Change detection in urban areas can be helpful for urban resource management and smart city planning. The effects of human activities on the environment and ground have gained momentum over the past decades, causing remote sensing data sources analysis (such as satellite images) to become an option for swift change detection in the environment and urban areas. We proposed a semi-transfer learning method of EfficientNetV2 T-Unet (EffV2 T-Unet) that combines the effectiveness of composite scaled EfficientNetV2 T as the first path or encoder for feature extraction and convolutional layers of Unet as the second path or decoder for reconstructing the binary change map. In the encoder path, we use EfficientNetV2 T, which was trained by the ImageNet dataset. In this research, we employ two datasets to evaluate the performance of our proposed method for binary change detection. The first dataset is Sentinel-2 satellite images which were captured in 2017 and 2021 in urban areas of northern Iran. The second one is the Onera Satellite Change Detection dataset (OSCD). The performance of the proposed method is compared with YoloX-Unet families, ResNest-Unet families, and other well-known methods. The results demonstrated our proposed method’s effectiveness compared to other methods. The final change map reached an overall accuracy of 97.66%.
Detecting changes in urban areas have always been an important element of urban planning and resource management. With the widening impacts of human activities on the ground terrain and landscapes, in recent decades the analysis of remote sensing data, including satellite images, has become a method of choice for rapid tracking of changes in the ground surface over wide geographical areas. However, this approach requires automated and semi-automated methods with the ability to detect changes in remote sensing data. In this study, Sentinel-2 satellite images taken in 2017 and 2021 were used for binary change detection in urban areas of northern Iran. This change detection was performed with a supervised deep learning method, which was used to generate a detailed change map of the area. Considering the generally good performance of convolutional neural networks in change detection applications, this study used an improved U-net with a weighted binary cross-entropy loss function, this function considers unequal weights for positive and negative targets so it performs better in detecting changes in complex urban areas especially small changes rather than binary cross-entropy. In addition to Sentinel-2 satellite images of northern Iran, Onera Satellite Change Detection (OSCD) dataset was also used to evaluate our proposed method. Experimental results showed the good performance of the method on both datasets, with the accuracy and F1score of final change maps reaching above 97%.
Abstract. This paper presents a deep learning approach for swift detection of COVID-19 in chest CT scan images in order to facilitate treatment planning and reduce the burden on hospital resources and staff workload. The detection procedure starts with a pre-processing step, which involves noise removal and resizing, and the pre-processed images are fed to VGG16, which is a powerful deep learning network for image classification applications. All algorithms have been implemented in Python and the deep learning network has been implemented in Tesorflow using the Keras library. Using VGG16, we have achieved 99% and 92% accuracy for the training and test data, respectively. Considering the accuracy of the method, it can be used for swift clinical detection of COVID-19, which could be of useful and magnificent help to treatment personnel. Also, this method is really helpful for detecting patient and starting treatment as soon as possible and reduces the cost of treatments.
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