Urbanization Detection Using LiDAR-Based Remote Sensing Images of Azad Kashmir Using Novel 3D CNNs

Abstract

An important measurable indicator of urbanization and its environmental implications has been identified as the urban impervious surface. It presents a strategy based on three-dimensional convolutional neural networks (3D CNNs) for extracting urbanization from the LiDAR datasets using deep learning technology. Various 3D CNN parameters are tested to see how they affect impervious surface extraction. For urban impervious surface delineation, this study investigates the synergistic integration of multiple remote sensing datasets of Azad Kashmir, State of Pakistan, to alleviate the restrictions imposed by single sensor data. Overall accuracy was greater than 95% and overall kappa value was greater than 90% in our suggested 3D CNN approach, which shows tremendous promise for impervious surface extraction. Because it uses multiscale convolutional processes to combine spatial and spectral information and texture and feature maps, we discovered that our proposed 3D CNN approach makes better use of urbanization than the commonly utilized pixel-based support vector machine classifier. In the fast-growing big data era, image analysis presents significant obstacles, yet our proposed 3D CNNs will effectively extract more urban impervious surfaces.

Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

“…The mitigation method presented for IT networks does not suit the IoT atmosphere, and some Machine Learning (ML) techniques were advanced for detecting assaults based on IoT traffic paradigms [5,6]. ML techniques will be appropriate since they can be implemented in several applications like anomaly detection (AD), data classification, and clustering [7].…”

Multi-Dataset Hyper-CNN for Hyperspectral Image Segmentation of Remote Sensing Images

“…Hyperspectral remote sensing has matured into a trustworthy instrument for Earth observations in recent years [1]. Because HSIs can collect so much information in both the spectral and spatial domains, they have found use in many different fields [2]. These fields include agriculture, geology, food science, and even military target reconnaissance.…”

“…The classification of hyperspectral images will have profound effects on the aforementioned areas of study. Improved hyperspectral imaging methods have made high-resolution HSIs available to the public, making it simpler for researchers to push the state of the art in HSI segmentation forward [2][3][4][5].…”

“…Several methods for recognizing and segmenting hyperspectral images have implemented standard approaches from the field of machine learning [2]. Several methods, including kernelized support vector machines (k-SVMs), Markov random fields (MRFs), sparse representation (SR), morphological transformations (MTs), and composite kernels or Processes 2023, 11, 435 2 of 18 spatial-spectral kernels [2,6,7], can be used to classify or segment images by combining the spectral and spatial information carried by the images. You can find tools to help with the categorization and division of datasets.…”

Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism

“…This method can solve the problem with the data better than the pixel-based support vector machine classifier. When it comes to the impact of food security on local and global economies, Mazhar ( Hameed et al., 2022b ) applied the sequential model in deep learning to classify the outer layer air particles through the analysis and characteristics of objects and fusion. Compared with the existing deep learning method of surface landscape, the accuracy rate reaches 98%.…”