Land cover plays an integral role in urban management as a source of information to
support authorities' decision making. Recently, computer vision methods, machine learning …

Land cover is a powerful tool in urban management as a source of information to support
authorities' decision making. In this paper, land cover of Eastern Economic Corridor cities in …

Abstract Recent Convolutional Neural Network (CNN) has shown great potential in image
classification, segmentation and object detection. Land cover takes advantage of CNN …

Regional land use planning and monitoring remain an issue in many developing countries.
Efficient solution for both tasks depended on remote sensing technology to capture and …

Our aim is to propose a new deep learning framework approach which uses an ensemble of
convolutional neural network (CNN) for land use-land cover mapping. Every CNN layer was …

In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to
estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can …

One of the potential and necessary remote sensing topics is land cover classification. Using
artificial intelligence algorithms in those type of applications decreases the required time of …

Recently, deep learning (DL) techniques including Convolutional neural network (CNN),
Recurrent neural network (RNN), and Recurrent-Convolutional neural network (R-CNN) …

Land use and land cover are two important variables in remote sensing. Commonly, the
information of land use is stored in geospatial databases. In order to update such databases …

Land cover and its dynamic information is the basis for characterizing surface conditions,
supporting land resource management and optimization, and assessing the impacts of …