PROGRAM: Schedule and Abstracts
MONDAY
- 8:30 Registration
- 9:00 Opening Session
- 9:15 Machine Learning and Remote Sensing for Wildfire and Spatial Risk Management
- 10:30 Coffee break
- 10:45 Machine Learning and Remote Sensing for Wildfire and Spatial Risk Management
- 12:30 Lunch
- 14:00 Machine Learning and Remote Sensing for Wildfire and Spatial Risk Management
- 15:30 Coffee break
- 15:45 Hyperspectral remote sensing for UAV-based monitoring
- 17:30 Icebreaker
TUESDAY
- 9:00 A comprehensive tool for analyzing and processing hyperspectral data
- 10:30 Coffee break
- 10:45 A comprehensive tool for analyzing and processing hyperspectral data
- 12:30 Lunch
- 14:00 A comprehensive tool for analyzing and processing hyperspectral data
- 15:30 Coffee break
- 15:45 EO for Mapping Shallow Water Marine Habitats
- 17:30 Class ends / Poster Session
WEDNESDAY
- 9:00 Field Trip: Krka National Parkwith scheduled drone and LiDAR data collection activities.
- 12:30 Lunch
- 14: 30 Field Trip: Krka National Park with scheduled drone and LiDAR data collection activities.
- 17:30 Return to Campus
THURSDAY
- 9:00 Developments and status of current and upcoming ESA SAR missions
- 10:30 Coffee break
- 10:45 Basics on SAR with a practical session on preprocessing of SAR image
- 12:30 Lunch
- 14:00 Basics on SAR with a practical session on preprocessing of SAR image
- 15:30 Coffee break
- 15:45 Basics on SAR with a practical session on preprocessing of SAR image
- 17:30 Class ends
- 19:00 Split @ night – Guided walking tour
- ~20:30 Social dinner (0ptional)
FRIDAY
- 9:00 UAS LiDAR for Landslide Slope Monitoring and Change Detection – LAStools
- 10:30 Coffee break
- 10:45 UAS LiDAR for Landslide Slope Monitoring and Change Detection – LAStools
- 12:30 Lunch
- 14:00 A hands-on course focused on handheld LiDAR data collection and processing
- 15:30 Coffee break
- 15:45 A hands-on course focused on handheld LiDAR data collection and processing
- 18:00 Class ends / Poster Session
SATURDAY
- 9:00 A hands-on course focused on handheld LiDAR data collection and processing – cont.
- 10:30 Coffee break
- 10:45 OBIA: Point Clouds and Object primitives in theory and practice
- 12:30 Lunch
- 14:00 OBIA: Point Clouds and Object primitives in theory and practice
- 15:30 Lectures end / Adjourn the event
Expand each day for more information !
TOPICS AND PRESENTERS!
• Machine Learning and Remote Sensing for Wildfire and Spatial Risk Management (Dr. Ljiljana Seric, FESB, Croatia)
• Hyperspectral remote sensing for UAV-based monitoring (Prof. Donna Delparte, Idaho State University, USA)
• ARTMO Toolbox: A comprehensive tool for analyzing and processing hyperspectral data (Dr. Jochem Verrelst, University of Valencia, Spain).
• Drone/UAV/UAS: UAS LiDAR for Landslide Slope Monitoring and Change Detection using LAStools (rapidlasso GmbH) (Prof. Donna Delparte, Idaho State University, USA)
• LiDAR: A hands-on course focused on handheld LiDAR data collection and processing (Dr. Ivan Balenovic, Croatian Forest Research Institute, and Luka Zalovic, Geo-Centar, Croatia)
• Object Based Image Analysis (OBIA) with eCognition: EO for Mapping Shallow Water Marine Habitats / Point Clouds and Object primitives in theory and practice (Branimir Radun and Dr. Ivan Tomljenovic, Oikon Ltd., Croatia)
• SAR – Developments and status of current and upcoming ESA SAR missions (Dr. Thibault Taillade, ESA)
• SAR – Basics on SAR with a practical session on preprocessing of SAR image (Michele Claus, eurac research, Italy),
• Field Trip: Explore Krka National Park and the charming town of Skradin, with scheduled drone and LiDAR data collection activities (Nikola Zoric i Ivan Balenovic, Croatian Forest Research Institute, Croatia).
