Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams | Paper Citation Record

Observation 6e2e9b6c-6699-4822-9399-735d50e00cf8 · outbound

This paper cites Geochat: Grounded large vision-language model for remote sensing,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Geochat: Grounded large vision-language model for remote sensing,

Reference 1

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Observation ca9bf469-76a6-4c05-8138-9188d8a1e11d · outbound

This paper cites Earthgpt: A universal multi-modal large language model for multi-sensor image comprehension in remote sensing domain,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Earthgpt: A universal multi-modal large language model for multi-sensor image comprehension in remote sensing domain,

Reference 2

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Observation 7ecc61f5-52f5-422c-90e8-c29c8051e633 · outbound

This paper cites Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery,

Reference 3

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Observation a2bf826d-af43-4be8-b820-fe91459d700e · outbound

This paper cites Spectralgpt: Spectral remote sensing foundation model,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Spectralgpt: Spectral remote sensing foundation model,

Reference 4

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Observation ac834deb-d4c3-493a-b79b-fff10ddc91d4 · outbound

This paper cites Earth ai: Unlocking geospatial insights with foundation models and cross-modal reasoning,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Earth ai: Unlocking geospatial insights with foundation models and cross-modal reasoning,

Reference 5

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Observation cc8f660b-8973-4505-a6ef-98627ce12128 · outbound

This paper cites Accurate medium-range global weather forecasting with pangu-weather,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Accurate medium-range global weather forecasting with pangu-weather,

Reference 6

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Observation 592e47af-40a3-4560-acef-b7605ab4b666 · outbound

This paper cites Learning skillful medium-range global weather forecast- ing,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Learning skillful medium-range global weather forecast- ing,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.507782Z

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Observation a64b336c-6001-4be7-9611-ac7889ae0544 · outbound

This paper cites Climax: A foundation model for weather and climate,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Climax: A foundation model for weather and climate,

Reference 8

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Observation bdeb6f0e-56a3-4567-82f2-ff47783cb860 · outbound

This paper cites Fengwu: Pushing the frontiers of global medium-range weather forecasting,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Fengwu: Pushing the frontiers of global medium-range weather forecasting,

Reference 9

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Observation 11b38c9e-c327-44fc-ab0e-61e48fc43d8e · outbound

This paper cites Foundation models for generalizable geospatial artificial intelligence,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Foundation models for generalizable geospatial artificial intelligence,

Reference 10

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Observation 121c2f26-fe75-467d-96aa-348bd73e7c38 · outbound

This paper cites XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?

Reference 11

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Observation 8d037971-d211-4b14-8468-85b12f148c7b · outbound

This paper cites Omniearth-bench: Towards holistic evaluation of earth’s six spheres and cross-spheres interactions with multimodal observational earth data,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Omniearth-bench: Towards holistic evaluation of earth’s six spheres and cross-spheres interactions with multimodal observational earth data,

Reference 12

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Observation 8c033838-395e-4658-9da7-7d829609cbe5 · outbound

This paper cites Vrsbench: A versatile vision- language benchmark dataset for remote sensing image understanding,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Vrsbench: A versatile vision- language benchmark dataset for remote sensing image understanding,

Reference 13

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Observation 343fc90b-11bf-425b-a875-1db4d1ddb812 · outbound

This paper cites Rsrsd-5m: A large-scale unlabeled dataset for pretraining earth foundation models,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Rsrsd-5m: A large-scale unlabeled dataset for pretraining earth foundation models,

Reference 14

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Observation 74b6e295-4b59-4731-95c1-feb792132bdb · outbound

This paper cites Deep learning for hourly geographical fore- casting at kilometer scale with metnet-3,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Deep learning for hourly geographical fore- casting at kilometer scale with metnet-3,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.524083Z

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Observation e1a395e2-d86c-4eda-badf-f44216ad2fb6 · outbound

This paper cites Artificial intelligence for modeling and under- standing extreme weather and climate events,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Artificial intelligence for modeling and under- standing extreme weather and climate events,

Reference 16

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Observation 6fcfb218-a602-4d87-abdd-9b6779fefb19 · outbound

This paper cites Early warning of complex climate risk with integrated artificial intelligence,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Early warning of complex climate risk with integrated artificial intelligence,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.528035Z

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Observation 664e745e-82f1-4c3a-93fe-313860abdc99 · outbound

This paper cites Crisismmd: Multimodal twitter datasets of natural disasters,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Crisismmd: Multimodal twitter datasets of natural disasters,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.530196Z

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Observation 7fa37c3b-4244-4248-8e88-321275839837 · outbound

