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Urban flood mapping with bitemporal multispectral imagery via a self-supervised learning framework,

Reference 12

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Observation 72e5489a-267f-4db9-b79a-e3a83d9e82ee · outbound

This paper cites Sen1floods11: a georeferenced dataset to train and test deep learning flood algorithms for sentinel-1,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Sen1floods11: a georeferenced dataset to train and test deep learning flood algorithms for sentinel-1,

Reference 13

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Observation 0a6d1b5f-b92b-4af4-8091-359a9bfd1afc · outbound

This paper cites Generating landslide density heatmaps for rapid detection using open-access satellite radar data in google earth engine,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Generating landslide density heatmaps for rapid detection using open-access satellite radar data in google earth engine,

Reference 14

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Observation 5fa786c7-bbe1-4bc1-b6e1-47e4dc9df9b2 · outbound

This paper cites Landslide mapping using object-based image analysis and open source tools,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Landslide mapping using object-based image analysis and open source tools,

Reference 15

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Observation a85c63ca-4bfb-4124-b899-5d2a72a09da8 · outbound

This paper cites Near real-time wildfire progression monitoring with sentinel-1 sar time series and deep learning,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Near real-time wildfire progression monitoring with sentinel-1 sar time series and deep learning,

Reference 16

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Observation 8a1beb6d-5a10-4d3c-9ec3-09780f8cb094 · outbound

This paper cites Change detection techniques for ers- 1 sar data,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Change detection techniques for ers- 1 sar data,

Reference 17

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Observation d7c2ebf3-e87a-495e-94f7-253200f1a1fc · outbound

This paper cites Synthetic aperture radar interferometry,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Synthetic aperture radar interferometry,

Reference 18

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Observation 48ece76a-883c-49f3-9574-61ffd3cc0600 · outbound

This paper cites How satellite insar has grown from opportunistic science to routine monitoring over the last decade,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product How satellite insar has grown from opportunistic science to routine monitoring over the last decade,

Reference 19

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This paper cites an unresolved cited work.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Unresolved cited work

Reference 20

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Observation 1491292a-058d-452b-9a3f-bca98e3cfb5a · outbound

This paper cites Evaluation of coherent and incoherent landslide detection methods based on synthetic aperture radar for rapid response: A case study for the 2018 hokkaido landslides,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Evaluation of coherent and incoherent landslide detection methods based on synthetic aperture radar for rapid response: A case study for the 2018 hokkaido landslides,

Reference 21

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Observation 4ea00321-c94d-459b-98a5-416ae1f3f6a0 · outbound

This paper cites Damage-mapping algorithm based on coherence model using multitemporal polarimetric– interferometric sar data,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Damage-mapping algorithm based on coherence model using multitemporal polarimetric– interferometric sar data,

Reference 22

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Observation 1816c27d-cbb2-40a9-b7f5-c3207f221766 · outbound

This paper cites OPERA Product Validation Document v1.5,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product OPERA Product Validation Document v1.5,

Reference 23

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Observation 8200a12d-778e-44ca-8b6e-8e3260f3f964 · outbound

This paper cites Flattening gamma: Radiometric terrain correction for sar imagery,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Flattening gamma: Radiometric terrain correction for sar imagery,

Reference 24

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Observation 2d197bc1-a470-4ec5-9543-16ce89cd2741 · outbound

This paper cites The opera radiometric terrain corrected sar backscatter from sentinel-1 (rtc- s1) product,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product The opera radiometric terrain corrected sar backscatter from sentinel-1 (rtc- s1) product,

Reference 25

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Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Unresolved cited work

Reference 26

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This paper cites Oliver and S.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Oliver and S

Reference 27

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Observation c6bbc106-8041-44aa-8683-7c4aaf2a83f3 · outbound

This paper cites Harmonizing sar and optical data to map surface water extent: A deep learning approach,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Harmonizing sar and optical data to map surface water extent: A deep learning approach,

Reference 28

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Observation 9440d309-45d5-413d-b2be-f9a650f7c93b · outbound

This paper cites Improving landslide detection on sar data through deep learning,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Improving landslide detection on sar data through deep learning,

Reference 29

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This paper cites Sentinel-1 sar-based globally distributed landslide detection by deep neural networks,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Sentinel-1 sar-based globally distributed landslide detection by deep neural networks,

