Developing Scalable Monitoring System for Acid Mine Drainage Detection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00025798%3A_____%2F24%3A10169459" target="_blank" >RIV/00025798:_____/24:10169459 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/IGARSS53475.2024.10641851" target="_blank" >https://doi.org/10.1109/IGARSS53475.2024.10641851</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IGARSS53475.2024.10641851" target="_blank" >10.1109/IGARSS53475.2024.10641851</a>

Result language
angličtina
Original language name
Developing Scalable Monitoring System for Acid Mine Drainage Detection
Original language description
This study focuses on advancing the development of effective monitoring systems Acid Mine Drainage (AMD) by leveraging Machine Learning techniques on optical multi and hyperspectral data. More specifically, the research investigates the utilization of hyperspectral data (PIKA L) acquired through Unmanned Aerial Vehicles (UAV) and multi-temporal data sets from the Sentinel-2 satellite. The results of ML classifications have been validated using ground truth, and it has been determined that the Radial Basis Function Support Vector Machine (RBF SVM) and Random Forest (RF) performs better than other tested ML approaches demonstrating especially effectiveness in handling high-dimensional spaces, which is crucial for hyperspectral data. Future work will focus on testing machine learning techniques on extended multi-temporal data sets, expanding the training and validation data sets to validate results across all scales and evaluating the transferability of the model to other geographical locations.
Czech name
—
Czech description
—

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
International Geoscience and Remote Sensing Symposium (IGARSS)
ISBN
979-8-3503-6031-8
ISSN
2153-7003
e-ISSN
—
Number of pages
5
Pages from-to
3404-3408
Publisher name
IEEE
Place of publication
Grece
Event location
Athens, Greece
Event date
Jul 8, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001316158503176

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