This paper cites Creating xbd: A dataset for assessing building damage from satellite imagery,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Creating xbd: A dataset for assessing building damage from satellite imagery,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.532407Z

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Observation c56449fb-da42-4555-960b-fda07d96a7f1 · outbound

This paper cites Sen12-flood: A multi-spectral active-passive satellite dataset for flood detection,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Sen12-flood: A multi-spectral active-passive satellite dataset for flood detection,

Reference 20

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Observation fb4f8ebf-5d06-4c2d-9367-f709d22cd14a · outbound

This paper cites Floodnet: A high-resolution aerial imagery dataset for post-flood scene understanding,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Floodnet: A high-resolution aerial imagery dataset for post-flood scene understanding,

Reference 21

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Observation a190d2d5-17d0-4101-9822-332997b82c4b · outbound

This paper cites Rescuenet: Joint building segmentation and damage assessment from satellite imagery,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Rescuenet: Joint building segmentation and damage assessment from satellite imagery,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.538389Z

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Observation f3b160d5-d3cd-4c1f-ac60-61e0f248abb3 · outbound

This paper cites Crasar-u-droids: A large-scale suas dataset for building damage assessment,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Crasar-u-droids: A large-scale suas dataset for building damage assessment,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.540287Z

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Observation 1e4e46a0-99dd-4f3b-ba29-b7d4f6b747e7 · outbound

This paper cites Disasterm3: A remote sensing vision-language dataset for disaster damage assessment and response,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Disasterm3: A remote sensing vision-language dataset for disaster damage assessment and response,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.542615Z

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Observation 64616c92-e551-4066-bb7d-78f96ebd79a0 · outbound

This paper cites Zeshot-vqa: Zero-shot visual question answering for natural disaster damage assessment,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Zeshot-vqa: Zero-shot visual question answering for natural disaster damage assessment,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.544774Z

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Observation 45b49d98-7a52-4fe7-be51-51dfb6769aee · outbound

This paper cites Disastervqa: Social media-based visual ques- tion answering benchmark for crisis response,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Disastervqa: Social media-based visual ques- tion answering benchmark for crisis response,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.546837Z

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Observation 7237227a-370c-44ab-89ad-2fbcdcfa90f3 · outbound

This paper cites Dora: An end-to-end agentic benchmark for real-world disaster response,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Dora: An end-to-end agentic benchmark for real-world disaster response,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.548773Z

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Observation 5efc118f-436d-4f1f-9b9e-a817ec59ee3d · outbound

This paper cites Bright: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Bright: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.550720Z

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Observation d6a9c9d4-a2bc-4478-b9e6-881e59bd8867 · outbound

This paper cites Constructing an extensible building damage dataset via semi-supervised fine-tuning across 12 natural disasters,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Constructing an extensible building damage dataset via semi-supervised fine-tuning across 12 natural disasters,

Reference 29

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Observation 105c143a-7d52-4ebc-8a2b-6702b6d7b880 · outbound

This paper cites Monitrs: Multimodal observations of natural incidents through remote sensing,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Monitrs: Multimodal observations of natural incidents through remote sensing,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.554903Z

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Observation acc76663-81d1-474b-9dfd-2831d200c2d3 · outbound

This paper cites Continuous monitoring of land cover changes using landsat time series,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Continuous monitoring of land cover changes using landsat time series,

Reference 31

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Observation 9c7c265f-f22b-4df8-8dc2-fe90780efadd · outbound

This paper cites Validation of earth observation time-series: A review for large-area and temporally dense land surface products,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Validation of earth observation time-series: A review for large-area and temporally dense land surface products,

Reference 32

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Observation 52635e3e-2ccf-4e80-93ee-c3f05f19f5bf · outbound

This paper cites Benchmark datasets for satellite image time series classification: A review,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Benchmark datasets for satellite image time series classification: A review,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.560880Z

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Observation 3f66039d-e576-470e-afb0-89a69624855c · outbound

This paper cites Remote sensing time series analysis: A review of data and applications,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Remote sensing time series analysis: A review of data and applications,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.562801Z

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Observation 807fd0b8-aca7-45ff-a197-2fbd207334bf · outbound

This paper cites Dynamicearthnet: Daily multi-spectral satellite dataset for semantic change segmentation,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Dynamicearthnet: Daily multi-spectral satellite dataset for semantic change segmentation,

Reference 35

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Observation 61e4f663-124c-4b8e-a150-7492d46bf6ec · outbound

This paper cites Fomo: Multi-modal, multi-scale and multi-task remote sensing foundation models for forest monitoring,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Fomo: Multi-modal, multi-scale and multi-task remote sensing foundation models for forest monitoring,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.567237Z

