Region-Specific Safety Datasets

• Region-specific safety datasets are curated corpora that capture localized hazards using tailored annotation schemas for precise risk analysis.
• They integrate diverse data modalities and regional taxonomies that reflect local hazards, policies, and infrastructural nuances.
• These datasets enable the development of context-aware safety-critical systems and support rigorous evaluation of models in localized environments.

Region-specific safety datasets are curated corpora designed to capture, encode, and benchmark safety-related phenomena as they manifest within specific linguistic, geographic, infrastructural, or regulatory contexts. These resources are characterized by precise regional scope, context-dependent annotation schemas, and modalities reflecting local hazards, behaviors, and policy priorities. Unlike generalized safety datasets, region-specific datasets enable detailed analysis, measurement, and model alignment for localized risk scenarios—ranging from multilingual AI safety and transport accident detection to industrial inspection and VRU (vulnerable road user) interaction modeling. Such datasets are indispensable for the development of robust, contextually aware safety-critical systems and for the rigorous evaluation of regionally relevant model performance.

1. Geographic, Linguistic, and Infrastructural Scope

Region-specific safety datasets exhibit granularity along multiple axes:

• National/Municipal Coverage: US-Accidents provides traffic crash data at state, county, city, and zip-code levels for the contiguous United States, supporting fine-grained aggregation and hotspot detection (Moosavi et al., 2019). CSDataset extends this to OSHA-recorded construction safety incidents at city, county, and ZIP-code levels across the U.S. over a 10-year span, with unified encoding for hierarchical aggregation or filtering (Ou et al., 9 Aug 2025).
• Urban Transport Microdomains: The AllTheDocks project samples 116.15 km of real cyclist routes traversing London, purposefully incorporating diverse road typologies, infrastructure, and environmental conditions unique to London’s urban form (Chiang et al., 2024). R²S100K quantitatively surveys over 1,000 km of Pakistani roadways, capturing structured and unstructured road environments in Punjab and Khyber-Pakhtunkhwa (Butt et al., 2023).
• Highway and Long-Tail Crash Detection: TUMTraf-A focuses on a 600 m segment of the German A9 near Munich, with roadside cameras/LiDAR precisely aligned to local road coordinates (Zimmer et al., 20 Aug 2025). The A9 Test Stretch dataset similarly captures accident events using regionally deployed sensor infrastructure (Zimmer et al., 1 Feb 2025).
• Multilingual and Cultural Context: Soteria’s XThreatBench and Qorgau epitomize linguistic region-specificity. XThreatBench evaluates LLM safety across 13 languages, from English and Chinese to Tamil and Telugu, with adversarial prompts tailored for each, while Qorgau targets the bilingual Kazakh–Russian context in Kazakhstan, localizing harm typologies to regional political, cultural, and historical sensitivities (Banerjee et al., 16 Feb 2025, Goloburda et al., 19 Feb 2025).
• Industrial Sites: InspecSafe-V1 enumerates five major Chinese industrial scenario types (tunnels, power facilities, sintering workshops, petrochemical plants, and coal conveyor trestles), and defines their region-specific risk signatures in both object classes and environmental modalities (Liu et al., 29 Jan 2026).
• VRU-rich Urban Microenvironments: OnSiteVRU is geographically and infrastructurally specific to Shanghai, capturing both intersection-heavy and unstructured urban village traffic with high VRU density and exact signal-phase annotations (Yan et al., 30 Mar 2025).

This diversity enables datasets to reflect regionally distinctive risk factors, such as London’s historic street layouts, German autobahn speed regimes, or Kazakhstani political sensitivities, making generalization beyond these contexts non-trivial.

2. Annotation Schemas, Taxonomies, and Data Modalities

Annotation strategies are closely tied to region-specific risk phenomena and use-cases:

