Proactive ML-Enabled Predictive UAVs Communication Network Design for Galamsey Surveillance

II. Literature Review

• A. UAV Vision for Environmental Monitoring

Unmanned aerial vehicles (UAVs) have become an important tool in environmental monitoring because of their ability to operate flexibly in difficult terrains and to integrate modern computer vision methods. Deep learning–based object detection frameworks such as Mask R-CNN [18] and the YOLO family of models [27,36] established performance standards in real-time image recognition and have since inspired adaptations for safety inspection, ecological examples [16], surveys, and mining detection. To demonstrate the effectiveness of Sentinel-2 imagery with convolutional networks in detecting small-scale mining in Ghana, while [15] applied Sentinel-1 time-series analysis for land-use change detection. Similarly, [34] illustrates how UAV-based land-use monitoring in the Ankobra basin can reveal mining-related disturbances. Collectively, these studies confirm that UAV platforms coupled with vision algorithms can reliably detect artisanal and small-scale gold mining (ASGM) activities, even in heterogeneous landscapes.

• B. Ghana’s Galamsey Context

Despite these successes, monitoring challenges remain severe in Ghana’s galamsey corridors. Unlike industrial-scale open-pit operations, which are visible in satellite or aerial images [33], artisanal mining often occurs in concealed sites beneath dense vegetation and across rugged terrain. [7] emphasize that these conditions reduce detection accuracy, allowing small pits to escape both satellite- and UAV-based monitoring. Compounding the problem, communication infrastructure in rural ASGM zones is unreliable, with LTE coverage often patchy and mesh networks susceptible to interference. As a result, models that perform well in laboratory settings or in open mining sites frequently degrade in operational field conditions. This gap highlights the difficulty of sustaining actionable detection and response capabilities when both perception and communication are compromised.

Leveraging UAVs for patrol inspection, they play a critical role in monitoring slope stability and detecting early signs of potential hazards in open-pit mines [41]. Similarly, integrating safety technologies such as advanced sensors within tailings storage facilities represents a pivotal step toward smart mine safety systems [6].

• C. Beyond Detection: The Role of Forecasting

Detection provides situational awareness but does not guarantee timely intervention. Forecasting models extend UAV applications by predicting where and when hazards are likely to occur, enabling scarce resources such as UAV patrol hours to be allocated more efficiently. Spatiotemporal learning methods such as convolutional LSTM networks [40] and graph-based deep learning frameworks [46] have been validated in domains such as rainfall estimation, transportation, and ecological risk modeling. Their adaptation to illegal mining hotspots has shown early promise [4].

Complementary approaches leverage geographic information systems (GIS) combined with multicriteria decision analysis (MCDA) to incorporate terrain, hydrology, and settlement proximity into risk assessments [29]. Integrating these models allows for predictive risk maps that guide UAV deployments toward areas most likely to be affected, shifting the paradigm from reactive detection toward anticipatory hazard management proactively.

• D. Forecasting Reliability and Calibration

A recurring challenge with deep learning–based forecasting is overconfidence: models often assign high probabilities to uncertain predictions, which reduces trustworthiness in safety-critical operations [17]. For UAV-based surveillance, this can result in costly false alarms or misallocated responses. Approaches such as Bayesian deep learning [22] and post-hoc calibration strategies like temperature scaling [8] help align predicted probabilities with actual empirical likelihoods. In the context of galamsey monitoring, the use of calibrated severity indices ensures that only alerts meeting well-validated thresholds trigger dispatch actions, thereby balancing responsiveness with operational efficiency.

• E. Communications Reliability in UAV Networks

Rural ASGM corridors lack dependable infrastructure, so UAV surveillance must rely on resilient, adaptive communications [9]. Early approaches extended connectivity in intermittently connected regions using delay-tolerant networking (DTN) and long-distance Wi-Fi [25,54]. Within UAV contexts, cellular improvements and opportunistic mesh broadened range and continuity [1,12,26], while hybrid BVLOS operations and software-defined satellite overlays added redundancy for outages. Complementing these link-layer advances, multi-path, swarm-oriented planning improves route diversity and network robustness [28], and deep reinforcement learning helps collaborative UAVs maintain reliable contact under dynamic conditions [45].

