Machine-to-Machine (M2M) collaboration opens new opportunities where systems can collaborate without any human intervention and solve engineering problems efficiently and effectively. M2M is widely used for various ap...Machine-to-Machine (M2M) collaboration opens new opportunities where systems can collaborate without any human intervention and solve engineering problems efficiently and effectively. M2M is widely used for various application areas. Through this reported project authors developed a M2M system where a drone and two ground vehicles collaborate through a base station to implement a system that can be utilized for an indoor search and rescue operation. The model training for drone flight paths achieves almost 100% accuracy. It was also observed that the accuracy of the model increased with more training samples. Both the drone flight path and ground vehicle navigation are controlled from the base station. Machine learning is utilized for modelling of drone’s flight path as well as for ground vehicle navigation through obstacles. The developed system was implemented on a field trial within a corridor of a building, and it was demonstrated successfully.展开更多
Undeniably,Deep Learning(DL)has rapidly eroded traditional machine learning in Remote Sensing(RS)and geoscience domains with applications such as scene understanding,material identification,extreme weather detection,o...Undeniably,Deep Learning(DL)has rapidly eroded traditional machine learning in Remote Sensing(RS)and geoscience domains with applications such as scene understanding,material identification,extreme weather detection,oil spill identification,among many others.Traditional machine learning algorithms are given less and less attention in the era of big data.Recently,a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision.The number of relevant articles has nearly doubled every year since 2015.Advances in remote sensing technology,as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale,have opened up the possibilities for a wide range of modern applications.However,there are some challenges related to the availability of annotated data,the complex nature of data,and model parameterization,which strongly impact performance.In this article,a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures(vintage Convolutional Neural Network,CNN;Fully Convolutional Networks,FCN;encoder-decoder,recurrent networks;attention models,and generative adversarial models).The characteristics,capabilities,and limitations of current DL models were examined,and potential research directions were discussed.展开更多
文摘Machine-to-Machine (M2M) collaboration opens new opportunities where systems can collaborate without any human intervention and solve engineering problems efficiently and effectively. M2M is widely used for various application areas. Through this reported project authors developed a M2M system where a drone and two ground vehicles collaborate through a base station to implement a system that can be utilized for an indoor search and rescue operation. The model training for drone flight paths achieves almost 100% accuracy. It was also observed that the accuracy of the model increased with more training samples. Both the drone flight path and ground vehicle navigation are controlled from the base station. Machine learning is utilized for modelling of drone’s flight path as well as for ground vehicle navigation through obstacles. The developed system was implemented on a field trial within a corridor of a building, and it was demonstrated successfully.
文摘Undeniably,Deep Learning(DL)has rapidly eroded traditional machine learning in Remote Sensing(RS)and geoscience domains with applications such as scene understanding,material identification,extreme weather detection,oil spill identification,among many others.Traditional machine learning algorithms are given less and less attention in the era of big data.Recently,a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision.The number of relevant articles has nearly doubled every year since 2015.Advances in remote sensing technology,as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale,have opened up the possibilities for a wide range of modern applications.However,there are some challenges related to the availability of annotated data,the complex nature of data,and model parameterization,which strongly impact performance.In this article,a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures(vintage Convolutional Neural Network,CNN;Fully Convolutional Networks,FCN;encoder-decoder,recurrent networks;attention models,and generative adversarial models).The characteristics,capabilities,and limitations of current DL models were examined,and potential research directions were discussed.
