An Internet-of-Things and AI-Powered Framework for Long-Term Flood Risk Evaluation

I Ahmed, M Ahmad, G Jeon… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Integrating Internet of Things (IoT) and artificial intelligence (AI) techniques have found
widespread application in various fields, including smart cities, agriculture, and …

A survey on applications of unmanned aerial vehicles using machine learning

K Teixeira, G Miguel, HS Silva, F Madeiro - IEEE Access, 2023 - ieeexplore.ieee.org
Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including
health, transport, telecommunications and safe and rescue operations. Their adoption can …

[HTML][HTML] Drone-based bathymetry modeling for mountainous shallow rivers in Taiwan using machine learning

CH Lee, LW Liu, YM Wang, JM Leu, CL Chen - Remote Sensing, 2022 - mdpi.com
The river cross-section elevation data are an essential parameter for river engineering.
However, due to the difficulty of mountainous river cross-section surveys, the existing …

TinyEmergencyNet: a hardware-friendly ultra-lightweight deep learning model for aerial scene image classification

OM Mogaka, R Zewail, K Inoue, MS Sayed - Journal of Real-Time Image …, 2024 - Springer
In the context of emergency response applications, real-time situational awareness is vital.
Unmanned aerial vehicles (UAVs) with imagers have emerged as crucial tools for providing …

[HTML][HTML] Using Explainable Artificial Intelligence (XAI) to Predict the Influence of Weather on the Thermal Soaring Capabilities of Sailplanes for Smart City Applications

M Schnieder - Smart Cities, 2024 - mdpi.com
Background: Drones, also known as unmanned aerial vehicles, could potentially be a key
part of future smart cities by aiding traffic management, infrastructure inspection and maybe …

Flood detection based on unmanned aerial vehicle system and deep learning

K Yang, S Zhang, X Yang, N Wu - Complexity, 2022 - Wiley Online Library
Floods are one of the main natural disasters, which cause huge damage to property,
infrastructure, and economic losses every year. There is a need to develop an approach that …

[HTML][HTML] Hybrid deep learning model with enhanced sunflower optimization for flood and earthquake detection

PK ES, VN Thatha, G Mamidisetti, SV Mantena… - Heliyon, 2023 - cell.com
Natural catastrophes may strike anywhere at any moment and cause widespread
destruction. Most people do not have the necessary catastrophe preparedness knowledge …

Extracting built-up areas from spectro-textural information using machine learning

A Maqsoom, B Aslam, A Yousafzai, F Ullah, S Ullah… - Soft Computing, 2022 - Springer
Extraction of built-up area (BUA) is essential for proper city planning and management. It
enables the concerned authorities to formulate better city development policies and manage …

A novel AI-based model for real-time flooding image recognition using super-resolution generative adversarial network

YF Zeng, MJ Chang, GF Lin - Journal of Hydrology, 2024 - Elsevier
Intensified climate change in recent years has had a global impact, leading to increased
precipitation events of short duration and high intensity. This phenomenon poses a severe …

MFEMANet: an effective disaster image classification approach for practical risk assessment

P Bhadra, A Balabantaray, AK Pasayat - Machine Vision and Applications, 2023 - Springer
An emergency risk assessment by collecting disaster-affected images via unmanned aerial
vehicles is the current norm. Reasonable rescue planning and resource allocation depend …