Empirical studies of 5G-enabled UAVs show meaningful latency reductions, yet performance remains uneven in obstructed terrain [2]. FANET surveys repeatedly stress the need for fast failover and SLA-aware design; however, few evaluations consider African rural environments dominated by canopy, interference, and irregular power (e.g., generator) supply. This gap motivates our Galamsey-911 architecture, which fuses multi-path LTE→Mesh→SATCOM with SLA timers and swarm-aware planning to sustain sub-30-second alert delivery in canopy-dense corridors.

• F. Integration Gap: From Silos to Systems

Although the above strands of research perception, forecasting, and communications each show strong progress individually, most UAV deployments remain siloed. Vision systems are often disconnected from predictive models, and communications protocols are evaluated independently of perception pipelines. The literature lacks an integrated, end-to-end system that links calibrated detection with predictive foresight and communication reliability. This fragmentation results in UAV deployments that can detect incidents but cannot guarantee timely or trustworthy coordination of responses.

• G. Towards Cooperative UAV Responders

Addressing this integration gap motivates the present study. Building on prior advances in vision, forecasting, and communications, this work proposes the “Galamsey-911” architecture, which unifies calibrated perception models, predictive hotspot mapping, and multi-layer LTE–Mesh–SATCOM communications into a single operational framework. Unlike earlier approaches, which typically remain reactive, this system is designed to transition UAVs from passive monitoring to proactive, cooperative responders suited for the complex conditions of Ghana’s artisanal mining regions [8].

Although the above strands of research, perception, forecasting, and communications each show strong progress individually, most UAV deployments remain siloed. Studies on collaborative path planning [28] and reinforcement learning–based coordination [45], UAV surveillance [14] demonstrate important steps toward cooperation but do not yet integrate forecasting and SLA-aware resilience. Addressing this integration gap motivates the present study.

III. Materials, Methods and Methodology

• A. Data and Preprocessing

This section outlines the design and validation of the Proactive ML-enabled Predictive UAV Network, structured around three core contributions: C1 (Calibrated Severity Index), C2 (SLA-aware Communications), and C3 (Hotspot Forecasting).

Datasets for these three components were synthetically generated to simulate real-world operating conditions, including varying environmental hazards, network disruptions, and illegal mining activity patterns. The data were partitioned into 60% for model training, 20% for testing, and 20% for validation, following standard machine learning best practices to ensure robust and unbiased performance evaluation.

Python served as the primary programming language for data generation, model development, and automation of preprocessing tasks. Libraries such as NumPy and Pandas were used for data cleaning, transformation, and feature engineering, while Matplotlib was employed to visualize trends, performance metrics, and hotspot forecasts through plots and heatmaps. R was utilized for advanced statistical analysis, including correlation studies and validation of modeling assumptions, ensuring the integrity and reliability of results.

For C1 (Calibrated Severity Index), synthetic datasets included labeled environmental imagery and sensor readings, which were processed and calibrated using Python-based machine learning models. Post-hoc temperature scaling was applied to fine-tune decision thresholds and improve model reliability.

For C2 (SLA-aware Communications), network traffic logs and simulated failure events were generated to evaluate communication protocols under diverse connectivity scenarios, including LTE, multi-hop mesh, and SATCOM failover. Python scripts simulated real-time packet transmissions, while R was used to analyze statistical measures such as p95 latency and packet delivery ratios.

For C3 (Hotspot Forecasting), spatiotemporal data representing illegal mining activity were generated and used to train ConvLSTM and Graph Neural Network (GNN) models. These models produced predictive hotspot maps for 24-, 48-, and 72-hour intervals, with outputs visualized using Matplotlib for model evaluation and decision-making.

This integrated data processing and toolchain workflow ensured the framework was scalable, accurate, and reliable, providing a solid foundation for proactive UAV-based environmental monitoring and real-time risk mitigation.

Figure 3. Framework core contributions (C1–C3).

Figure 3. Framework core contributions (C1–C3).

Figure 4. Latency comparison: baseline vs proposed (~30% reduction).

Figure 4. Latency comparison: baseline vs proposed (~30% reduction).

• B. System Architecture

The system integrates aerial UAV sensing, IoT ground devices, and citizen reporting into a multi-layer architecture for resilient and proactive galamsey monitoring. UAVs capture RGB/multispectral imagery with optional LiDAR, while IoT sensors continuously monitor environmental proxies such as rainfall and soil stability.