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Observation 580e411d-4659-4835-a817-d18620b43f19 · outbound

This paper cites Treefinder: A us-scale benchmark dataset for individual tree mortality monitoring using high-resolution aerial imagery,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Treefinder: A us-scale benchmark dataset for individual tree mortality monitoring using high-resolution aerial imagery,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.569344Z

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Observation fef073ce-db89-4a4b-9076-796859a43a4e · outbound

This paper cites Anysat: One earth observation model for many resolutions, scales, and modalities,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Anysat: One earth observation model for many resolutions, scales, and modalities,

Reference 38

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Observation d5b9b911-32d8-4fe6-b0d1-31fe0f6cd087 · outbound

This paper cites Terramind: Large-scale generative multimodality for earth observation,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Terramind: Large-scale generative multimodality for earth observation,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.573510Z

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Observation 7d1da24e-87b8-44ef-8076-4f92f4a8893e · outbound

This paper cites Omnigaia: Towards native omni-modal ai agents,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Omnigaia: Towards native omni-modal ai agents,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.575558Z

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Observation a764716d-9b74-42a3-bdef-8ea1aa9eb1ac · outbound

This paper cites Earth-agent: Unlocking the full landscape of earth observation with agents,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Earth-agent: Unlocking the full landscape of earth observation with agents,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.577600Z

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Observation 87064096-12a4-4298-bcf9-b72bca43d968 · outbound

This paper cites Terrabench: Can agents reason over heterogeneous earth-system data?.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Terrabench: Can agents reason over heterogeneous earth-system data?

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.579825Z

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Observation f221bd30-b595-4cb8-8890-cfc6b6542a6e · outbound

This paper cites Rescuenet: A high resolution uav semantic segmentation dataset for natural disaster damage assessment,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Rescuenet: A high resolution uav semantic segmentation dataset for natural disaster damage assessment,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.582054Z

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Observation be3507d0-6c4b-49f4-88a1-3075fa488c07 · outbound

This paper cites Rscc: A benchmark for remote sensing change caption- ing with rich human descriptions,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Rscc: A benchmark for remote sensing change caption- ing with rich human descriptions,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.584215Z

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Observation 0fca3355-91fd-41b1-bbc3-15632fa6a2b3 · outbound

This paper cites Anomaly-cd: Earth anomaly change detection with high- resolution time series,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Anomaly-cd: Earth anomaly change detection with high- resolution time series,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.586327Z

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Observation 07206f00-722c-4556-854c-8f17b597a062 · outbound

This paper cites Shield: Unsupervised detection of disaster- affected areas,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Shield: Unsupervised detection of disaster- affected areas,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.588436Z

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Observation 9e37acc9-882f-4f31-9b71-4bcbca7f4da4 · outbound

This paper cites A foundation model for the earth system,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams A foundation model for the earth system,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.590387Z

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Observation b91eb513-4e90-429d-a453-b1b79b5147f6 · outbound

This paper cites End-to-end data-driven weather prediction,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams End-to-end data-driven weather prediction,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.592497Z

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Observation 88695087-0330-482d-9f80-7257a4a26b00 · outbound

This paper cites Ai foundation models for weather and climate,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Ai foundation models for weather and climate,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.594487Z

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Observation 6d9412d2-83a8-4476-902c-9c5dc059be73 · outbound

This paper cites Ai in extreme weather events prediction and response: a systematic topic-model review (2015–2024),.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Ai in extreme weather events prediction and response: a systematic topic-model review (2015–2024),

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.596838Z

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Observation c257a7a8-c97b-4380-b89e-7f00a3553cc9 · outbound

This paper cites Remote sensing improves multi-hazard flooding and extreme heat detection by fivefold over current estimates,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Remote sensing improves multi-hazard flooding and extreme heat detection by fivefold over current estimates,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.599093Z

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Observation b38e0f58-f1ff-44f2-9410-cf73772f5211 · outbound

This paper cites Introducing claude opus 4.8,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Introducing claude opus 4.8,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.602224Z

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Observation f6e09e1d-e4f2-4a50-a3e5-fb8b8524eea3 · outbound

This paper cites Introducing gpt-5.5,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Introducing gpt-5.5,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.604338Z

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Observation 55d5ce36-f9db-442b-ba11-4df6569285cb · outbound

This paper cites Kimi-k2.6,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Kimi-k2.6,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.606732Z

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Unavailable: canonical work link unavailable.

Observation 2be8c08d-bace-4aae-b6f7-11ae929ba92e · outbound

This paper cites Qwen3.5: Towards native multimodal agents,.

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams Qwen3.5: Towards native multimodal agents,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T02:47:25.608993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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