# Special Issue: Deep Learning in Remote Sensing Applications (Editorial)
###### Abstract
wrote \"MSACN: A Cloud Extraction Method from Satellite Image Using Multiscale Soft Attention Convolutional Neural Network\". In satellite remote sensing, the presence of clouds in the atmosphere may occlude access to ground truth information. This is certainly the case when optical sensors are used. In light of this, the detection and extraction of cloud in images is an important goal. The paper presents MSACN, a sophisticated software tool which combines a CNN and spatial information to obtain very good results in a series of numerical experiments on publicly available data. Note that the results obtained with MSACN are compared with those obtained with alternative methods and that this comparison confirms the quality of the work presented. For a comprehensive review of this kind of technique, see [5]. [PERSON] et al., the authors of \"_Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images_\", explore the detection of mudslides from remotely sensed data using deep learning. In particular, several versions of YOLO exist and are used in many different circumstances [6]. The paper primarily takes the form of an experimental study based on the processing of real data. A fair comparison of the results obtained is possible only after carefully reprocessing the data. The YOLOX version of the YOLO algorithm emerges as the recommended choice between the algorithms considered. [PERSON] et al. present \"_A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery_\". The ongoing development of urban areas results in a great deal of solid waste production, and using UAV (Unmanned Aerial Vehicle) images to monitor this waste is an interesting possibility that is worthy of investigation [7]. The paper combines deep learning with traditional image processing tools, such as image fusion, to pursue its goals. The study area considered for testing the method developed in this work is a specific location in the Nanhu District of Jiaxing City in Zhejiang Province, China. However, the results obtained are not expected to be associated with this specific study area. In fact, it should be possible to obtain similar results everywhere when suitable data are available. [PERSON] et al. wrote \"_A Rotating Object Detector with Convolutional Dynamic Adaptive Matching_\". The usual sliding along a coordinate direction during CNN processing is a source of trouble when rotating object detection is considered. Note that aerial targets can be difficult to detect [8] and that rotating targets may be relevant in both military and civil situations. The paper attempts to eliminate these difficulties by implementing a kind of \"dynamic convolution\" combined with a spatial feature detection tool. The resulting detector is stabilized with several ad hoc artifacts. The developed tool is calibrated and tested on publicly available data. These experiments show the quality of the results obtained and the balance between accuracy and computational complexity achieved. [PERSON] et al. present \"_An Enhanced Dual Stream Network Using Multi Source Remote Sensing Imagery for Water Body Segmentation_\", in which the problem of accurate surface water mapping is considered [9]. It is easy to imagine practical situations in which this issue is relevant. When many different shapes and scales of water surfaces are present in the same remotely sensed image, automatically extracting them from the image can be challenging. The paper combines a Swin Transformer with a CNN to produce a tool that can resolve this issue. After its implementation, the tool is tested on real data relating to water surface variations recorded during a period of several years in Mongolia, with good results. Note that there is no alternative to the use of remote sensing in these kinds of studies. [PERSON] et al. wrote \"_Enhanced Atrous Extractor and Self-Dynamic Gate Network for Superpixel Segmentation_\". A superpixel is a group of pixels with similar properties.