Landslide susceptibility map** using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region
Landslides are one of the most widespread natural disasters and pose severe threats to
people, properties, and the environment in many areas. Landslide susceptibility map** …
people, properties, and the environment in many areas. Landslide susceptibility map** …
Convolutional neural networks for global human settlements map** from Sentinel-2 satellite imagery
Spatially consistent and up-to-date maps of human settlements are crucial for addressing
policies related to urbanization and sustainability, especially in the era of an increasingly …
policies related to urbanization and sustainability, especially in the era of an increasingly …
Federated Learning for Urban Sensing Systems: A Comprehensive Survey on Attacks, Defences, Incentive Mechanisms, and Applications
In recent years, advancements in Artificial Intelligence (AI), the Internet of Things (IoT) and
wireless technologies have propelled the evolution of smart cities. Urban sensing systems …
wireless technologies have propelled the evolution of smart cities. Urban sensing systems …
Instance segmentation for large, multi-channel remote sensing imagery using mask-RCNN and a mosaicking approach
Instance segmentation is the state-of-the-art in object detection, and there are numerous
applications in remote sensing data where these algorithms can produce significant results …
applications in remote sensing data where these algorithms can produce significant results …
Classification of high-spatial-resolution remote sensing scenes method using transfer learning and deep convolutional neural network
The deep convolutional neural network (DeCNN) is considered one of promising techniques
for classifying the high-spatial-resolution remote sensing (HSRRS) scenes, due to its …
for classifying the high-spatial-resolution remote sensing (HSRRS) scenes, due to its …
Deep learning-based signal modulation identification in OFDM systems
Signal modulation identification (SMI) plays a very important role in orthogonal frequency-
division multiplexing (OFDM) systems. Currently, SMI methods are often implemented via …
division multiplexing (OFDM) systems. Currently, SMI methods are often implemented via …
The characteristic and transformation of 3D urban morphology in three Chinese mega-cities
Urban morphology exerts an important influence on human settlements and serves to
promote sustainable development. However, few studies have focused on the three …
promote sustainable development. However, few studies have focused on the three …
Automatic modulation classification using compressive convolutional neural network
The deep convolutional neural network has strong representative ability, which can learn
latent information repeatedly from signal samples and improve the accuracy of automatic …
latent information repeatedly from signal samples and improve the accuracy of automatic …
Application of convolutional neural networks with object-based image analysis for land cover and land use map** in coastal areas: A case study in Ain Témouchent …
N Zaabar, S Niculescu… - IEEE Journal of Selected …, 2022 - ieeexplore.ieee.org
Land use and land cover (LULC) information is a fundamental component of environmental
research relating to urban planning, agricultural sustainability, and natural hazards …
research relating to urban planning, agricultural sustainability, and natural hazards …
Identification of active attacks in Internet of Things: Joint model-and data-driven automatic modulation classification approach
The Internet of Things (IoT) pervades every aspect of our daily lives and industrial
productions since billions of interconnected devices are deployed everywhere of the globe …
productions since billions of interconnected devices are deployed everywhere of the globe …