• Taxonomy Depth and Coverage:
  • XThreatBench defines a flat, ten-category structure derived from Meta’s content guidelines (e.g., sexual content, economic fraud, privacy violations), one label per item without sub-hierarchies (Banerjee et al., 16 Feb 2025).
  • Qorgau introduces a six-risk-area, 17-harm-type taxonomy adapted to Kazakh–Russian context, covering both universal (e.g., privacy, hate speech) and regionally unique categories (e.g., Jeltoqsan protests, legal/human-rights issues in Kazakhstan/Russia) (Goloburda et al., 19 Feb 2025).
  • InspecSafe-V1 catalogs 234 industrial object types with region-specific criticality, assigning safety labels with a four-level ordinal scale from "hazardous" to "safe" for each inspection keyframe (Liu et al., 29 Jan 2026).
  • R²S100K distinguishes 14 region-defined road-surface classes (asphalt, distress, water puddle, crag-stone) tailored to Pakistani road geography (Butt et al., 2023).
• Sensor/Modality Integration:
  • AllTheDocks leverages synchronized helmet-cam video, GPS, accelerometer, gyroscope, and road roughness metrics, with regionally prevalent hazard classes (potholes, encroaching vehicles, cobblestones) annotated per frame (Chiang et al., 2024).
  • US-Accidents integrates weather, time-of-day, and POI proximity with geolocated traffic incidents to enable context-rich regional analysis (Moosavi et al., 2019).
  • OnSiteVRU provides high-resolution (Δt=0.04 s) VRU trajectories aligned with phase-accurate traffic signal states and obstacle maps, supporting the calibration of scene- and conflict-based risk models (Yan et al., 30 Mar 2025).
• Annotation Protocols and Reliability:
  • XThreatBench deploys a three-stage prompt validation (human, GPT-4, Perspective API) without published inter-rater agreement metrics (Banerjee et al., 16 Feb 2025).
  • Qorgau uses graduate-level native annotators with 90%+ agreement against GPT-4o on binary harm labels; fine-grained agreement is lower (≈70%), and Cohen’s κ is not reported but standard if future computation is desired (Goloburda et al., 19 Feb 2025).
  • InspecSafe-V1 enforces ≥95% mask accuracy in two audit rounds across object categories; label consensus is maintained via multi-stage human checks (Liu et al., 29 Jan 2026).

A plausible implication is that region-specific datasets require iterative refinement of both taxonomy and annotation strategies, as imported schemes (from e.g. English, Chinese) are insufficient for capturing localized harm or hazard semantics.

3. Region-Tailored Metrics and Evaluation Protocols

Safety evaluation in these datasets hinges on contextually meaningful metrics:

• LLM and Content Safety:
  • XThreatBench measures Attack Success Rate (ASR), defined as the fraction of adversarial prompts that induce a harmful response. All prompts are adversarial, so ViolationRate = 1.0 by design (Banerjee et al., 16 Feb 2025).
  • Qorgau quantifies "Safety Score" (percentage of safe responses out of total), with breakdowns by risk area and question type (direct, indirect, false positive probes), and evaluates model robustness to code-switching (Goloburda et al., 19 Feb 2025).
• Road and Industrial Safety:
  • AllTheDocks uses the International Roughness Index (IRI) to quantify physical discomfort and potential danger; together with a 4-point Likert safety-perception rating by experienced local cyclists (Chiang et al., 2024).
  • InspecSafe-V1 employs discrete safety levels and mean Intersection-over-Union (mIoU) for object segmentation, with fusion models reporting accuracy across vision, infrared, and sensor streams (Liu et al., 29 Jan 2026).
  • Construction-site CSDataset allows computation of incident rates per 1,000 worker-hours, violation severity scores, and models region-specific risk via hierarchical or mixed-effects methods. For policy analysis, average treatment effect ($\Delta P$) quantifies the impact of interventions (e.g., complaint-driven inspections lower incident rates by 17.3 percentage points in the U.S., but with heterogeneity by region) (Ou et al., 9 Aug 2025).
• Autonomous Driving/Accident Detection:
  • A9/TUMTraf-A datasets combine classic precision/recall and Average Precision (AP) for detection, but metrics are explicitly tailored by region: thresholds for time-to-collision and closing speed are matched to local speed limits and typical collision dynamics (Zimmer et al., 1 Feb 2025, Zimmer et al., 20 Aug 2025).

Region-specific adaptation is evidenced by the choice and calibration of metrics—for instance, IRI interpretation and accident risk scoring must be adapted if transferred to a new urban context or industrial environment.

4. Policy Alignment, Cultural Localization, and Sensitivity

Region-specific datasets reflect or directly encode local policy and cultural context.

• Policy Source and Localization:
  • XThreatBench categories are formally "derived from Meta’s content guidelines,” but the paper does not document adaptation to country-specific law or protocol for regional alignment, limiting explicit regulatory correspondence (Banerjee et al., 16 Feb 2025).
  • Qorgau’s process of replacing references to the Nanjing Massacre or Chinese dynasties with pivotal events in Kazakh or Russian history, and using local names/sites, operationalizes cultural localization—surfacing vulnerabilities missed by global taxonomies (Goloburda et al., 19 Feb 2025).
  • OnSiteVRU annotates traffic signal phase and lanelet maps with high regional accuracy, enabling spatiotemporal analysis of VRU risk that is closely mapped to current Shanghai intersection control logic and non-motor vehicle policy (Yan et al., 30 Mar 2025).
  • Construction safety CSDataset includes inspection types ("complaint" vs. programmed) reflecting U.S. OSHA policy; impact analysis is stratified by major U.S. regions, demonstrating policy-driven nonuniformity in risk reduction (Ou et al., 9 Aug 2025).

A plausible implication is that absent rigorous policy-compatibility mapping and native-speaker input, region-specific datasets risk both under-representation of salient local hazards and misalignment with de facto safety standards.

