Artificial Intelligence applied to environmental monitoring

Section outline

• Select section Description

  Description

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  in-person

  Ecotekne University Campus, Via Lecce-Monteroni, 73100 LE

  BUILDING A, SEMINAR ROOM I FLOOR

  Classroom location

  (hold down CTRL+left mouse button and click the Google Maps link)

  https://maps.app.goo.gl/R47PY17cBwZA4fXV9

  From June 3rd to June 6th, 2025

  25 (in-person)

  Francesco Iarlori, Vittoria Mascellaro

  

  This intensive four-day course offers an interdisciplinary exploration of Artificial Intelligence (AI) and Machine Learning (ML) applications within environmental sciences. Participants will gain foundational knowledge of AI concepts, including supervised, unsupervised, and reinforcement learning, and understand how these technologies are transforming environmental research and practices.

  
  The curriculum delves into practical applications such as biodiversity monitoring through image and audio recognition, climate modelling, remote sensing via satellites and drones, and big data analysis for air, water, and soil quality.

  
  Critical discussions will address the technical challenges of AI in environmental contexts, including data quality, model biases, and system complexities. Ethical considerations will be a focal point, examining issues like surveillance, data privacy, the impact of automation on scientific work, and the implications of AI in environmental policymaking.

  
  Through a combination of lectures, hands-on activities, group discussions, and a mini-project, participants will develop the skills to critically assess and apply AI tools in environmental science, fostering a responsible and effective integration of technology in the pursuit of sustainability and environmental justice.
• Select section Training objectives

  Training objectives

  • Understand Fundamental Concepts: Define and distinguish between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning, including various learning paradigms such as
    supervised, unsupervised, and reinforcement learning.
  • Apply AI in Environmental Contexts: Utilize AI tools and techniques for environmental applications, including biodiversity monitoring, climate modelling, remote sensing, and big data analysis related to air, water, and soil quality.
  • Evaluate Ethical Implications: Assess the ethical considerations of deploying AI in environmental contexts, focusing on privacy, data sovereignty, automation impacts, and equitable decisionmaking.
• Select section Jointed Modules

  Jointed Modules

  • Select activity Introduction to AI and Machine Learning (ML)_Jointed Version

    

    Introduction to AI and Machine Learning (ML)_Jointed Version File PDF
  • Select activity Artificial Intelligence applied to environmental monitoring_Jointed Version

    

    Artificial Intelligence applied to environmental monitoring_Jointed Version File PDF
  • Select activity Technical challenges and limitations of environmental AI_Jointed Version

    

    Technical challenges and limitations of environmental AI_Jointed Version File PDF
  • Select activity Ethics of AI and Environmental Justice_Jointed Version

    

    Ethics of AI and Environmental Justice_Jointed Version File PDF
• Select section Module 1 - Introduction to AI and Machine Learning (ML)

  Module 1 - Introduction to AI and Machine Learning (ML)

  • Select activity Introduction - Artificial Intelligence applied to environmental

    

    Introduction - Artificial Intelligence applied to environmental File PDF
  • Select activity Module 1 - Definitions and Key Concepts

    

    Module 1 - Definitions and Key Concepts File PDF
  • Select activity Module 2 - Types of Learning

    

    Module 2 - Types of Learning File PDF
  • Select activity Module 3 - Datasets, Algorithms, and Models

    

    Module 3 - Datasets, Algorithms, and Models File PDF
  • Select activity Module 4 - Building a Simple ML Model

    

    Module 4 - Building a Simple ML Model File PDF
  • Select activity Module 5 - Final Activity & Review Quiz

    

    Module 5 - Final Activity & Review Quiz File PDF
• Select section Module 2 - Applications of AI in Environmental Sciences

  Module 2 - Applications of AI in Environmental Sciences

  • Select activity Module 6 - Biodiversity Monitoring with AI

    

    Module 6 - Biodiversity Monitoring with AI File PDF
  • Select activity Module 7 - Climate Modelling and Extreme Events Prediction

    

    Module 7 - Climate Modelling and Extreme Events Prediction File PDF
  • Select activity Module 8 - Remote Sensing with AI

    

    Module 8 - Remote Sensing with AI File PDF
  • Select activity Module 9 - Big Environmental Data Analysis

    

    Module 9 - Big Environmental Data Analysis File PDF
  • Select activity Module 10 - Language Models & NLP in Research

    

    Module 10 - Language Models & NLP in Research File PDF
• Select section Module 3 - Technical Challenges and Limitations of Environmental AI

  Module 3 - Technical Challenges and Limitations of Environmental AI

  • Select activity Module 11 - AI and Data

    

    Module 11 - AI and Data File PDF
  • Select activity Module 12 - Enviromental data

    

    Module 12 - Enviromental data File PDF
  • Select activity Module 13 - AI ethics

    

    Module 13 - AI ethics File PDF
• Select section Module 4 - Ethics of AI and Environmental Justice

  Module 4 - Ethics of AI and Environmental Justice

  • Select activity Module 14 - Governance and AI ActTraining

    

    Module 14 - Governance and AI ActTraining File PDF
  • Select activity Module 15 - Advanced technologies for environmental management

    

    Module 15 - Advanced technologies for environmental management File PDF
  • Select activity Module 16 AI for environmental and land monitoring

    

    Module 16 AI for environmental and land monitoring File PDF