New Year Offer End Date: 30th April 2024
Program
AI for Ecosystem Intelligence, Biodiversity Monitoring & Restoration Planning
Applying artificial intelligence to understand, protect, and restore ecosystems in a rapidly changing world.
Skills you will gain:
About Program:
An advanced 3-day workshop on applying AI to ecosystem intelligence—covering biodiversity monitoring, habitat mapping, ecological forecasting, and restoration planning—while emphasizing scientific validity, uncertainty, ethics, and alignment with conservation policy, UN SDGs, and global biodiversity frameworks.
Aim: To train participants to use AI for biodiversity monitoring, ecological forecasting, and restoration planning.
Program Objectives:
- Use AI for biodiversity monitoring, species detection, and habitat mapping.
- Build ecological forecasting models for climate- and land-use impacts.
- Integrate multi-source ecological data (field, sensors, remote sensing, GIS).
- Evaluate uncertainty, bias, interpretability, and ecological validity.
- Design restoration prioritization and conservation decision-support workflows aligned with policy, SDGs, and biodiversity frameworks.
What you will learn?
📅 Day 1 — AI for Ecosystem Monitoring & Biodiversity Intelligence
Theme: From sensing → ecological evidence
- Ecosystem intelligence framework: end-to-end pipeline (sensing → inference → decision).
- Biodiversity data streams: field surveys, camera traps, bioacoustics, (optional eDNA), remote sensing + GIS + climate layers, and citizen science (bias + coverage gaps).
- AI for monitoring: species detection/classification, habitat mapping, fragmentation/change analysis, and multimodal fusion (species + habitat + climate).
- Scientific rigor: data quality, sampling bias, spatial leakage, and reproducibility.
- Hands-On: Monitoring Pipeline Design Sprint (data → model → validation → decision output).
📅 Day 2 — AI-Enabled Ecosystem Modeling & Ecological Forecasting
Theme: From patterns → predictions (with uncertainty)
- Ecological modeling foundations: populations, interactions/food webs, and ecosystem services (what you predict & why).
- AI + hybrid forecasting: species distribution shifts, resilience/tipping points, and integration of climate/land-use drivers.
- Trust in models: uncertainty, interpretability, and scenario analysis for decision-relevant forecasting.
- Peer-review evaluation: spatial/temporal cross-validation and ecology-aligned metrics (beyond accuracy).
- Hands-On: Forecasting Model Stress-Test (bias, leakage, shift, uncertainty, decision risk).
📅 Day 3 — AI for Restoration Planning, Policy & SDGs
Theme: From predictions → action under constraints
- Restoration prioritization: suitability mapping, multi-objective planning, and constraints (budget, access, governance, permanence).
- Decision-support systems: dashboards/KPIs and adaptive management loops (monitor → learn → update).
- Responsible conservation AI: sensitive species data handling, consent, transparency, and auditability.
- Alignment: UN SDGs + biodiversity frameworks; policy-ready outputs (maps + uncertainty bands + narratives).
- Hands-On Capstone: Nature-Positive Restoration Mini-Plan (deliverables, stakeholders, metrics).
Mentor Profile
Fee Plan
INR 1999 /- OR USD 50
Get an e-Certificate of Participation!
Intended For :
- Senior academicians, faculty members, and researchers in ecology, environmental science, conservation biology, or related fields.
- PhD scholars and postdoctoral researchers working on biodiversity, climate impacts, ecosystem services, restoration, or geospatial/ecological modeling.
- Conservation professionals from NGOs, government agencies, research institutes, and environmental consultancies.
- Environmental planners and policy researchers involved in land-use planning, restoration programs, sustainability, or nature-positive initiatives.
- Participants should have basic familiarity with ecological concepts; prior AI/ML experience is helpful but not mandatory (tools will be explained conceptually).
Career Supporting Skills
Program Outcomes
- A systems-level understanding of AI for ecosystem monitoring, forecasting, and restoration planning.
- Ability to design biodiversity intelligence and decision-support workflows using multi-source ecological data.
- Skills to assess model uncertainty, bias, interpretability, and ethical deployment.
- Clear direction for fundable research, publications, and policy/NGO engagement aligned with SDGs and biodiversity frameworks.