Table 1
Role of geoinformatics and remote sensing in the management of poly crisis.
| ITEM | AU AGENDA 2063 | ROLE OF GEOINFORMATICS AND REMOTE SENSING | SDG |
|---|---|---|---|
| Risk Assessment & Hazard Mapping | -‘Environmentally sustainable and climate resilient economies and communities’. -‘Modern, affordable and liveable habitats’. | -Maps and models spatial threats (e.g., floods, wildfires), -Help identify, visualize, and analyze vulnerable areas and populations. | SDG 11 – ‘Sustainable Cities and Communities’ |
| Early Warning & Real-Time Monitoring | -‘World-class infrastructure crisis – crosses Africa’. -‘Climate resilience and natural disasters preparedness’. | -Integrates data from satellites, drones, sensors, and social media to provide timely alerts and situational awareness for fast-moving crises. This supports rapid decision-making, resource allocation, and coordination among agencies during complex emergencies. | SDG 13 – ‘Climate Action’ |
| Resource Allocation & Coordination | ‘Environmentally sustainable and climate resilient economies and communities’. | -Supports efficient deployment of emergency resources, evacuation planning, and inter-agency coordination using real-time, interactive maps. | SDG 12 – ‘Responsible Consumption and Production’. |
| Impact Assessment & Recovery | ‘World-class infrastructure crisis – crosses Africa’. | -Assesses damage, monitors recovery, and supports long-term planning by analyzing post-crisis spatial data. | SDG 3 – ‘Good Health and Well-being’ |
| Scenario Modeling & Decision Support | -‘Environmentally sustainable and climate resilient economies and communities’. -‘Climate resilience and natural disasters preparedness’. | -Enables scenario analysis and predictive modeling, often integrating with AI, to inform policy and operational decisions during complex, overlapping crises. | SDG 15 – ‘Life on Land’. SDG 11 – ‘Sustainable Cities and Communities’ |
Table 2
Key components of an effective structure for resilience.
| COMPONENT | DESCRIPTION |
|---|---|
| Structure | Clear committees, reporting lines, mandates, and terms of reference that adapt existing governance frameworks to oversee resilience. |
| Roles & Responsibilities | Defined roles from board to operational teams, with clear accountability for resilience activities and decision-making. |
| People & Culture | A culture of resilience, driven by leadership, with training and awareness at all levels to embed resilient behaviors and values. |
| Enabling Processes | Policies, procedures, and information systems that support decision-making, monitoring, and continuous improvement in resilience. |
| Subject Matter Expertise | Inclusion of relevant expertise (e.g., risk, technology, operations, compliance) to inform and guide resilience strategies. |
| Stakeholder Engagement | Mechanisms for engaging internal and external stakeholders to foster trust, transparency, and coordinated response. |
| Continuous Evaluation | Regular assessment, reporting, and adaptation of governance and resilience plans to ensure effectiveness and relevance. |
Figure 1
Identification criteria of studies.
Figure 2
Poly crisis technology convergence model.
Table 3
Technology convergence assessment framework for poly crisis.
| PROBLEM IDENTIFICATION AND SCOPING | |
| Poly crisis Context Definition: -Map cross-domain interdependencies between climates, social, economic, and governance systems. -Identify cascading risk pathways and feedback loops. -Establish system boundaries and scope of intervention. | Stakeholder Analysis: -Identify key actors across sectors and scales. -Define roles and responsibilities in poly crisis governance. -Map information needs and decision-making processes. |
| SOLUTION ARCHITECTURE AND OBJECTIVES | |
| Performance Objectives and KPIs: -Climate Action Metrics: Avoided emissions, carbon sequestration rates, and renewable energy adoption. -Resilience Indicators: Exposure reduction, vulnerability indices, adaptive capacity measures. -Operational Metrics: Time-to-alert, recovery duration, service uptime, system availability. -Social Impact Metrics: Engagement rates, equity Co-benefits, community participation levels. -Economic Metrics: Climate risk-adjusted ROI, cost-benefit ratios, damage avoidance. | Decision Thresholds and Success Criteria: -Define trigger points for interventions. -Establish acceptable risk levels and tolerance bands. -Set performance benchmarks for each system component. |
| INTEGRATED CAPABILITY STACK DEVELOPMENT | |
| AI Layer Architecture: -Data Fusion Module: Integration of remote sensing, geospatial, and socioeconomic datasets. -Predictive Analytics: Machine learning models for cascading impact forecasting. -Pattern Recognition: Anomaly detection and early warning systems. | Geoinformatics Layer: -Spatial Risk Surfaces: Multi-hazard vulnerability mapping. -Dependency Graphs: Infrastructure and system interdependency modeling. -Decision Layers: Optimized resource allocation and intervention planning. |
| VR Module Development: -Immersive Training Scenarios: Policy rehearsal environments for decision-makers. -Crisis Simulation: Multi-stakeholder coordination exercises. -Public Engagement Tools: Community awareness and preparedness programs. | AR Module Implementation: -Field Overlays: Real-time hazard visualization and navigation. -Infrastructure Status: Asset condition and service disruption indicators. -Operational Guidance: Context-aware decision support for responders. |
