PROGRAM:

  • 8:30   Registration
  • 9:00   Opening Session
  • 9:15    LiDAR images and processing
  • 10:30 Coffee break
  • 10:45 LiDAR images and processing
  • 12:30 Lunch
  • 14:00 Hands-on course to LiDAR processing with LAStools
  • 15:30  Coffee break
  • 15:45  Hands-on course to LiDAR processing with LAStools
  • 17:30  Icebreaker
  • 9:00   Hands-on course on Artificial Intelligence
  • 10:30 Coffee break
  • 10:45 Hands-on course on Artificial Intelligence
  • 12:30 Lunch
  • 14:00 Hands-on course on Artificial Intelligence
  • 15:30 Coffee break
  • 15:45 Hands-on course on Artificial Intelligence
  • 17:30 Class ends / Poster Session
  • 9:00  Basics on SAR with a practical session on preprocessing of SAR image
  • 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 Multi-interferometric SAR processing techniques applied to infrastructure monitoring
  • 18:00 Class ends / Poster Session
  • 9:00   The use of interactive tools for creating labeled datasets – lecture
  • 10:30  Coffee break
  • 10: 45 Departure from campus – Field trip to Sierra Nevada: The use of drones and sensors
  • 14:00  Stop at restaurant
  • 16:30  Return to Campus
  • 19:30  Granada @ night – Guided walking tour
  • ~21:00  Social dinner (0ptional)
  • 9:00 The use of predictive models for decision support from drone and satellite images. Google Earth Engine
  • 10:30 Coffee break
  • 10:45 The use of predictive models for decision support from drone and satellite images. Google Earth Engine
  • 12:30 Lunch
  • 14:00 Creating digital twins of agricultural scenarios from multisensory data
  • 15:30 Coffee break
  • 15:45 Hyperspectral data processing. Forest modeling
  • 17:00 GIS and multi-source data fusion and representation
  • 18:30 Class ends / Adjourn the event

TOPICS AND PRESENTERS!

LiDAR
-Jorge Delgado-García: Hands-on course to LiDAR processing with LAStools

Artificial Intelligence
-Luis Paulo Reis and Brígida Mónica Faria: Hands-on course on Artificial Intelligence

SAR
-Mattia Callegari: Basics on SAR with a practical session on preprocessing of SAR image
-Antonio Miguel Ruiz-Armenteros: Multi-interferometric SAR processing techniques applied to infrastructure monitoring

Optical imagery
-Juan Lorite and Domingo Alcaraz: Demonstration of the use of drones and sensors – Fieldwork trip to the Natural Park
-Juan Roberto Jiménez Pérez: Hyperspectral data processing. Forest modeling

Predictive models, virtual modeling, data representation
-María Isabel Ramos Galán and Ruth Córdoba Ortega: The use of predictive models for decision support from drone and satellite images. Google Earth Engine
-Lidia M. Ortega: Creating digital twins of agricultural scenarios from multisensory data
-David Jurado Rodríguez: The use of interactive tools for creating labeled datasets
-Antonio Garrido-Almonacid: GIS and multi-source data fusion and representation

2024 – BRING YOUR POSTER!

Discuss your project and poster with remote sensing experts.

ABSTRACTS:

Basics on SAR with a practical session on preprocessing of SAR image

Synthetic aperture radar (SAR) are active systems that exploit an electromagnetic wave in the microwave spectrum to characterize the geometric and dielectric characteristics of a target object. The active nature and the working frequency of the SAR systems render them independent from the sunlight and almost insensitive to the presence of clouds. Therefore, SAR is of paramount importance from an application point of view. This lecture provides the basic notions on Synthetic Aperture Radar, including an introduction on SAR interferometry, SAR data processing and examples of applications exploiting past and current SAR satellite missions.

  • SAR basics
  • SAR Interferometry
  • SAR processing
  • SAR missions

At the end of the SAR session, the 1-hour lecture focuses on 1) introductory material related to landslide susceptibility mapping / landslide spatial probability, and 2) ArcGIS SDMtoolbox.  The PS-InSAR techniques are used to monitor displacement and to validate and verify the landslide susceptibility mapping models”.

