Developing Scalable Monitoring System for Acid Mine Drainage Detection

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Developing Scalable Monitoring System for Acid Mine Drainage Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Developing Scalable Monitoring System for Acid Mine Drainage Detection

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10505 - Geology

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Geoscience and Remote Sensing Symposium (IGARSS)

ISBN

979-8-3503-6031-8

ISSN

2153-7003

e-ISSN

—

Počet stran výsledku

5

Strana od-do

3404-3408

Název nakladatele

IEEE

Místo vydání

Grece

Místo konání akce

Athens, Greece

Datum konání akce

8. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001316158503176