Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Ecosystem Intelligence, Biodiversity Monitoring & Restoration shows how machine learning helps us understand and protect the living world at scale. You learn to apply AI to biodiversity data — camera-trap and acoustic recordings, satellite imagery and citizen-science observations — to identify species, track populations and habitats, and detect threats like deforestation and poaching. The course connects monitoring to conservation and restoration decisions, and to the goal of reversing biodiversity loss. You finish able to reason about an AI approach to a biodiversity problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to ecosystem intelligence — monitoring biodiversity, tracking species and habitats, and supporting conservation and restoration decisions.
📋 Course Objectives
1. Identify species from images and audio.
2. Track populations and habitats over time.
3. Detect threats like deforestation and poaching.
4. Work with citizen-science and satellite data.
5. Connect monitoring to conservation action.
👥 Who Should Enroll?
• Ecologists and conservation professionals
• Environmental data scientists
• Wildlife and restoration researchers
• Students of conservation technology
🚀 Key Learning Outcomes
• An understanding of AI for biodiversity.
• A conservation-monitoring perspective.
• An ecosystem-analytics project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Measuring Biodiversity Credibly
• Occupancy and detectability: absence of evidence is not absence
• Survey design, sampling effort and spatial bias in citizen-science data
• Biodiversity metrics and what each does and does not capture
Acoustic, Camera Trap and eDNA Streams
• Bioacoustic monitoring and species classification from soundscapes
• Camera trap pipelines: detection, individual identification, sequence handling
• Environmental DNA metabarcoding and bioinformatic assignment limits
Habitat and Change Detection
• Optical and radar satellite data for land cover and canopy structure
• Change detection, deforestation alerts and cloud-gap handling
• Scale mismatch between satellite pixels and ecological processes
Distribution and Restoration Planning
• Species distribution modelling and extrapolation under climate change
• Connectivity and corridor analysis for restoration prioritisation
• Spatial prioritisation under budget and land-tenure constraints
Evidence, Reporting and Communities
• Monitoring restoration outcomes rather than area planted
• Reporting frameworks including TNFD and national biodiversity commitments
• Working with local and indigenous knowledge holders and data sovereignty
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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