Multi-interferometric SAR processing techniques applied to infrastructure monitoring

Many engineering infrastructures worldwide are structurally deficient, posing potential risks to public safety. Monitoring and inspecting each structure individually is impractical due to the high costs and time involved. Synthetic Aperture Radar Interferometry (InSAR) is a cost-effective remote sensing technique for measuring small displacements of the Earth’s surface over large areas. Advanced InSAR time series algorithms, based on radar interferometry, enable the detection of vertical/horizontal displacements in infrastructure with high accuracy. This technology, utilizing SAR images from satellites like Sentinel-1, TerraSAR-X, COSMO-SkyMed, and PAZ, allows for efficient monitoring of engineering infrastructures such as dams, bridges, ports, and railways over extensive areas. InSAR’s ability to operate in darkness and any weather conditions further reduces monitoring costs. Recent advancements in high-resolution radar imagery and multi-interferogram techniques have significantly improved the effectiveness of InSAR. This lecture will address the applicability of using spaceborne SAR sensors for infrastructure monitoring and present several cases of study. The following topics will be addressed:

  • Advanced InSAR techniques for getting deformation time series.
  • Practical examples of ground deformation and infrastructure monitoring.

Creating digital twins of agricultural scenarios from multisensory data

In recent years there has been a breakthrough in the development of new sensors and platforms capable of optimizing traditional techniques of acquisition, processing and analysis of environmental variables. The fields of application of remote sensing and informatics are various, including Precision Agriculture. Examples of these advances are those sensors mounted on satellites or unmanned aerial platforms (UAS) (some examples are multispectral, hyperspectral, thermographic, RGB and LiDAR). The huge amount of heterogeneous information obtained must follow the processes of fusion, preparation, processing and analysis in order to provide farmers and technicians with the necessary decision-making tools. In this framework, Digital Twins (DT) are an excellent solution as the systems that are able to provide a virtual representation of the real world while constantly checking its behavior, analyzing it and predicting its behavior in the future. Definitely, DT is conceived as the comprehensive tool for overseeing crop lifecycle, tailored to cater to the specific needs of various user profiles within the system: farmers, technicians and companies in the sector.

Hands-on Course to LiDAR processing with LAStools

This course will include both theoretical and hands-on lectures on the LiDAR processing tools, which are widely known for their blazing speeds and high productivity. The software combines robust algorithms with efficient I/O and clever memory management to achieve high throughput for data sets containing billions of points. The LAStools software suite has deep market penetration and is heavily used in the commercial sector, government agencies, research labs, and educational institutions alike — filtering, tiling, rasterizing, triangulating, converting, clipping, quality-checking, etc.

Hands-on course on Artificial Intelligence

This theoretical and hands-on lecture will include Introduction to Artificial Intelligence (AI) and Genarative AI / Machine Learning. The concepts of traditional programming and Machine Learning (ML) will be compared in a hands-on session using Jupyter Notebook and JupyterLab among other applications. Classification vs. regression in ML will be explained in detail. The emphasise will be on Neural Networks and Deep Learning. Data mining and mining tasks will be also discussed.

The use of interactive tools for creating labeled datasets 

In this presentation, Dr. Rodriguez will emphasise the significance of interactive tools in the creation of labeled datasets, specifically concentrating on multi-sensor data. The incorporation of Geographic Information Systems (GIS) and data fusion methods from multiple sources will be explored to attain a deeper comprehension of our environment. The discussion will cover techniques and practical applications of these technologies across various domains, offering insights into how they can facilitate a more thorough understanding and interaction with our surroundings.

The use of predictive models for decission support from drone and satellite images – Google Earth Engine

The usefulness of predictive systems as a management and decision-making tool is well known. In any sector, the availability of information in advance is an extra tool for correct decision-making. In this sense, the goodness of any predictive model lies in the quality and, above all, the timeliness of the data. Any predictive model requires a training phase based on the data set. A correct selection of predictor variables, an adequate acquisition and availability of data of these variables and, finally, a wide temporal range in these data, are the keys to generating an optimal predictive model. This paper discusses the workflow for generating predictive models in the field of agricultural areas using predictor variables from sensors mounted on drones and satellites. It also describes the contribution of tools such as Google Earth Engine to obtain predictive data.

GIS and multi-source data fusion and representation

Dr. Garrido-Almonacid will teach the use of GIS tools for the creation of synthesis indicators and mapping to geographic data. We will start with the connection to OGC services, the downloading of spatial and thematic information, the application of table joining processes, the elaboration of indicators and the processes of cartography creation, especially the design of templates and their graphic design. We will use for the development of the practices the free software QGIS.

Hyperspectral data processing – Forest modeling

Modelling the real world and simulating its behaviour has been a traditional goal of computer sciences in collaboration with other research fields. In the current digital era we have at our disposal many sensors and techniques to capture and process a huge amount of data from real environments. The hyperspectral data allows us to capture information from a considerable number of wavelengths in the visible and the near infrared range. Together with geometric information (cloud of points) from LiDAR sensors it is possible to compose a precise replica of the represented environment. In this lecture, the main concepts about this kind of sensors and the acquired data are presented as well as the process to get a complete 3D model of the corresponding scenario.