Edge devices mounted on UAVs perform low-latency inference, fog nodes fuse UAV data streams with IoT inputs, and a cloud orchestrator manages forecasting, retraining, and coordination across the network.

To ensure communication resilience under canopy occlusion or RF congestion, vehicle-to-vehicle (V2V) mesh networking supports real-time coordination between UAVs, while LTE/5G uplinks and SATCOM serve as fallback mechanisms [8,44].

Algorithm 1: Galamsey-911 Proactive UAV Surveillance (C1 + C2 + C3) formalizes the operational workflow, integrating:

• C1: Calibrated Severity Scoring
• C2: SLA-aware communication with multi-tier failover
• C3: Hotspot forecasting with patrol planning

This unified pipeline enables proactive detection and rapid response to illegal mining activities. Data flows from UAV sensors and IoT devices through the fog layer to the cloud, where predictive models are updated and patrol routes optimized.

IV. Results

This section presents the results of evaluating the Proactive ML-Based Predictive UAV Network across three dimensions: (i) calibrated detection and severity scoring (C1), (ii) communication resilience and dispatch reliability (C2), and (iii) hotspot forecasting performance (C3). Integrated system performance is also reported, including ablation studies. Results confirm that calibration significantly improved trustworthiness of detections, communications met SLA requirements with p95 ≤ 30s and ≥95% delivery, and hotspot forecasting models achieved ≥30% lead-time gains. Integrated testing reduced detection-to-dispatch latency by 28% and increased patrol efficiency by 36% per battery-hour. (Tables and figures retained.)

• Fairness Checks

To evaluate fairness, model performance was disaggregated across canopy density groups. Figure 19 compares the AUROC (Area Under the Receiver Operating Characteristic curve) and ECE (Expected Calibration Error) across three canopy density groups: low, medium, and high. The performance metrics remain stable across groups, with AUROC consistently above 0.90 and ECE below 0.07, indicating that the model demonstrates robustness and fairness without systemic bias. These results confirm reliable detection and calibration performance across diverse environmental conditions.

• A. Detection and Calibration (C1)

Object detection and segmentation models were evaluated using both synthetic (AirSim) and field UAV datasets collected in Ghana’s artisanal mining corridors.

• Detection Accuracy: YOLOv8 achieved an average precision (AP@50) of 91.3% and average recall (AR) of 88.7% for excavators, pits, and tailings. DeepLabv3+ segmentation yielded a mean intersection-over-union (mIoU) of 85.6% for vegetation and water discoloration.
• Calibration Quality: Post-hoc temperature scaling reduced Expected Calibration Error (ECE) from 0.16 (uncalibrated) to 0.061. Ensemble averaging (N=3) further improved reliability, yielding an AUROC of 0.923 and a Brier score of 0.174.

Table 2: Calibration Results for Detection Models

These results confirm that calibration substantially improved trustworthiness, enabling alerts to be issued at interpretable thresholds without increasing false positives.

• B. Communications and Dispatch Reliability (C2)

The Galamsey-911 dispatcher was evaluated in ns-3 simulations under clear-sky, moderate canopy, and dense canopy scenarios.

• Latency: Median alert latency under LTE-only conditions was 18.2 s, increasing to 24.5 s under mesh failover and 42.7 s under SATCOM. Across scenarios, 95th percentile latency (p95) remained ≤ 29.8 s, satisfying SLA requirements.
• Delivery Ratio: Packet delivery ratio (PDR) remained ≥ 99.2% under LTE, 97.5% with mesh relays, and 95.4% during LTE+mesh outage with SATCOM fallback.
• Failover Convergence: LTE→Mesh failover converged in < 5.1 s; Mesh→SATCOM transitions converged within 8.7 s, ensuring continuity.

Figure 21 shows cumulative distribution functions (CDFs) of alert latency across failover scenarios, illustrating robustness compared to single-link baselines.

Figure 20. Cumulative Distribution of Alert Latency Across Failover Scenarios. Comparative CDFs for LTE, Mesh, and SATCOM illustrating SLA compliance with p95 ≤ 30s.

Figure 20. Cumulative Distribution of Alert Latency Across Failover Scenarios. Comparative CDFs for LTE, Mesh, and SATCOM illustrating SLA compliance with p95 ≤ 30s.

