Papers Disaster Response
“Disaster Response” 태그가 달린 논문 217편 · 필터 해제
Differential Attention for Multimodal Crisis Event Analysis
Social networks can be a valuable source of information during crisis events. In particular, users can post a stream of multimodal data that can be critical for real-time humanitarian response. However, effectively extra…
Disaster ResponseHumanitarianAutomated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning
Operations in disaster response, search \& rescue, and military missions that involve multiple agents demand automated processes to support the planning of the courses of action (COA). Moreover, traverse-affecting change…
Disaster ResponseDiversityGraph LearningGraph Neural NetworkCloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions
Cloud contamination significantly impairs the usability of optical satellite imagery, affecting critical applications such as environmental monitoring, disaster response, and land-use analysis. This research presents a C…
Disaster ResponseImage ReconstructionSSIMUAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs
Agentic UAVs represent a new frontier in autonomous aerial intelligence, integrating perception, decision-making, memory, and collaborative planning to operate adaptively in complex, real-world environments. Driven by re…
Disaster ResponseAutonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response
Natural disasters have caused significant losses to human society, and the timely and efficient acquisition of post-disaster environmental information is crucial for the effective implementation of rescue operations. Due…
Dimensionality ReductionDisaster ResponseSchedulingGeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models
Classifying geospatial imagery remains a major bottleneck for applications such as disaster response and land-use monitoring-particularly in regions where annotated data is scarce or unavailable. Existing tools (e.g., RS…
ClassificationDisaster Responseimage-classificationImage Classification+6Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms
Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of intere…
Decision MakingDecision Making Under UncertaintyDisaster ResponseVLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation
Path planning is a fundamental capability of autonomous Unmanned Aerial Vehicles (UAVs), enabling them to efficiently navigate toward a target region or explore complex environments while avoiding obstacles. Traditional …
Disaster ResponseLanguage ModelingLanguage ModellingNavigateVME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
Detecting vehicles in satellite images is crucial for traffic management, urban planning, and disaster response. However, current models struggle with real-world diversity, particularly across different regions. This cha…
Disaster ResponseDiversityObject DetectionObject Detection In Aerial Images+2Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems
Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentral…
Disaster ResponsePositionAnalysis of Joint Radar and Communication in Disaster Scenarios
With the increasing frequency and intensity of natural disasters, there is a necessity for advanced technologies that can provide reliable situational awareness and communication. Conventional systems are often inadequat…
Disaster ResponseManagementREOBench: Benchmarking Robustness of Earth Observation Foundation Models
Earth observation foundation models have shown strong generalization across multiple Earth observation tasks, but their robustness under real-world perturbations remains underexplored. To bridge this gap, we introduce RE…
BenchmarkingContrastive LearningDisaster ResponseEarth ObservationThe Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models
AI systems in high-consequence domains such as defense, intelligence, and disaster response must detect rare, high-impact events while operating under tight resource constraints. Traditional annotation strategies that pr…
Disaster ResponseDiversityFlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models
Unmanned Aerial Vehicle (UAV) Vision-and-Language Navigation (VLN) is vital for applications such as disaster response, logistics delivery, and urban inspection. However, existing methods often struggle with insufficient…
Disaster ResponseVision and Language NavigationTales of the 2025 Los Angeles Fire: Hotwash for Public Health Concerns in Reddit via LLM-Enhanced Topic Modeling
Wildfires have become increasingly frequent, irregular, and severe in recent years. Understanding how affected populations perceive and respond during wildfire crises is critical for timely and empathetic disaster respon…
Disaster ResponseSignals from the Floods: AI-Driven Disaster Analysis through Multi-Source Data Fusion
Massive and diverse web data are increasingly vital for government disaster response, as demonstrated by the 2022 floods in New South Wales (NSW), Australia. This study examines how X (formerly Twitter) and public inquir…
Disaster ResponseBuilding-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mapping
Accurate building damage assessment using bi-temporal multi-modal remote sensing images is essential for effective disaster response and recovery planning. This study proposes a novel Building-Guided Pseudo-Label Learnin…
Building Damage AssessmentChange DetectionDisaster ResponsePseudo LabelGraph Neural Network Aided Deep Reinforcement Learning for Resource Allocation in Dynamic Terahertz UAV Networks
Terahertz (THz) unmanned aerial vehicle (UAV) networks with flexible topologies and ultra-high data rates are expected to empower numerous applications in security surveillance, disaster response, and environmental monit…
Deep Reinforcement LearningDisaster ResponseGraph Neural NetworkMagnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation
In crisis management and remote sensing, image segmentation plays a crucial role, enabling tasks like disaster response and emergency planning by analyzing visual data. Neural networks are able to analyze satellite acqui…
Burned Area DelineationDisaster ResponseImage SegmentationSemantic SegmentationPost-Hurricane Debris Segmentation Using Fine-Tuned Foundational Vision Models
Timely and accurate detection of hurricane debris is critical for effective disaster response and community resilience. While post-disaster aerial imagery is readily available, robust debris segmentation solutions applic…
Disaster ResponsePrompt Engineering