| Governance and Ethics Framework: -Data Stewardship: Privacy protection and consent management. -Security Protocols: Cybersecurity measures and access controls. -Sustainability Monitoring: Energy consumption and carbon footprint tracking. -Ethical Guidelines: Bias mitigation and fairness assurance | |
| SYSTEM INTEGRATION AND DOCUMENTATION | |
| Technical Integration: -API development and standardization. -Shared schema design and data interoperability. -Micro-services architecture implementation. -Real-time data pipeline construction. | Open Science Documentation: -Code repositories with version control. -Data dictionaries and metadata standards. -Containerized deployment packages. -Technical documentation and user guides. |
| PILOT DEMONSTRATION | |
| VR-Based Applications: -Policy Rehearsal: Scenario-based decision-making exercises. -Public Engagement: Community consultation and feedback sessions. -Training Programs: Capacity building for stakeholders. | AR Field Deployment: -Operational Decision Support: Real-time guidance during events. -Situational Awareness: Enhanced field intelligence. -Resource Coordination: Multi-agency collaboration tools. |
| Dashboard Implementation: -Cross-Agency Coordination: Unified operational picture. -Performance Monitoring: Real-time KPI tracking. -Decision Audit Trail: Documentation of actions and outcomes. | |
| EVALUATION FRAMEWORK | |
| Technical Performance Metrics: -Model Accuracy: AUC, F1 scores, precision-recall curves. -Calibration Quality: Reliability diagrams, calibration plots. -Forecast Lead Time: Early warning system effectiveness. | Decision Quality Assessment: -Process Efficiency: Time-to-decision, decision cycle duration. -Resource Allocation: Accuracy and optimization of deployments. -Error Rates: False alarm and miss detection frequencies. |
| Resilience Outcome Measurement: -Baseline Shifts: Changes in vulnerability indicators. -Recovery Metrics: Time to restoration, bounce-back capacity. -Exposure Reduction: Population and asset protection levels. | Human Factors Analysis: -Usability Assessment: System Usability Scale (SUS) scores. -Training Effectiveness: Pre/post competency evaluations. -Adoption Metrics: User engagement and retention rates. |
| Equity and Ethics Evaluation: -Benefit Distribution: Spatial and demographic equity analysis. -Privacy Compliance: GDPR and data protection adherence. -Accessibility: Universal design compliance. | Sustainability Assessment: -Energy Footprint: Computational and device energy consumption. -Climate Cost-Benefit: Net emissions impact analysis. -Resource Efficiency: Optimization of system operations. |
| ADAPTIVE MANAGEMENT AND SCALING | |
| Adaptive Cycle Monitoring: -Phase Indicators: Metrics for exploitation, conservation, release, and reorganization stages. -Transition Triggers: Thresholds for phase shift detection. -System Resilience: Capacity for transformation and renewal. | Iterative Improvement: -Model Updates: Continuous learning and retraining protocols. -Data Refresh: Regular dataset updates and quality assurance. -Content Evolution: Training scenario and tool refinement. |
| Scaling Strategy: -Pilot to Portfolio: Phased expansion from demonstration to full deployment. -Geographic Scaling: Replication across regions and contexts. -Sectoral Integration: Cross-domain application and adaptation. | |
Table 4
Audit calibration log.
| ATTRIBUTE | DESCRIPTION |
|---|---|
| event_id | unique alert/event identifier |
| phase | ‘platform’ (calibration generated for the platform run) |
| predicted_probability | model alert probability (0–1) |
| observed_outcome | 1 if event occurred, 0 otherwise |
Table 5
Audit operations log.
| ATTRIBUTE | DESCRIPTION |
|---|---|
| event_id | unique operational event id |
| phase | ‘baseline’ or ‘platform’ |
| neighborhood | Location bucket (N1…N6) |
| event_time | ISO timestamp of the event onset |
| alert_time | ISO timestamp when alert was issued |
| decision_start / decision_end | ISO timestamps for the decision cycle |
| lead_time_min | event_time – alert_time (minutes) |
| decision_cycle_min | decision_end – decision_start (minutes) |
| false alarm | 1 alert that did not correspond to an event |
| miss | 1 when an event was missed |
| allocation correct | 1 when resource allocation matched plan/need |
Table 6
Audit system usability scale.
| ATTRIBUTE | DESCRIPTION |
|---|---|
| participant_id | anonymized id |
| sus_score | System Usability Scale score (0–100) |
Table 7
Audit equity coverage.
| ATTRIBUTE | DESCRIPTION |
|---|---|
| neighborhood | N1….N6 |
| baseline coverage | vulnerability-weighted coverage under baseline |
| platform coverage | vulnerability-weighted coverage under the platform |
Figure 3
Reliability diagram (observed vs. predicted event probabilities, binned by deciles); the dashed line indicates perfect calibration. This figure evaluates how well the predicted probabilities match the actual observed outcomes (i.e., calibration of the predictive model). Data source, Table 5 with columns: event_id, phase, predicted_probability, observed_outcome. The orange dashed line (‘Perfect Calibration’) represents the ideal relationship—e.g., when predicted probabilities exactly equal observed event frequencies. The blue curve (‘Observed vs Predicted’) is derived by binning predicted probabilities (e.g., 0–0.1, 0.1–0.2, etc.) and computing the average observed event rate per bin. The closeness of the blue line to the diagonal indicates good calibration. Here, the line roughly follows the diagonal, suggesting the Platform’s predictive model is well-calibrated, with mild underconfidence at low probabilities.