Reference 30

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This paper cites A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,

Reference 31

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This paper cites Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,

Reference 32

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This paper cites HLS Burn Scars Dataset – Hugging Face,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product HLS Burn Scars Dataset – Hugging Face,

Reference 33

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This paper cites Sen12-flood: a sar and multispectral dataset for flood detection,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Sen12-flood: a sar and multispectral dataset for flood detection,

Reference 34

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This paper cites Changemamba: Remote sensing change detection with spatio-temporal state space model,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Changemamba: Remote sensing change detection with spatio-temporal state space model,

Reference 35

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This paper cites Masked autoencoders are scalable vision learners,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Masked autoencoders are scalable vision learners,

Reference 36

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This paper cites Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data,

Reference 37

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Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product A simple frame- work for contrastive learning of visual representations,

Reference 38

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Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Unresolved cited work

Reference 39

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Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Sequence to Sequence Learning with Neural Networks

Reference 40

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This paper cites Attention is all you need,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Attention is all you need,

Reference 41

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Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Language models are few-shot learners,

Reference 42

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 43

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This paper cites Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.337640Z

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This paper cites Lightweight, Pre-trained Transformers for Remote Sensing Timeseries.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 45

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Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Unresolved cited work

Reference 46

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This paper cites Copernicus EMS Rapid Mapping,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Copernicus EMS Rapid Mapping,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.297287Z

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This paper cites dist-s1-events – GitHub repository,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product dist-s1-events – GitHub repository,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.266640Z

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This paper cites an unresolved cited work.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:16:20.238652Z

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This paper cites Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.220373Z

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Observation f42404bc-ce2d-42af-b001-3f4c4bd94f16 · outbound

This paper cites Sentinel-1 Acquisition Maps,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Sentinel-1 Acquisition Maps,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.198669Z

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This paper cites Opera dist product - algorithm theoretical basis document,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Opera dist product - algorithm theoretical basis document,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.178858Z

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Observation b94b3c7f-58d0-43fb-bfa2-199700a986a7 · outbound

This paper cites Mapping the spatial-temporal variability of tropical forests by alos-2 l-band sar big data analysis,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Mapping the spatial-temporal variability of tropical forests by alos-2 l-band sar big data analysis,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.164258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fd58ffb3-6095-47e8-8beb-8dc971788b9a · outbound

This paper cites Forest disturbance alerts for the congo basin using sentinel-1,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Forest disturbance alerts for the congo basin using sentinel-1,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.033905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60e01f90-a8fc-4d25-90df-1b933f06b42f · outbound

This paper cites Characterizing tropical forest cover loss using dense sentinel-1 data and active fire alerts,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Characterizing tropical forest cover loss using dense sentinel-1 data and active fire alerts,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.151091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ede2f788-8fd5-4b07-a0c0-0eed9e70dbdd · outbound

This paper cites How to Compare Noisy Patches? Patch Similarity beyond Gaussian Noise,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product How to Compare Noisy Patches? Patch Similarity beyond Gaussian Noise,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.116055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 73b8f171-f476-4300-931e-8bc59a5c3ca3 · outbound

This paper cites War related building damage assessment in kyiv, ukraine, using sentinel-1 radar and sentinel-2 optical images,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product War related building damage assessment in kyiv, ukraine, using sentinel-1 radar and sentinel-2 optical images,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.100731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4ae6c1be-94a2-4d8a-8887-2fe9ac75c894 · outbound

This paper cites Augmentation of wrf-hydro to simu- late overland-flow-and streamflow-generated debris flow susceptibility in burn scars,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Augmentation of wrf-hydro to simu- late overland-flow-and streamflow-generated debris flow susceptibility in burn scars,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.080828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c897f428-faf5-4eeb-bf22-89d948395081 · outbound

This paper cites Sentinel-1 sar amplitude imagery for rapid landslide detection,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Sentinel-1 sar amplitude imagery for rapid landslide detection,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.056249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 722a3e6c-8977-4881-aa31-bbe61fd15dcb · outbound

This paper cites Landslide failures detection and mapping using syn- thetic aperture radar: Past, present and future,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Landslide failures detection and mapping using syn- thetic aperture radar: Past, present and future,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.031346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a455e64b-b997-4988-92f8-c8b9800f2226 · outbound

