Monitoring biodiversity is severely constrained by the high costs and temporal gaps of traditional surveys. eBorneensis is a novel smartphone application developed for UMS to empower the campus community to act as citizen scientists by recording wildlife encounters. Powered by an advanced AiGeo backend, the system processes spatial data intelligently. To ensure data reliability, the system employs an expert-in-the-loop validation protocol where ITBC experts verify submissions before database integration. This facilitates robust, long-term monitoring and generates high-resolution spatial data to map biodiversity hotspots, guiding sustainable eco-campus management.
Traditional surveys are resource-intensive leading in data gaps that miss rapid population changes.
While crowdsourcing offers scale, data often suffers from misidentification by inexperienced observers.
Leveraging the campus community to generate continuous, large-scale spatial wildlife data.
Backing community observations with expert oversight ensures highly reliable and accurate results.
AiGeo backend provides a constant stream of reliable spatial data for effective conservation efforts.
Rigorous review by ITBC experts guarantees the reliability of user submissions.
Smart mapping and predictive modeling powered by an intelligent spatial AI backend.
Users record media; the app automatically extracts GPS and metadata.
Cloud backend intelligently organizes and verifies spatial records.
Encryption ensures compliance with the Malaysian PDPA 2010.
Interactive dashboards display verified wildlife encounters instantly.
AiGeo driven maps pinpointing exact locations of wildlife within UMS.
Mapping of breeding grounds and travel routes for local and migratory species.
Continuous journal serving as a baseline for future ecological research.
Drastically Reduced Overhead costs for environmental monitoring.
Provides data for sustainable campus infrastructure planning.
Directly reports UN SDG Alignment (Sustainable Development Goals).
Designed for licensing by universities, municipalities, and protected urban micro-refugia.
Utilizes PWA and ER database structures powered by AiGeo to ensure scalability.
Specifically targets tropical regions facing urgent and similar conservation challenges.
The system is built for immediate commercialization and adaptation across new territories.
Provides a repeatable, AI-enhanced framework for managing biodiversity through structured spatial data.
Expanding the system's digital framework to monitor landscapes beyond the university borders.
Successfully transforms the university community into engaged participants in local protection.
Enhancing the protection of broader ecosystems through integrated regional monitoring.
Overcomes technical limitations of crowdsourced platforms using AI-driven spatial validation.