2025 – BRING YOUR POSTER!
Discuss your project and poster with remote sensing experts.
ABSTRACTS:
UAS LiDAR for Landslide Slope Monitoring and Change Detection
UAS LiDAR enables cost-effective temporal mapping of landslide slopes using high resolution datasets. In this workshop, students will learn the entire UAS workflow from mission planning and data processing to analysis and error estimation. Students will gain hands-on experience in analyzing UAS LiDAR data to detect change in slope stability. By using point cloud analysis, students will determine 3D slope changes and identify susceptible slopes.
A hands-on course focused on handheld LiDAR data collection and processing
Rapid progress in sensor miniaturization has led to the development of lightweight and highly mobile personal laser scanning (PLS) systems that can be carried or held by a person. The emergence and availability of next-generation PLS systems, with significantly improved features (e.g., scanning range and rate, measurement accuracy) of LiDAR sensors, have recently sparked more intensive research into their application in forest inventory, particularly for estimating key tree attributes.
This interactive workshop will begin with a general overview (presentation) of the state-of-the-art hand-held PLS technology, as well as an introduction to the main principles of data collection and processing. Following the introduction, students will participate in field data collection using a high-end PLS instrument (Faro Orbis) in a vegetated area. The workshop will then continue with a hands-on session where students will perform the typical steps of a PLS LiDAR data processing workflow on their own computers using the previously collected data and provided software.
First, students will ‘preprocess’ the collected data (e.g., converting raw data to .las/.laz format, point cloud registration, and colorization) using Faro SCENE software. In the next step, students will learn how to use LiDAR360 software for further processing (point cloud classification, normalization, segmentation, etc.) and extraction of key tree attributes (diameter at breast height, tree height, crown dimensions).
EO for Mapping Shallow Water Marine Habitats
This workshop will focus on the use of Earth Observation (EO) data in mapping shallow water marine habitats. Participants will gain insight into the importance of shallow water marine habitats, challenges associated with using optical satellite data for such applications, including issues such as the effects of the water column, turbidity, and spatial resolution limitations. The session will provide practical solutions to these challenges by demonstrating the fusion of different image processing approaches.
Through real-world examples, participants will explore how these methods are applied to classify and delineate benthic habitats of the Adriatic Sea, including seagrass meadows and other key ecological features. Hands-on activities using tools like eCognition will guide attendees in applying classification rules to the marine environment, integrating multiple data sources, and performing accuracy assessments. These exercises will equip participants with practical skills to address real-world challenges and ensure robust and transferable habitat mapping results.
The ARTMO toolbox for analyzing and processing of hyperspectral data
This tutorial will focus on the use of ARTMO’s (Automated Radiative Transfer Models Operator) radiative transfer models (RTMs), retrieval toolboxes and post-processing tools (https://artmotoolbox.com/) for the generation and interpretation of hyperspectral data. ARTMO brings together a diverse collection of leaf, canopy and atmosphere RTMs into a synchronised user-friendly GUI environment. Essential tools are provided to create all kinds of look-up tables (LUT). These LUTs can then subsequently be used for mapping applications from optical images. A LUT, or user-collected field data, can subsequently be inserted into three types of mapping toolboxes: (1) through parametric regression (e.g. vegetation indices), (2) nonparametric methods (e.g. machine learning methods), or (3) through LUT-based inversion strategies. In each of these toolboxes various optimization algorithms are provided so that the best-performing strategy can be applied for mapping applications. When coupled with an atmosphere RTM retrieval can take place directly from top-of-atmosphere radiance data.
