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Work item:
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Y.aiwl
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Subject/title:
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AI driven Wild Life Conservation and Monitoring Service Model
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Status:
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Under study
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Approval process:
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AAP
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Type of work item:
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Recommendation
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Version:
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New
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Equivalent number:
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-
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Timing:
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2025-Q3 (Medium priority)
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Liaison:
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SG20, SG2 FG
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Supporting members:
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India, Brazil, Sunchon national university (Rep.of Korea), China telecom
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Summary:
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Wildlife conservation and monitoring are crucial today for preserving biodiversity, ensuring ecosystem stability, and maintaining genetic diversity essential for species adaptation. These efforts align with several United Nations Sustainable Development Goals (SDGs), such as SDG 13 (Climate Action), and SDG 15 (Life on Land) and Global initiatives like the Convention on Biological Diversity (CBD), the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) etc which emphasize the need to protect ecosystems and biodiversity. This work item on AI-Driven Wildlife Conservation and Monitoring system utilizes a sophisticated integration of technologies to enhance the effectiveness of wildlife protection and habitat management. This would be helpful in real-time emergency situations like poaching, disasters like forest fires, floods, animal deaths due to accidents if roads and railway tracks are running inside the forest etc.
The process begins with deploying IoT sensors throughout wildlife habitats to collect environmental and animal-specific data. Data collected from sensors, cameras, and GPS trackers is transmitted through a robust network infrastructure integrating cellular, satellite and long range low power communications. AI-driven Edge computing devices process the data locally to reduce latency and bandwidth usage, allowing real-time decision-making in the field using machine-learning algorithms. Initial data analysis and anomaly detection occur at these edge nodes. Processed data is then transmitted to cloud computing systems for storage in data lakes and data warehouses. Advanced AI and machine learning models analyze this vast dataset. Anomaly detection models identify unusual patterns that may indicate poaching activities or environmental changes, triggering alerts for immediate action. Finally, system's user interface includes mobile applications and web portals equipped with GIS-based data visualization tools, such as interactive maps and charts, which provide conservationists with real-time alerts, monitoring dashboards, and comprehensive visual representations of data which facilitates informed decision-making
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Comment:
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-
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Reference(s):
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Historic references:
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Contact(s):
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First registration in the WP:
2024-08-16 11:55:47
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Last update:
2024-08-16 12:01:28
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