5. Applications, Cross-Region Limitations, and Extensibility

• Core Applications:
  • Safety-critical LLM alignment and jailbreak evaluation in diverse linguistic/legislative ecosystems (Soteria/XThreatBench, Qorgau) (Banerjee et al., 16 Feb 2025, Goloburda et al., 19 Feb 2025).
  • Region-adapted object detection, anomaly recognition, and safety assessment in transport, industry, and urban mobility (AllTheDocks, InspecSafe-V1, OnSiteVRU) (Chiang et al., 2024, Liu et al., 29 Jan 2026, Yan et al., 30 Mar 2025).
  • Policy impact modeling, hotspot localization, and human-factor analysis in U.S. accident data (Moosavi et al., 2019, Ou et al., 9 Aug 2025).
  • Multi-modal, high-precision VRU trajectory replay and interaction event simulation in Shanghai-centric virtual testing (Yan et al., 30 Mar 2025).
• Limitations:
  • Transfer learning is constrained: London cycling data exhibits urban idiosyncrasies (historical cobbles, variable daylight) that reduce interoperability with U.S. cities (Chiang et al., 2024).
  • A9/TUMTraf-A is highway-specific; lacks urban intersection coverage, so cannot inform city-scale crash risk without further sampling (Zimmer et al., 1 Feb 2025, Zimmer et al., 20 Aug 2025).
  • Industrial datasets such as InspecSafe-V1 face class imbalance in rare hazard types and modality drift in extreme weather or dust; performance in unseen environments is not guaranteed (Liu et al., 29 Jan 2026).
• Recommended Procedures for New Regions:
  • XThreatBench and Qorgau suggest: define a region-relevant risk taxonomy, engage local expert annotators, ensure prompts/questions are not simple translation but cultural adaptation, and employ contextual verification (Banerjee et al., 16 Feb 2025, Goloburda et al., 19 Feb 2025).
  • For road or infrastructure safety, recalibrate sensor setup and object classes, extend OpenLABEL or equivalent schemas to cover regional layouts, and benchmark detection/segmentation algorithms using locally meaningful metrics (e.g., VRU density, intersection-specific conflict scoring) (Zimmer et al., 20 Aug 2025, Yan et al., 30 Mar 2025).

When adapting region-specific safety datasets, best practices emphasize the necessity of recalibration and validation in the target context, to avoid misalignment and unmodeled risk exposure. This ensures the resulting models or infrastructure are robust, interpretable, and compliant with both local hazards and regulatory frameworks.

6. Representative Datasets: Schematic Overview

Dataset	Region/Domain	Coverage/Modality
XThreatBench	13 languages/globally	3,000 adversarial LLM prompts, policy-derived categories
Qorgau	Kazakhstan (bilingual)	8,169 LLM prompts, risk-typology with regional content
AllTheDocks	London cycling	Multi-sensor telemetry, IRI, safety ratings, object hazards
CSDataset	US construction	Incidents, inspections, violations, structured/unstructured
US-Accidents	US nationwide	2.2M accident rec., with weather, geo, POI
R²S100K	Punjab/KP (Pakistan)	100k images, 14 road classes, semi-supervised segmentation
TUMTraf-A, A9	German autobahn	Camera+LiDAR, 3D boxes, high-speed crash detection
InspecSafe-V1	5 Chinese industries	5,013 inspection points, 234 segment classes, 7 modalities
OnSiteVRU	Shanghai urban	17,429 VRU-rich trajectories, signal/obstacle mapping

This tabular view highlights the spread of coverage, domain, and annotation styles that define state-of-the-art region-specific safety datasets.

7. Future Directions and Open Challenges

Research indicates a critical need for extending region-specific safety datasets along several vectors:

• Multimodality and richer integration—especially in VRU, industrial, and road-safety contexts—enabling cross-sensor anomaly reasoning (Liu et al., 29 Jan 2026, Yan et al., 30 Mar 2025).
• Automated, context-aware prompt/adversarial sampling for underrepresented languages and cultures in LLM safety, moving beyond translation toward regional cultural alignment (Banerjee et al., 16 Feb 2025, Goloburda et al., 19 Feb 2025).
• Systematic reporting and maximization of inter-annotator agreement (e.g., Cohen’s κ) to ensure consistency of safety judgments across multilingual/region-adapted corpora (Goloburda et al., 19 Feb 2025).
• Scalable, replicable adaptation procedures, including domain transfer with calibration to local physical conditions, policy, and hazard distributions (Butt et al., 2023, Zimmer et al., 20 Aug 2025).
• Expansion to new geographies and regulatory regimes, sharing data and protocols to foster reproducibility and global model robustness.

A plausible implication is that robust and ethical region-specific safety assurance in complex environments—across AI, transport, or industrial domains—will continue to depend on the creation and open sharing of rigorously annotated, context-sensitive datasets, feeding into empirically validated, policy-aligned safety models.