## 2 Related Work  
Satellite remote sensing is an effective way to monitor vast interesting areas. Unfortunately, it is limited by the satellite's revisit and the high cost of data acquisition [14]. [PERSON] et al. [15] explored five approaches based on improvements of U-Net and Mask-R convolutional neural network models to detect the positions of buildings on satellite images. The work was focused on dealing with the object detection issue in the image recognition research field. However, drones have become a novel alternative method for satellite remote sensing. Recently, researchers have paid more attention to aerial maps for drone remote-sensing applications [16]. For example, [PERSON] et al. [17] designed a unified framework that includes a scale-adaptive strategy as well as circular flow anchor selection and feature extraction to detect and count vehicles from drone images simultaneously. Due to the targets in drone-based scenes being small and dense, this method extracts features and detects small objects. [PERSON] et al. [18] developed a scene detection and estimation method using an unsupervised classification method upon completing the flight mission. The drone navigation was based on GPS information, and target recognition was applied using the histogram of oriented gradient feature extraction and linear support vector machine (SVM) algorithm. However, both the image capturing and process execution were on the ground station. [PERSON] et al. [19] proposed real-time object detection and tracking using a CNN with a single-shot detector architecture for real-time implementation at parrot AR drone 2. However, a major challenge for real-time implementation is reducing the computational time. They divided the computational load onto the CPU and GPU, resulting in a 10-ms computational time. [PERSON] et al. [20] applied a CNN-based Hough transform to automatically detect crop rows in images taken from a drone. For autonomous guidance of drones in agriculture monitoring tasks, accurate detection of crop rows is required to improve the environmental quality. Currently, drones perform numerous tasks using images and aerial panorama images captured in various working environments. They are suitable for performing the regular monitoring of a large-scale target region because of their high mobility and low-cost characteristics [21]. Through aerial panorama image construction, drones make monitoring the environment in a target area more efficient. Thus, aerial map construction for drone remote-sensing applications provides useful information and is used for deep learning model enhancements and efficiencies [9]. Therefore, aerial remote-sensing research continues to draw attention. [PERSON] et al. [22] proposed a stitched panorama construction method using color blending calculation from the images imputed into the drone. This method exploits the similarity between neighboring pixels in a UAV image to reduce the computational time in post-processing. [PERSON] et al. [23] presented an automatic panorama construction algorithm using blur filtering, key frame selection, and camera correction. They produced a resultant panorama image with high-quality details in a real subway construction site in Korea. Images captured using an RGB camera is a general method in drones. However, nowadays, multispectral imaging has become an attractive sensing modality, especially for SWIR images. [PERSON] et al. [24] developed an iterative aerial map at the encoder-decoder simultaneously from the infrared video captured during drone flying missions. The advantage of this scheme is that it reconstructs the images of the video sequence by using a decoder to avoid storing the aerial map, thereby lowering the bit rate. [PERSON] et al. [25] studied the best accuracy of near-infrared and SWIR hyperspectral frame cameras for moisture estimation. They indicated that drones could significantly improve their efficiency and safety in various remote-sensing applications. Drones can improve environmental monitoring safety, utility, and efficiency.