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Hybrid Event

1st - 2nd October 2026 | London, UK

International Conference on AI-driven Data Science for Environmental Monitoring (ICIADSEM - 26)

4

Days

4

Hrs

07

Min

02

Sec

Conference Program

Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

Why it matters

SDG 2 — Zero Hunger
SDG 3 — Good Health and Well-being
SDG 9 — Industry, Innovation and Infrastructure
SDG 11 — Sustainable Cities and Communities
SDG 12 — Responsible Consumption and Production
SDG 13 — Climate Action
SDG 14 — Life Below Water
SDG 15 — Life on Land
SDG 17 — Partnerships for the Goals
Explore All Session Tracks
Track 01
AI Techniques in Environmental Data Analysis

This track focuses on the application of artificial intelligence methodologies in the analysis of environmental data. Researchers are invited to present innovative AI techniques that enhance the understanding of ecological systems.

Track 02
Machine Learning for Climate Change Mitigation

This session aims to explore the role of machine learning in developing strategies for climate change mitigation. Papers should address novel algorithms and their applications in predicting climate-related phenomena.

Track 03
Remote Sensing Innovations for Environmental Monitoring

This track highlights advancements in remote sensing technologies and their applications in environmental monitoring. Contributions should discuss new methodologies for satellite image processing and data interpretation.

Track 04
Ecological Modeling and Simulation

This session invites research on ecological modeling techniques that utilize data science for simulating environmental processes. Participants are encouraged to share models that address biodiversity and ecosystem dynamics.

Track 05
Pollution Detection and Analysis

This track focuses on the development of AI-driven methods for pollution detection and analysis. Submissions should present case studies or novel approaches that utilize big data analytics for environmental health assessment.

Track 06
Biodiversity Monitoring through Data Science

This session explores the application of data science in monitoring and preserving biodiversity. Papers should highlight innovative approaches to data collection and analysis that inform conservation strategies.

Track 07
Natural Disaster Prediction and Management

This track addresses the use of machine learning and AI in predicting and managing natural disasters. Contributions should focus on predictive models and their effectiveness in disaster response and recovery.

Track 08
Weather Forecasting with AI and Big Data

This session invites research on the integration of AI and big data analytics in improving weather forecasting accuracy. Participants are encouraged to present novel algorithms and their practical applications in meteorology.

Track 09
Geospatial AI for Environmental Insights

This track examines the intersection of geospatial analysis and AI in deriving insights from environmental data. Contributions should explore innovative applications of geospatial technologies in environmental research.

Track 10
Smart Agriculture and Sustainable Practices

This session focuses on the role of AI and data science in promoting smart agriculture and sustainable farming practices. Papers should discuss technological innovations that enhance agricultural productivity while minimizing environmental impact.

Track 11
Environmental Big Data: Challenges and Solutions

This track addresses the challenges associated with managing and analyzing environmental big data. Researchers are invited to propose solutions that leverage AI and data science to overcome these challenges.

2026 UPDATE

Consistent Academic Support

Science Net ensures that research activities continue without interruption in the current global situation. Participants can engage through digital and hybrid conference formats.