Further, ARTMO’s RTM post-processing tools include: (1) global sensitivity analysis, (2) emulation, i.e. approximating RTMs through machine learning, and (3) synthetic scene generation.
The proposed tutorial will consist of a theoretical introduction and a practical session, where the following topics will be addressed:
- Basics of leaf, canopy and atmosphere RTMs: generation of RTM simulations
- Overview of retrieval methods: parametric, nonparametric, inversion and hybrid methods.
- Principles of emulation of hyperspectral data, and applications such as global sensitivity analysis and scene generation.
In the practical session we will learn to work with the ARTMO toolboxes. They provide practical solutions dealing with the abovementioned topics. Step-by-step tutorials, demonstration cases and demo data will be provided. No prior knowledge is needed, however for the practical Matlab in Windows is required. In case no Matlab is available, students will be asked to team up in small groups.
Developments and status of current and upcoming ESA SAR missions
The purpose of the presentation is to provide an overview of the European Space Agency (ESA) activities in the Earth Observation domain and more especially on the exploitation of Synthetic Aperture Radar data for Land applications. The presentation will focus on the developments and status of current and upcoming ESA SAR missions. Applications and results of various projects and initiative with Copernicus missions like Sentinel-1, the Earth Explorers BIOMASS and Harmony missions, and the upcoming satellites such as ROSE-L and Sentinel-1 Next Generation will also be discussed.
Machine Learning and Remote Sensing for Wildfire and Spatial Risk Management
This interactive workshop offers hands-on experience in integrating artificial intelligence (AI) and remote sensing for wildfire prediction, analysis, and broader spatial risk management. Participants will gain a solid foundation in satellite platforms, learning to interpret key satellite products such as hotspots, burned area mapping, and vegetation moisture indices, essential for assessing environmental risks. Through hands-on exercises, attendees will explore machine learning techniques for pixel and object classification, fuel classification, and moisture regression—vital for understanding fire dynamics and risk zones. Practical exercises will also include fire spread behavior reconstruction, allowing participants to gain critical insights into wildfire propagation patterns. With real-world case studies and interactive sessions, this workshop equips attendees with the technical skills to harness satellite data and AI for comprehensive management in dynamic and uncertain environments. These skills are invaluable not only for wildfire managers but also for professionals across sectors involved in spatial risk management, including disaster response, environmental monitoring, and infrastructure resilience planning. By the end of the workshop, participants will have the practical skills and insights needed to utilize AI and satellite data for proactive risk management in dynamic and uncertain environments.
Basics on SAR with a practical session on preprocessing of SAR image
Synthetic Aperture Radar (SAR) are active sensors that utilize electromagnetic waves in the microwave spectrum to analyze the geometric and dielectric properties of target objects. Thanks to their active nature and operating frequency, SAR systems are not dependent on sunlight and are nearly insensitive to weather conditions like cloud cover. This makes them invaluable for a wide range of applications. This lecture introduces the fundamental principles of SAR, including SAR interferometry, data processing techniques, and real-world applications. Additionally, participants will gain hands-on experience using open-source software and cloud-based platforms for satellite data analysis, enabling them to effectively process and interpret SAR imagery.
LiDAR & OBIA: Point Clouds and Object primitives in theory and practice
This session will provide an introduction to LiDAR technology, covering its fundamentals and the types of LiDAR platforms, such as airborne, terrestrial, and mobile systems, each tailored to specific applications in spatial data collection. It will then focus on the theoretical and practical aspects of using LiDAR data and Object-Based Image Analysis (OBIA) for object recognition and classification within spatial point clouds. Emphasis will be placed on segmentation and classification techniques that enable automated extraction of information, such as building outlines or tree canopy. Participants will explore tools and approaches for developing classification rules based on geometric and radiometric properties of data, as well as methods for assessing the transferability of developed models across different datasets.