| technologies-21 |
UAV for Autonomous Water Sampling |
Cable-suspended mechanism, multirotor UAV, sensor fusion |
Autonomous system |
Load cell, depth sensor, ultrasonic sensor, camera |
Not specified |
Not specified |
A motion control framework for autonomous water sampling and swing-free transportation of a multirotor UAV with a cable-suspended mechanism |
10.1002/rob.22182 |
883484 |
PathoCERT |
Autonomous water sampling using a UAV with a cable-suspended mechanism |
| technologies-22 |
PathoINVEST |
Digital Twin |
Software |
Various data sources (not specified) |
Not specified |
Not specified |
PathoINVEST: Pathogen Contamination Investigations During Emergencies |
10.4995/wdsa-ccwi2022.2022.14799 |
883484 |
PathoCERT |
Assists first responders and water authorities in investigating and responding to water contamination events |
| technologies-23 |
Land–Water Transition Zone Monitoring Tool |
Machine Learning |
Inundation mapping |
Spaceborne imagery |
Hydroperiod maps |
No |
Land–Water Transition Zone Monitoring in Support of Drinking Water Production |
10.3390/w15142596 |
101004157 |
WQeMS |
Monitoring changes in water level and dissolved substances in open surface water reservoirs. |
| technologies-24 |
Muddy Water Mapping Tool |
Machine Learning (Random Forest) |
Semantic segmentation |
Sentinel-2 imagery |
Muddy water maps |
No |
Towards a Paradigm Shift on Mapping Muddy Waters with Sentinel-2 Using Machine Learning |
10.3390/su151813441 |
101004157 |
WQeMS |
Mapping the presence, frequency, and spatial extent of muddy water in inland drinking water reservoirs. |
| technologies-25 |
Aquatic Vegetation Mapping Tool |
Machine Learning (Random Forest, Boosting Trees) |
Classification |
Sentinel-2 imagery |
Maps of emergent and floating aquatic vegetation |
No |
Machine Learning for Identifying Emergent and Floating Aquatic Vegetation from Space: A Case Study in the Dniester Delta, Ukraine |
10.1007/s42979-024-02873-7 |
101004157 |
WQeMS |
Mapping emergent and floating aquatic vegetation in freshwater ecosystems. |
| technologies-26 |
Underwater Aquatic Vegetation Mapping Tool |
Machine Learning (Logistic Regression, Random Forest, Segment Anything Model) |
Classification |
Airborne and spaceborne images |
Maps of underwater aquatic vegetation |
No |
Mapping underwater aquatic vegetation using foundation models with air- and space-borne images: the case of Polyphytos Lake |
10.3390/rs15164001 |
101004157 |
WQeMS |
Mapping underwater aquatic vegetation using air- and space-borne images. |
| technologies-27 |
Flood Detection Tool |
Graph Neural Networks |
Multimodal fusion |
Image, text, and time information from social media and other sources |
Flood maps |
No |
Flood-Related Multimedia Benchmark Evaluation: Challenges, Results and a Novel GNN Approach |
10.3390/s23073767 |
101004157 |
WQeMS |
Detecting flood events in real time using multimedia data. |
| technologies-28 |
Oil Spill Detection Tool |
Machine Learning |
Classification |
Multi-spectral satellite images |
Oil spill maps |
No |
Detection of oil spills in inland lake using multi-spectral satellite images |
10.5281/zenodo.6605875 |
101004157 |
WQeMS |
Detecting oil spills in inland lakes using multi-spectral satellite images. |
| technologies-29 |
WQeMS Platform |
Web-based platform |
Monitoring and analysis |
Various data sources (Sentinel data, in situ data, social media) |
Water quality data, maps, alerts |
No |
WQEMS platform for inland surface water bodies' monitoring: serving user communities and supporting expert analyses |
10.1117/12.2680817 |
101004157 |
WQeMS |
Provides an operational water quality emergency monitoring service. |
| technologies-30 |
NIRS-based contaminant detection algorithm |
Near Infrared Spectroscopy (NIRS), Aquaphotomics |
Algorithm |
NIRS spectra of contaminated water |
Not specified |
Not specified |
Not specified |
Not specified |
237819 |
AQUASENSE |
Identifies contaminants in water using NIRS data and aquaphotomics principles. |