Figure 4
Early-warning lead time (minutes) by phase (baseline vs. platform). This figure measures how early the system issues alerts before the actual event occurs. Data source: Table 5 with columns: event_id, phase, lead_time_min. Baseline median lead time: ~40 minutes. Platform median lead time: ~90 minutes. The platform provides significantly early alerts, nearly doubling the lead time. Longer lead times improve preparedness but must be balanced against false alarms.
Figure 5
Decision cycle duration (minutes) by phase; duration measured from decision start to decision confirmation. This figure assesses how long it takes teams to make a decision after receiving an alert. Data source: decision_cycle_min from the same event Table 5. Baseline median decision cycle: ~60 minutes. Platform median: ~40 minutes. Decision cycles are shorter under the Platform, suggesting the decision-support features help operators act faster.
Figure 6
Resource allocation accuracy under operational constraints, baseline vs. platform (proportion of correct deployments). This figure compares how accurately resources (e.g., teams, equipment) were allocated to meet the need. Data source: allocation_correct (binary variable: 1 = correct allocation, 0 = incorrect), from Table 5. Baseline accuracy ≈ 0.72, platform accuracy ≈ 0.87. The platform improves resource allocation accuracy by about 15 percentage points, likely through better situational awareness or guidance.
Figure 7
Error rates in escalation workflows—false alarms and misses—comparing baseline and platform phases. The figure compares error rates for false alarms (alerts with no true event) and misses (events not detected). Data source: false alarms and missfields from Table 5. Baseline: False alarms ≈ 0.16, Misses ≈ 0.14; Platform: False alarms ≈ 0.09, Misses ≈ 0.08. The platform reduces both error types, improving both precision and recall of alerting.
Figure 8
Vulnerability-weighted coverage by neighborhood, comparing baseline and platform; higher values indicate more equitable protection. This figure evaluates fairness and consistency in how alerts and resources reach different communities. Data source: Table 7 of neighborhood, baseline coverage, and platform coverage. For all neighborhoods (N1–N6), platform coverage exceeds baseline, increasing from ~0.4–0.5 to ~0.6–0.65. Indicates improved equity in coverage, meaning more vulnerable or less-reached areas now receive comparable attention. These generated data serve to validate the framework’s objectives: a) Improve situational awareness: Better calibration and longer lead times (Figures 3, 4), b) Support effective decision-making: Shorter decision cycles, higher allocation accuracy (Figures 5, 6), c) Enable better risk assessment: Reduced misses and false alarms (Figure 7), d) Facilitate collaboration: Fewer workflow errors, improved communication efficiency, And e) Enhance public understanding and equity: Higher vulnerability-weighted coverage across all regions (Figure 8).
Figure 9
Distribution of documents over time.
Figure 10
Network visualization a) Network visualization of the terms related to climate research. b) Network visualization of the terms related to poly crisis.
| Contribution Type | Authors |
| Topic | Rania Elsayed Ibrahim |
| Conceptualization | Rania Elsayed Ibrahim, Abdelaziz Elfadaly, Tshiamo Motshegwa, Alaa A. Elbiomy, Mai Ramadan Ibraheem |
| Methodology | Rania Elsayed Ibrahim, Abdelaziz Elfadaly, Tshiamo Motshegwa, Alaa A. Elbiomy, Mai Ramadan Ibraheem |
| Software | Rania Elsayed Ibrahim, Alaa A. Elbiomy, Mai Ramadan Ibraheem |
| Validation | Rania Elsayed Ibrahim |
| Formal Analysis | Rania Elsayed Ibrahim; Abdelaziz Elfadaly, Alaa A. Elbiomy, Mai Ramadan Ibraheem |
| Data curation | Rania Elsayed Ibrahim, Mai Ramadan Ibraheem |
| Investigation | Rania Elsayed Ibrahim, Mai Ramadan Ibraheem, Alaa A. Elbiomy, |
| Resources | Rania Elsayed Ibrahim, Abdelaziz Elfadaly, Tshiamo Motshegwa, Alaa A. Elbiomy, Mai Ramadan Ibraheem |
| Writing—Original Draft Preparation | Rania Elsayed Ibrahim |
| Writing—Review and Editing | Rania Elsayed Ibrahim, Abdelaziz Elfadaly, Mai Ramadan Ibraheem, Alaa A. Elbiomy, Tshiamo Motshegwa |
| Project Administration | Rania Elsayed Ibrahim, Tshiamo Motshegwa |