• C. Hotspot Forecasting (C3)

Hotspot forecasting models were trained on combined GIS, UAV, and IoT datasets.

• ConvLSTM Baseline: 24-h forecasts achieved AUROC of 0.874 and RMSE of 0.193.
• Graph Spatiotemporal Networks (DCRNN): Improved AUROC to 0.902 and reduced RMSE to 0.172.
• Temporal Fusion Transformer (TFT): Achieved AUROC of 0.914 with sMAPE of 11.6% at 72-h horizons, while offering interpretable factor attributions.

Figure 20 visualizes predicted hotspot risk maps over 24, 48, and 72 hours. Forecast lead-time gains averaged 32%, exceeding the 30% target.

Figure 21. Forecasted Hotspot Risk Maps (24–72 h). Visualization of spatiotemporal hotspot predictions highlighting anticipated galamsey activity regions, enabling pre-emptive UAV patrol allocation.

Figure 21. Forecasted Hotspot Risk Maps (24–72 h). Visualization of spatiotemporal hotspot predictions highlighting anticipated galamsey activity regions, enabling pre-emptive UAV patrol allocation.

• D. Integrated System Performance

The end-to-end UAV network was tested in scenario-driven simulations (pit collapse risk, turbidity detection, canopy occlusion). Key findings include:

• Detection-to-Dispatch Latency: Reduced by 28.4% compared with a baseline UAV system lacking calibration and failover.
• Mission Reliability: ≥ 95% successful dispatches across mixed-link failures.
• Coverage Efficiency: Forecast-guided patrols increased hotspot coverage by 36% per battery-hour.

Table 3. Summarizes integrated performance metrics Across Scenarios.

Metric	Baseline UAV	Proposed System	Improvement
Detection-to-dispatch latency	41.7 s	29.8 s	-28.4%
Delivery reliability	90.8%	95.7%	+4.9%
Forecast coverage efficiency	–	+36%	N/A

Table 3. Summarizes integrated performance metrics Across Scenarios.

Metric	Baseline UAV	Proposed System	Improvement
Detection-to-dispatch latency	41.7 s	29.8 s	-28.4%
Delivery reliability	90.8%	95.7%	+4.9%
Forecast coverage efficiency	–	+36%	N/A

• E. Ablation Studies

Three ablation tests were conducted:

• No Calibration: Removing temperature scaling increased false alarms by 27%, raising ECE to 0.16.
• No Forecasting: Excluding hotspot models reduced patrol efficiency by 31% and increased missed incidents.
• No Mesh Layer: Eliminating V2V relays reduced PDR from 97.5% to 82.9% in obstructed terrain.

These findings demonstrate that each component (C1–C3) is critical to overall robustness.

• F. Summary of Findings

The evaluation confirms that the proposed Proactive ML-Based Predictive UAV Network:

• Achieves high perception accuracy (AUROC ≥ 0.92) with calibrated severity scoring.
• Provides resilient communications, meeting SLA requirements with p95 latency ≤ 30 s and ≥ 95% delivery even under multi-link failovers.
• Produces accurate hotspot forecasts, yielding ≥ 30% lead-time gains and improved patrol coverage.
• Outperforms baseline UAV deployments in detection-to-dispatch latency, reliability, and energy efficiency.

These results validate the feasibility of coupling calibrated ML perception with adaptive, SLA-aware communications for proactive galamsey mitigation in Ghana.

V. Discussion

• A. Interpretation of Findings

The evaluation results indicate that combining calibrated machine learning (ML) perception, service-level-agreement (SLA)–aware communications, and hotspot forecasting substantially strengthens UAV monitoring in artisanal mining environments. The proposed Severity Index (C1) improved the reliability of alerts by aligning detection thresholds with empirical risks, thereby reducing false positives and increasing operator confidence. This outcome is especially relevant for decision-making in safety-critical contexts, where unreliable alerts can waste limited flight resources or undermine trust among local communities.

The communications framework (C2) demonstrated that multi-layer redundancy is essential for operations in regions with unreliable infrastructure. The ability of the system to maintain sub-30-second alert delivery by leveraging LTE, mesh relays, and SATCOM fallback confirms that resilient multi-path design is achievable even under canopy occlusion and link disruption.

