Introduces Forged Calamity benchmark and shows that fine-tuned and zero-shot synthetic image detectors lose substantial accuracy on unseen generators and disaster types.
xBD: A Dataset for Assessing Building Damage from Satellite Imagery, November 2019
12 Pith papers cite this work. Polarity classification is still indexing.
12
Pith papers citing it
citation-role summary
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citation-polarity summary
representative citing papers
DORA is the first end-to-end agentic benchmark for LLM-based disaster response, covering perception, spatial analysis, evacuation planning, temporal reasoning, and report generation over heterogeneous geospatial data, with evaluations of 13 frontier models revealing tool-use and composition failures
The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting limitations in grounding and safety.
FROST performs training-free few-shot segmentation on remote-sensing imagery by nonparametric density-ratio classification on frozen DINOv3 features and reports 5.6 mIoU gains from one example across 17 benchmarks.
GeoDisaster provides a new benchmark for operational disaster geo-intelligence and proposes an RCEA-trained multi-agent framework with 18 geospatial tools that improves tool use and decision consistency over existing RS-VLMs.
Damage-TriageFormer extends a DINOv3 ViT-L backbone with a feature pyramid and gated head to classify mono-temporal post-event imagery into five damage typologies on a new NOAA-derived benchmark, reporting macro F1 of 0.62.
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.
A hybrid AI system combines super-resolution, YOLO-based detection, and vision-language models to semantically classify building damage severity in pre- and post-disaster satellite images.
Supervised domain adaptation is required for any functional building damage detection across disasters, reaching Macro-F1 0.5552 on unseen Ida-BD data when combined with unsharp masking.
Dual-domain models achieve the highest accuracy (0.4688) while frequency-only models overfit and all approaches struggle with the minor damage class due to imbalance.
citing papers explorer
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Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion
Introduces Forged Calamity benchmark and shows that fine-tuned and zero-shot synthetic image detectors lose substantial accuracy on unseen generators and disaster types.
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Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations
DORA is the first end-to-end agentic benchmark for LLM-based disaster response, covering perception, spatial analysis, evacuation planning, temporal reasoning, and report generation over heterogeneous geospatial data, with evaluations of 13 frontier models revealing tool-use and composition failures
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Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems
The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting limitations in grounding and safety.
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FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics
FROST performs training-free few-shot segmentation on remote-sensing imagery by nonparametric density-ratio classification on frozen DINOv3 features and reports 5.6 mIoU gains from one example across 17 benchmarks.
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GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence
GeoDisaster provides a new benchmark for operational disaster geo-intelligence and proposes an RCEA-trained multi-agent framework with 18 geospatial tools that improves tool use and decision consistency over existing RS-VLMs.
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Damage-TriageFormer: A Foundation-Model Framework for Typology-Based Building Damage Assessment from Mono-Temporal Imagery
Damage-TriageFormer extends a DINOv3 ViT-L backbone with a feature pyramid and gated head to classify mono-temporal post-event imagery into five damage typologies on a new NOAA-derived benchmark, reporting macro F1 of 0.62.
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UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
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Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.
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From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery
A hybrid AI system combines super-resolution, YOLO-based detection, and vision-language models to semantically classify building damage severity in pre- and post-disaster satellite images.
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Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation
Supervised domain adaptation is required for any functional building damage detection across disasters, reaching Macro-F1 0.5552 on unseen Ida-BD data when combined with unsharp masking.
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Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations
Dual-domain models achieve the highest accuracy (0.4688) while frequency-only models overfit and all approaches struggle with the minor damage class due to imbalance.
- RoofNet: A Global Multimodal Dataset for Roof Material Identification from Earth Observation