This study presents a rapid flood impact assessment framework using Sentinel-1 SAR data and Google Earth Engine, enhancing disaster response capabilities.
SAR Flood Impact Assessment for Agriculture Read More »
A new machine learning framework enhances urban flood forecasting by combining high-fidelity models with sparse sensor data.
Urban Flood Forecasting Read More »
Researchers optimize LSTM models for street-scale flood prediction in Norfolk, Virginia, exploring data grouping and model scalability.
LSTM Models for Flood Prediction Read More »
Researchers have developed a fast conceptual model to predict flood arrival times after dike breaches, enhancing disaster management.
Predicting Flood Arrival Times Post-Dike Breach Read More »
Driving automation taxonomies are under scrutiny for lacking clarity and adaptability to new technologies.
Clarity in SAE J3016 Automation Taxonomy Read More »
This study examines wildfire ignitions in mainland Portugal, focusing on municipal-scale analysis and spatial modeling to enhance fire management.
Wildfire Management in Portugal Read More »
Researchers have developed a LiDAR SLAM-based geo-localization framework that operates independently of GNSS, enhancing safety and reliability in maritime operations.
LiDAR SLAM for Autonomous Maritime Navigation Read More »
Researchers from Delft University of Technology have developed a calibration strategy for Discrete Element Method (DEM) modeling, enhancing simulation accuracy in blast furnace mixture charging under high-velocity conditions.
DEM Calibration Boosts Blast Furnace Efficiency Read More »
Satellite Communication (SatCom) offers a lifeline for internet connectivity in regions where traditional infrastructure is impractical.
Optimizing Satellite Communication Read More »
This study examines how forest vegetation density influences GNSS navigation accuracy for off-road vehicles, improving solutions for forestry and autonomous vehicles.
GNSS Navigation Accuracy in Dense Forests Read More »
