MAITRI
Bridging the Gap Between Humans and Wilds
The problem MAITRI solves
The problem MAITRI solves
MAITRI is our comprehensive solution for preventing wildlife-human conflicts and ensuring safety for both communities and wildlife. The platform offers innovative features like real-time conflict risk prediction, where advanced machine learning algorithms analyze satellite data, weather patterns, and historical incident records to forecast potential hotspots. When risk levels exceed safe thresholds, instant notifications are sent to villagers, tourists, and forest officials within affected areas. Additionally, MAITRI integrates crowdsourced wildlife sighting reports with intelligent routing systems, alerting users about safe travel corridors and high-risk zones marked on interactive maps.
Beyond conflict prevention, the platform also excels in community empowerment and tourism safety. It includes a dual-mode interface that serves both local villagers with crop protection alerts and SMS warnings in regional languages, and tourists with guided trail recommendations, nearest forest checkpost locations, and real-time safety protocols. With MAITRI, whether it's protecting vulnerable farming communities from elephant raids or ensuring wildlife enthusiasts have safe safari experiences, you have the tools to safeguard what matters most.
Challenges we ran into
Developing MAITRI presented several challenges that we overcame to create a seamless and effective platform. The problems we faced are discussed below:
- Frontend - Backend Integration
- Running parallelly Dual ML Models of XGBoost -
i) Classification Model for Nearby Villagers for Risk Categorization: Low, Medium and High.
ii) Regression Model for Tourist Enthusiasts, Tour Guiding and Forest Officials - Showing Live Location Updates of Wildlife Species nearby and generating a safe route for travellers.
- Faced problem while creating & establishing a private network for testing purposes.
- The scarcity of publicly available training data, combined with pronounced class imbalance—specifically the underrepresentation of 'SAFE' alerts—creates a dual challenge that compromises model performance and generalizability in alert categorization systems.
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