# Special Issue: Deep Learning in Remote Sensing Applications (Editorial)
###### Abstract  
Satellite remote sensing applications are data-intensive, and monitoring activities are the most common use of remote sensing data. This means that remote sensing is a data-intensive context in which the time series of data are widely available. Data science and, in particular, machine learning methods and algorithms are natural candidates for improving the performance of existing remote sensing applications and suggesting new applications. Sensors capture images across the electromagnetic spectrum, ranging from ultraviolet to infrared images. These images are taken in a variety of modes, from single-band to hyperspectral images. Statistical and signal processing methods, such as moving averages, linear regression, and principal component analysis, have been used to analyze these images since the early days of remote sensing. However, recently, new techniques originating from the field of data science, such as neural networks, support vector machines, and decision trees, have received a great deal of attention as image processing tools. This Special Issue comprises a collection of eleven papers on algorithmic innovations, data analysis, and hardware architectures involving machine learning methods and remote sensing applications. Several remote sensing applications, including agricultural production, monitoring of mudslides, solid waste monitoring, sea ice image segmentation, and building extraction are considered, exploring applications using images taken from satellites and UAVs (Unmanned Aerial Vehicles). Several methods are used to study these images, with neural networks and, in particular, convolutional neural networks being the most prevalent tools. In fact, the YOLO (You Only Look Once) algorithm, which is mentioned in several of the Special Issue papers, is based on convolutional neural networks. This Special Issue consists of eleven high-quality papers which explore the use of artificial intelligence tools to interpret remote sensed data. Please see below for a brief commentary on each paper. [PERSON] et al. 's paper, \"_Domain--Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification_\", is concerned with Hyperspectral Image (HSI) classification. Labeled samples of hyperspectral data usually have limited availability due to the cost and practical difficulties involved. Consequentially, in this context, the use of standard deep learning methods is challenging due to the lack of training data [1, 2, 3]. Naturally, the idea of using synthetic data to train deep learning tools has arisen. The aforementioned paper suggests using Few-Shot Contrastive Learning (FSCL), a generative AI-based technique developed based on the well-known Generative Adversarial Networks (GANs). The FSCL technique is adapted to the context of remote sensing with the help of traditional image analysis tools, such as domain adaptation and feature extraction. The methodology developed has been tested in numerical experiments, with very promising results. [PERSON] et al. present \"Enhancing a You Only Look One-Plated Detector via Auxiliary Textual Coding for Multi-Scale Rotating Remote Sensing Object in TransportationMonitoring Applications\", in which they outline a new detection method for remotely sensed multiscale objects with a rotating status, possibly in the presence of occlusions. Their proposed methodology is based on YOLO (You Only Look Once) and or a Swin Transformer. YOLO is a celebrated real-time object detection algorithm, founded on single-shot data and CNN (Convolutional Neural Network). It is characterized by the fact that the processing of the image and the detection of the objects contained in it are performed simultaneously. Swin Transformers have been introduced in natural language processing and are used here for the purpose of feature extraction. Previous use of Swin Transformer in the context of image analysis has been described in [4]. Extensive experiments on publicly available datasets have achieved excellent results. [PERSON] et al.