Announcements
6 May 2025
Topics Webinar | EO&GEO Series: Investigating State-of-the Art Machine Learning Approaches in Vegetation Analysis Through Earth Observation Data, 15 May 2025
A message from the webinar Chair:
Vegetation cover maps, whether they focus on structural attributes, ecological aspects, or biomass content, are invaluable for understanding Earth’s ecosystems in a spatial context.
The advent of Earth Observation data has transformed vegetation mapping and trend analysis, delivering datasets with various spatial and spectral resolutions on a global scale. Vegetation mapping and analysis provide critical insights into the distribution and density of vegetation, while also highlighting the impact of environmental changes on biodiversity and ecosystems. The accurate interpretation of this remote sensing data necessitates sophisticated analytical techniques to manage their complexity and vastness.
Join us for an insightful webinar that explores cutting-edge machine learning methodologies and their role in vegetation analysis using Earth Observation (EO) data. This webinar features four scholars presenting their latest research on how machine learning and EO data can enhance vegetation monitoring and mapping. This will facilitate the exchange of insights and ideas among participants while fostering opportunities for future collaboration.
We are privileged to welcome esteemed research scientists and academics from recognized research institutions and universities in Australia. They will share their expertise and findings on the effective application of machine learning technology in Earth Observation data in obtaining essential information related to climate change, land use planning, ecosystem conservation, weed management, and agricultural management.
Webinar: EO&GEO Series: Investigating State-of-the-Art Machine Learning Approaches in Vegetation Analysis through Earth Observation Data
Date: 15 May 2025
Time: 3.00 p.m. AEST | 1.00 p.m. CST | 7.00 a.m. CEST
Webinar ID: 854 2945 1681
More information: https://sciforum.net/event/topics-34
This is a free webinar. After registration, you will receive a confirmation email on how to join the webinar. Registrations with academic institutional email addresses will be prioritized.
Unable to attend? Register anyway, and we will let you know when the recording becomes available for viewing.
Register for free:
Program:
| Speaker/Presentation | Time in AEST | Time in CST (Asia) | Time in CEST |
| Dr. Arnick Abdollahi Chair Introduction |
15:00–15:10 | 13:00–13:10 | 07:00–07:10 |
| Dr. Catherine Ticehurst Generating an Australia-Wide Vegetation Height Product by Combining Gedi Satellite Lidar with Optical, Radar and Climate Earth Observation Data in a Machine Learning Model |
15:10–15:30 | 13:10–13:30 | 07:10–07:30 |
| Q&A Session | 15:30–15:40 | 13:30–13:40 | 07:30–07:40 |
| Dr. Sanjeev Kumar Srivastava Earth Observation Data Fusion for Improved Mapping of Weeds in Natural and Plantation Forests with Machine Learning Approaches |
15:40–16:00 | 13:40–14:00 | 07:40–08:00 |
| Q&A Session | 16:00–16:10 | 14:00–14:10 | 08:00–08:10 |
| Dr. Chandrama Sarker Chair Introduction |
16:10–16:20 | 14:10–14:20 | 08:10–08:20 |
| Dr. Arnick Abdollahi Harnessing Earth Observation Technology and Machine Learning for National Biomass Assessment in Australia |
16:20–16:40 | 14:20–14:40 | 08:20–08:40 |
| Q&A Session | 16:40–16:50 | 14:40–14:50 | 08:40–08:50 |
| Dr. Kate Giljohann and Dr. Roozbeh Valavi Ecosystem Condition Modelling Using the Habitat Condition Assessment System (Hcas) and Its Applications in Australia |
16:50–17:10 | 14:50–15:10 | 08:50–09:10 |
| Q&A Session | 17:10–17:20 | 15:10–15:20 | 09:10–09:20 |
| Closing of Webinar | 17:20–17:25 | 15:20–15:25 | 09:20–09:25 |
Webinar Chairs and Speakers:
- Dr. Arnick Abdollahi, University of Technology Sydney, New South Wales, Australia;
- Dr. Chandrama Sarker, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia;
- Dr. Catherine Ticehurst, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia;
- Dr. Sanjeev Kumar Srivastava, University of the Sunshine Coast, Queensland, Australia;
- Dr. Roozbeh Valavi, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia);
- Dr. Kate Giljohann, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.
Relevant Special Issue:
“Investigating State-of-the-Art Machine Learning Approaches in Vegetation Analysis through Earth Observation Data”
Guest Editors: Dr. Arnick Abdollahi and Dr. Chandrama Sarker
Deadline for submission: 25 May 2025
Relevant Papers:
“Retrieval of Crop Canopy Chlorophyll: Machine Learning vs. Radiative Transfer Model”
by Mir Md Tasnim Alam, Anita Simic Milas, Mateo Gašparović and Henry Poku Osei
Remote Sens. 2024, 16(12), 2058; https://doi.org/10.3390/rs16122058
“A Robust Dual-Mode Machine Learning Framework for Classifying Deforestation Patterns in Amazon Native Lands”
by Julia Rodrigues, Mauricio Araújo Dias, Rogério Negri, Sardar Muhammad Hussain and Wallace Casaca
Land 2024, 13(9), 1427; https://doi.org/10.3390/land13091427
“Evaluating Land Surface Temperature Trends and Explanatory Variables in the Miami Metropolitan Area from 2002–2021”
by Alanna D. Shapiro and Weibo Liu
Geomatics 2024, 4(1), 1-16; https://doi.org/10.3390/geomatics4010001
“A GIS-Based Framework to Analyze the Behavior of Urban Greenery During Heatwaves Using Satellite Data”
by Barbara Cardone, Ferdinando Di Martino, Cristiano Mauriello and Vittorio Miraglia
ISPRS Int. J. Geo-Inf. 2024, 13(11), 377; https://doi.org/10.3390/ijgi13110377
“UAV-Based Wetland Monitoring: Multispectral and Lidar Fusion with Random Forest Classification”
by Robert Van Alphen, Kai C. Rains, Mel Rodgers, Rocco Malservisi and Timothy H. Dixon
Drones 2024, 8(3), 113; https://doi.org/10.3390/drones8030113
