arXiv subjects

Yuki Miura

Publications and source records attributed to Yuki Miura.

4 recordsLinked to original sources

arXiv2026

RoofNet: A Global Multimodal Dataset for Roof Material Identification from Earth Observation

Building-level exposure data are critical to natural hazard risk modeling, yet most global inventories describe where buildings are located rather than what they are made of. Roof material is a critical but poorly documented attribute for assessing vulnerability to wildfires, wind hazards, urban heat, floods, and earthquakes. To address this gap, we introduce RoofNet, a global dataset that maps 49,662 georeferenced building instances from 101 countries to 14 key roofing material classes using Earth observation (EO) imagery (redistributed where permitted) and associated geospatial metadata. RoofNet contributes (1) climatographically and architecturally diverse coverage of roof material labels, (2) a scalable annotation pipeline combining SME-guided manual labeling with vision-language model (VLM)-assisted classification, rule-based validation, and human-in-the-loop verification, and (3) a resource for evaluating subtle, geographically variable material-level identification in EO imagery and its implications for material-aware hazard risk modeling. Evaluation on a manually labeled hold-out set shows that zero-shot Remote Contrastive Language-Image Pre-Training (RemoteCLIP) struggles with roof material classification, while fine-tuning with RoofNet improves top-1 accuracy from 4.9% to 47.7%. We use RoofNet in an illustrative hazard case study to demonstrate how material-aware exposure data can change vulnerability estimates relative to material-naive inventories. RoofNet provides a missing material layer for global building attribute mapping and scalable hazard risk assessment.

cs.CE↗

arXiv2026

Bridging the Urban Divide: Adaptive Cross-City Learning for Disaster Sentiment Understanding

Social media platforms provide a real-time lens into public sentiment during natural disasters; however, models built solely on textual data often reinforce urban-centric biases and overlook underrepresented communities. This paper introduces an adaptive cross-city learning framework that enhances disaster sentiment understanding by integrating mobility-informed behavioral signals and city similarity-based data augmentation. Focusing on the January 2025 Southern California wildfires, our model achieves state-of-the-art performance and reveals geographically diverse sentiment patterns, particularly in areas experiencing overlapping fire exposure or delayed emergency responses. We further identify positive correlations between emotional expressions and real-world mobility shifts, underscoring the value of combining behavioral and textual features. Through extensive experiments, we demonstrate that multimodal fusion and city-aware training significantly improve both accuracy and fairness. Collectively, these findings highlight the importance of context-sensitive sentiment modeling and provide actionable insights toward developing more inclusive and equitable disaster response systems.

cs.SI↗

arXiv2025

A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation

Rapid, fine-grained disaster damage assessment is essential for effective emergency response, yet remains challenging due to limited ground sensors and delays in official reporting. Social media provides a rich, real-time source of human-centric observations, but its multimodal and unstructured nature presents challenges for traditional analytical methods. In this study, we propose a structured Multimodal, Multilingual, and Multidimensional (3M) pipeline that leverages multimodal large language models (MLLMs) to assess disaster impacts. We evaluate three foundation models across two major earthquake events using both macro- and micro-level analyses. Results show that MLLMs effectively integrate image-text signals and demonstrate a strong correlation with ground-truth seismic data. However, performance varies with language, epicentral distance, and input modality. This work highlights the potential of MLLMs for disaster assessment and provides a foundation for future research in applying MLLMs to real-time crisis contexts. The code and data are released at: https://github.com/missa7481/EMNLP25_earthquake

cs.CL↗

arXiv2021

PreDisM: Pre-Disaster Modelling With CNN Ensembles for At-Risk Communities

The machine learning community has recently had increased interest in the climate and disaster damage domain due to a marked increased occurrences of natural hazards (e.g., hurricanes, forest fires, floods, earthquakes). However, not enough attention has been devoted to mitigating probable destruction from impending natural hazards. We explore this crucial space by predicting building-level damages on a before-the-fact basis that would allow state actors and non-governmental organizations to be best equipped with resource distribution to minimize or preempt losses. We introduce PreDisM that employs an ensemble of ResNets and fully connected layers over decision trees to capture image-level and meta-level information to accurately estimate weakness of man-made structures to disaster-occurrences. Our model performs well and is responsive to tuning across types of disasters and highlights the space of preemptive hazard damage modelling.

cs.CV↗