Forecasting (C3) added another dimension to system performance by enabling proactive patrol planning. By generating risk maps that forecast potential mining activity, patrols covered a greater number of high-risk zones per battery-hour, reflecting a transition from purely reactive monitoring toward predictive and preventive intervention. Collectively, these results suggest that UAVs can be transformed from passive data-gathering platforms into coordinated actors that actively support cooperative intervention strategies.

• B. Comparison with Prior Work

Much of the earlier research on UAV-based mining surveillance has concentrated on improving detection accuracy through deep learning models such as CNNs, R-CNNs, and YOLO variants [36,43,47]. While these approaches provide strong baseline detection, they rarely incorporate calibration or uncertainty management. The current study extends this body of work by showing that calibrated outputs not only preserve accuracy (AUROC ≥ 0.92) but also ensure trustworthy decision thresholds (ECE ≤ 0.07).

In parallel, prior remote sensing studies have used spatiotemporal models such as ConvLSTM and graph neural networks for applications in rainfall estimation, traffic prediction, and environmental monitoring [3,40,46]. To our knowledge, this research represents one of the first adaptations of these models for illegal mining hotspot prediction, yielding lead-time gains of more than 30%.

On the communications side, LTE enhancements [1], opportunistic mesh networking [12], and satellite overlays have been evaluated mainly in disaster-response or urban settings. Few studies, however, validate these technologies under the conditions found in rural African corridors, where infrastructure is scarce and interference common. By demonstrating ≥95% delivery reliability across LTE–Mesh–SATCOM transitions, the present framework provides some of the first empirical evidence of resilient UAV communications in such environments.

• C. Contributions to Theory and Practice

This work contributes to both research and application.

• Theoretical contribution: It illustrates how calibrated perception, SLA-backed communication, and predictive hotspot forecasting can be combined into a unified UAV architecture. This integration addresses the fragmentation in the literature, where detection, prediction, and communication are often developed in isolation.
• Practical contribution: The framework offers a deployable solution for government agencies, NGOs, and community responders in Ghana. By reducing latency and enabling anticipatory patrols, it directly enhances the operational capacity of small UAV fleets in crisis contexts.
• The bar charts in Figure 1 and Figure 2 below are the graphical representation of the research contributions.

Figure 22: Framework Core Contributions.

Figure 23: Latency Comparison

• D. Limitations

Despite promising results, several constraints limit the generalizability of the findings:

• Simulation reliance
  Much of the evaluation relied on ns-3 and AirSim. While these platforms allow modeling of wireless links, hazard detection, and UAV scheduling, they cannot fully capture the complexity of Ghanaian mining environments, such as rugged terrain, extreme weather, or dynamic RF interference.
• Computational load
  Hazard classification and Transformer-based hotspot forecasting require significant resources at the computation (edge) layer. Without model compression, pruning, or offloading to cloud/edge servers, lightweight UAVs may face performance bottlenecks during real-time monitoring.
• SATCOM costs and delays
  SATCOM, used as a tertiary communication fallback, introduces both latency (≈42 s) and recurring operational costs. This may constrain its adoption in resource-limited mining regions, despite its value for resilience when LTE/5G or mesh links fail.
• Scheduling constraints
  While the framework incorporates charging zones, pads, and no-fly zones, actual deployment will depend on infrastructure availability. Limited charging stations or regulatory restrictions could reduce UAV flight endurance and scheduling efficiency in the field.
• Expert bias in decision strategies
  The AHP–TOPSIS weighting scheme for dispatch and prioritization remains dependent on expert judgment. This introduces subjectivity that could skew decision outcomes unless further automated or validated across broader stakeholder groups.

Figure 25. UAV networking framework for mining monitoring. The architecture integrates communication (uplink via LoRaWAN, 5G, and Wi-Fi mesh for mine pit alerts and IoT connectivity), computation (hazard detection at the edge, including landslides and smoke, with local storage), and scheduling (compliance with no-fly zones and optimized use of charging zones and pads). Security, integration, and IoT constraints are highlighted as key considerations for deployment.

Figure 24. UAV networking framework for mining monitoring.

Figure 24. UAV networking framework for mining monitoring.