This paper cites Detecting coseismic landslides in gee using machine learning algorithms on combined optical and radar imagery,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Detecting coseismic landslides in gee using machine learning algorithms on combined optical and radar imagery,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:20.014945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 329d5a8d-4e1b-4965-93f8-8162d3fe1829 · outbound

This paper cites Ratio-based Multitemporal SAR Images Denoising: RABASAR,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Ratio-based Multitemporal SAR Images Denoising: RABASAR,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.982831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 50651c2d-aa0f-443f-b8a9-00c0db929f1c · outbound

This paper cites Monitoring forest loss in alos/palsar time-series with superpixels,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Monitoring forest loss in alos/palsar time-series with superpixels,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.955659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1536423a-629f-40a3-91b4-f312832ab3ad · outbound

This paper cites Unsupervised change-detection based on convolutional-autoencoder feature extrac- tion,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Unsupervised change-detection based on convolutional-autoencoder feature extrac- tion,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.935621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 253213a7-dbce-4253-b654-e32929bcb225 · outbound

This paper cites Automatic Analysis of the Difference Image for Unsupervised Change Detection,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Automatic Analysis of the Difference Image for Unsupervised Change Detection,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.917419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5fbdd88d-6ef6-4b9c-b2ec-50da27064fab · outbound

This paper cites Rapid and robust monitoring of flood events using sentinel- 1 and landsat data on the google earth engine,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Rapid and robust monitoring of flood events using sentinel- 1 and landsat data on the google earth engine,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.887051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b2675313-9842-4594-8eb0-a8752476f100 · outbound

This paper cites Characterizing tropical forest cover loss using dense sentinel-1 data and active fire alerts,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Characterizing tropical forest cover loss using dense sentinel-1 data and active fire alerts,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.867992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5c4281b1-cced-4722-b797-eb71973b91ba · outbound

This paper cites A Change Detection Reality Check.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product A Change Detection Reality Check

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.146304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1367007-12f2-45d5-8d70-1012ef996a9a · outbound

This paper cites Learning spectral-spatial- temporal features via a recurrent convolutional neural network for change detection in multispectral imagery,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Learning spectral-spatial- temporal features via a recurrent convolutional neural network for change detection in multispectral imagery,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.839531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation df07c7ed-2e2d-4062-bd02-e6726acf5b7a · outbound

This paper cites An Unsupervised Approach based on the Generalized Gaussian Model to Automatic Change Detec- tion in Multitemporal SAR images,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product An Unsupervised Approach based on the Generalized Gaussian Model to Automatic Change Detec- tion in Multitemporal SAR images,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.820729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3d9028df-abaf-409a-bf63-57b4d20a1373 · outbound

This paper cites Rapid damage mapping for the 2015 m w 7.8 gorkha earthquake using synthetic aperture radar data from cosmo–skymed and alos-2 satellites,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Rapid damage mapping for the 2015 m w 7.8 gorkha earthquake using synthetic aperture radar data from cosmo–skymed and alos-2 satellites,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.790233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3930b6a3-18b1-4faf-b9b9-6be8d1801cdd · outbound

This paper cites NASA Jet Propulsion Laboratory, 2018, 261 pp.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product NASA Jet Propulsion Laboratory, 2018, 261 pp

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.758629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4770f130-781f-4723-9112-24182c4b9acc · outbound

This paper cites Forest disturbance product gener- ation,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Forest disturbance product gener- ation,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.739515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8baa0c0f-fa27-4c79-b755-60252ba6dc7e · outbound

This paper cites Adapting cusum algorithm for site-specific forest conditions to detect tropical deforestation,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Adapting cusum algorithm for site-specific forest conditions to detect tropical deforestation,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.719204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aefadc15-0982-43f3-b22f-486e17301e32 · outbound

This paper cites Coherent change detection using insar temporal decorrelation model: A case study for volcanic ash detection,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Coherent change detection using insar temporal decorrelation model: A case study for volcanic ash detection,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.695069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9564b4b4-b63a-4f0e-91e7-8aa252288469 · outbound

This paper cites GPT-4 Technical Report.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product GPT-4 Technical Report

