This session aims to present recent advances in the analysis of environmental and soil contaminations using Applied Geophysics, Remote Sensing and Artificial Intelligence.
Characterizing and understanding the surface and subsurface is a challenge for many scientific areas.
Applied Geophysics investigates underground using a variety of non-invasive, and non-destructive techniques such as ground-penetrating radar, magnetics, electrical resistivity tomography, electromagnetic induction, and seismics. Remote Sensing uses methods such as photogrammetry, LIDAR, GNSS, and satellite hyperspectral data to determine physical properties at a distance. Some remote sensing technologies can also provide information from the subsurface or interior of structures. Artificial Intelligence, namely Machine Learning, can be a useful tool to manage information using as input data provided by different methods, allowing the calculation of new contamination maps, that can help in the analysis of contamination areas.
Knowledge in these fields can be applied to a variety of research topics, in addition to laboratorial chemical analysis procedures, namely, to evaluate environmental pollutants (e.g. potentially toxic metals), and contributing to increasing knowledge about contaminated areas. When combined with other methods, they enable the development of integrated models of environmental management of contaminated areas, allowing the development of environmental risk maps, and contributing to the reduction of sampling and operational costs, as well as the reduction of assessment times in the management of contaminated areas.
The potential for replicability of this approaches is high, and can be applied in mines, landfills, industry and intensive agriculture.
This session will collect the contributions from Applied Geophysics, Remote Sensing, and Artificial Intelligence on the following topics:
- Environmental studies: characterization of the soil contamination by potentially toxic metals.
- Innovations in data acquisition, and processing of Geophysical, Remote Sensing and AI methods.
Characterizing and understanding the surface and subsurface is a challenge for many scientific areas.
Applied Geophysics investigates underground using a variety of non-invasive, and non-destructive techniques such as ground-penetrating radar, magnetics, electrical resistivity tomography, electromagnetic induction, and seismics. Remote Sensing uses methods such as photogrammetry, LIDAR, GNSS, and satellite hyperspectral data to determine physical properties at a distance. Some remote sensing technologies can also provide information from the subsurface or interior of structures. Artificial Intelligence, namely Machine Learning, can be a useful tool to manage information using as input data provided by different methods, allowing the calculation of new contamination maps, that can help in the analysis of contamination areas.
Knowledge in these fields can be applied to a variety of research topics, in addition to laboratorial chemical analysis procedures, namely, to evaluate environmental pollutants (e.g. potentially toxic metals), and contributing to increasing knowledge about contaminated areas. When combined with other methods, they enable the development of integrated models of environmental management of contaminated areas, allowing the development of environmental risk maps, and contributing to the reduction of sampling and operational costs, as well as the reduction of assessment times in the management of contaminated areas.
The potential for replicability of this approaches is high, and can be applied in mines, landfills, industry and intensive agriculture.
This session will collect the contributions from Applied Geophysics, Remote Sensing, and Artificial Intelligence on the following topics:
- Environmental studies: characterization of the soil contamination by potentially toxic metals.
- Innovations in data acquisition, and processing of Geophysical, Remote Sensing and AI methods.