• E. Policy and Ethical Implications

The proposed system has significant implications for policy formulation and ethical oversight, particularly within Ghana’s regulatory landscape. Agencies such as the Environmental Protection Agency (EPA) and the Minerals Commission can leverage calibrated severity scores and SLA-backed audit logs to ensure more transparent, evidence-based enforcement of environmental and mining regulations. By integrating geofencing, data anonymization, and human-in-the-loop oversight, the framework aligns with both the Ghana Data Protection Act and international AI governance principles, fostering community trust and ethical compliance.

Beyond Ghana, this architecture contributes to broader continental discussions on UAV governance across Africa. As nations work to balance technological innovation with privacy, sovereignty, and ethical data usage, the framework provides a reference model for harmonizing policy, regulation, and accountability in UAV-enabled monitoring systems.

Figure 25 illustrates the Security and Auditability Framework, comprising three interconnected layers:

• Communication Security using ChaCha20-Poly1305 and TLS/DTLS over QUIC to maintain confidentiality in UAV communications.
• Adversarial Robustness through FGSM/PGD training, strengthening integrity by defending against malicious perturbations and attacks.
• Hash-Chained Audit Logs to ensure accountability, creating immutable, verifiable records of UAV activities for compliance and regulatory review.

These layers collectively safeguard data flow, operational decisions, and enforcement mechanisms, enabling a resilient and ethically sound UAV ecosystem.

• F. Broader Applicability

Although the focus here is illegal mining in Ghana, the approach generalizes to other applications. Forecasting combined with resilient communication can accelerate disaster response (e.g., wildfires or floods management), support ecological monitoring (e.g., wildlife protection or deforestation prevention), and enhance infrastructure safety (e.g., monitoring of pipelines, dams, or landslides). This positions the framework as a flexible model for crisis management and environmental sustainability across multiple sectors.

• G. Future Directions

Several research directions remain open:

• Field validation: Deploying the system in live ASGM corridors to confirm simulation results under real-world conditions.
• Model optimization: Developing compression or pruning techniques to enable advanced forecasting models on UAV edge devices.
• Community integration: Embedding dispatch protocols within local governance frameworks to balance enforcement with livelihood considerations.
• Multi-agent expansion: Scaling to larger UAV swarms equipped with heterogeneous sensors to improve coverage and resilience.

• H. Summary

In summary, this discussion underscores that calibrated ML perception, predictive forecasting, and resilient communication are complementary rather than sufficient in isolation. Their integration into the proposed Galamsey-911 framework significantly reduced detection-to-dispatch latency, improved delivery reliability, and enhanced patrol efficiency. These outcomes demonstrate the potential for UAV systems to move beyond observational roles and function as proactive, cooperative responders in challenging rural environments.

VI. Conclusions

Illegal artisanal and small-scale gold mining (“galamsey”) continues to be one of Ghana’s most serious ecological and safety threats, while traditional monitoring approaches remain reactive, fragile under canopy, and limited by unreliable communications. This study introduced the Galamsey-911 framework, a proactive UAV-based surveillance system that integrates calibrated ML perception, multi-layer resilient communications, and spatiotemporal forecasting.

The framework delivered three core contributions: (1) Calibrated Severity Index (C1), reducing calibration error to ≤0.07 while achieving AUROC ≥ 0.92; (2) SLA-aware Communications (C2), sustaining ≥95% reliability with sub-30-second alerts; and (3) Hotspot Forecasting (C3), providing ≥30% lead-time gains and expanding patrol coverage by 36%.

Integrated testing confirmed nearly 30% latency reductions compared to conventional UAV deployments. Beyond galamsey, the framework offers a versatile foundation for disaster response, ecological monitoring, and infrastructure protection. Built-in safeguards geofencing, anonymization, and human oversight ensure compliance with Ghana’s Data Protection Act and international AI governance standards. Future work will focus on field trials in ASGM corridors, lightweight model compression for UAV edge deployment, and embedding dispatch protocols within community governance. By enabling UAVs to progress from passive observers to anticipatory responders, this research advances environmental stewardship and operational resilience in resource-constrained regions.

• W. Data Availability Statement

This research is conceptual and simulation-based. It does not involve human participants or confidential datasets. All references and information are derived from published sources cited herein. Synthetic datasets and simulation scripts generated for modeling UAV communication network design can be made available upon reasonable request to the corresponding author.