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.226073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 816e023e-d656-404d-a6e4-4b5a25c42405 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.243751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47d42b17-ee13-4949-a11d-3c296026dd0c · outbound

This paper cites Masked autoencoders as spatiotemporal learners,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Masked autoencoders as spatiotemporal learners,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.670264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ffc300f3-a7c7-4e8b-9890-50aa85ffe70d · outbound

This paper cites Segment anything,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Segment anything,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.254057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de186ba6-46cf-4f5c-b698-28806281825f · outbound

This paper cites Masked autoencoders as spatiotemporal learners,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Masked autoencoders as spatiotemporal learners,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.602540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation af201056-233d-460c-a5dc-5d624ccccad4 · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Florence: A New Foundation Model for Computer Vision

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.267860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10c3c27b-3e43-412b-b4e0-75f7f87797fa · outbound

This paper cites Geo- bench: Toward foundation models for earth monitoring,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Geo- bench: Toward foundation models for earth monitoring,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.574996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3638b9d7-e737-4569-ad8c-5fd0497e2ce0 · outbound

This paper cites Scale- mae: A scale-aware masked autoencoder for multiscale geospatial representation learning,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Scale- mae: A scale-aware masked autoencoder for multiscale geospatial representation learning,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.544787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 47d2a8e9-985b-496c-85d0-bd2295a702af · outbound

This paper cites Foundation Models for Generalist Geospatial Artificial Intelligence.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.306624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac4334a4-465a-4f9f-92f5-2862bf52baa0 · outbound

This paper cites Model repository,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Model repository,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.519202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5572e591-68f0-4219-bba5-c91d10d95367 · outbound

This paper cites Ssl4eo- l: Datasets and foundation models for landsat imagery,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Ssl4eo- l: Datasets and foundation models for landsat imagery,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.495244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d1afbe3b-748f-41e8-a0e2-cc569b05e141 · outbound

This paper cites Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.338439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b19bbb3-e452-47b7-9d93-7ea725aa7dbd · outbound

This paper cites Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:16:18.846628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cbbd88a7-4397-42ad-bb18-d78e98fa86d1 · outbound

This paper cites Auto-Encoding Variational Bayes.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Auto-Encoding Variational Bayes

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.355169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbf8e110-dbbd-4166-b83f-e4ff96ea5c9b · outbound

This paper cites Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:16:18.798685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 03a118c6-c039-46db-8405-cf6d69aabc89 · outbound

This paper cites Denoising diffusion probabilistic models,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Denoising diffusion probabilistic models,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.371832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9820b145-3952-4de4-b392-e8472cea805c · outbound

This paper cites Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre- training,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre- training,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.468093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5b387eed-e668-4d1f-bb13-e3a5bafc3aec · outbound

This paper cites Forest disturbance detection via self-supervised and transfer learning with sentinel-1 2 images,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Forest disturbance detection via self-supervised and transfer learning with sentinel-1 2 images,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.450543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1f35d188-3f41-4cb5-bd0e-b867a3482b28 · outbound

This paper cites Deep residual learning for image recognition,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Deep residual learning for image recognition,

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.388371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ad645a4-4b33-45c1-82e1-8c190835e8aa · outbound

This paper cites Layer normalization,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Layer normalization,

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.394609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c64730e-4379-4ae8-a983-f5e1f26b8496 · outbound

This paper cites Planet application program interface: In space for life on earth,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Planet application program interface: In space for life on earth,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.408952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 93334f6b-1a07-46e3-96d4-61a4e2be0e20 · outbound

This paper cites Inferno Scars Valpara ´ıso,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Inferno Scars Valpara ´ıso,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.395631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4a0fb8c0-12f3-4de2-bbc2-f35fd611b8a7 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Adam: A Method for Stochastic Optimization

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.413048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5281161-492f-4f10-af72-33ed8f815ea0 · outbound

This paper cites Esri World Imagery,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Esri World Imagery,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.376031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bdb3b5c8-444a-40b5-bcdb-802609fb1a4d · outbound

This paper cites Rapid mapping of landslides on sar data by attention u- net,.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product Rapid mapping of landslides on sar data by attention u- net,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:19.361326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b655ed0f-78a8-49be-a5ab-29d7bc684ece · outbound

This paper cites On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.

Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:18.429213Z

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