With the rapid development of satellite technology, the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing. Hence, it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications. However, with the improvements in temporal and spatial resolution, remote sensing data have become fragmented, which brings challenges to data retrieval. At present, most data service platforms rely on the query engines to retrieve data. Retrieval results still have a large amount of data with a high degree of overlap, which must be manually selected for further processing. This process is very labour-intensive and time-consuming. This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area. The retrieval efficiency of this algorithm was analysed by applying different implementation practices: Arcpy, PyQGIS, and GeoPandas. The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data.

Due to the cloud coverage of remote-sensing images, the ground object information will be attenuated or even lost, and the texture and spectral information of the image will be changed at the same time. Accurately detecting clouds from remote-sensing images is of great significance to the field of remote sensing. Cloud detection utilizes semantic segmentation to classify remote-sensing images at the pixel level. However, previous studies have focused on the improvement of algorithm performance, and little attention has been paid to the impact of bit depth of remote-sensing images on cloud detection. In this paper, the deep semantic segmentation algorithm UNet is taken as an example, and a set of widely used cloud labeling dataset “L8 Biome” is used as the verification data to explore the relationship between bit depth and segmentation accuracy on different surface landscapes when the algorithm is used for cloud detection. The research results show that when the image is normalized, the effect of cloud detection with a 16-bit remote-sensing image is slightly better than that of an 8-bit remote sensing image; when the image is not normalized, the gap will be widened. However, using 16-bit remote-sensing images for training will take longer. This means data selection and classification do not always need to follow the highest possible bit depth when doing cloud detection but should consider the balance of efficiency and accuracy.

With the rapid development of remote sensing technology, remote sensing products have found increasingly widespread applications across various fields. Nevertheless, as the volume of remote sensing image data continues to grow, traditional data retrieval techniques have encountered several challenges such as substantial query results, data overlap, and variations in data quality. Users need to manually browse and filter a large number of remote sensing datasets, which is a cumbersome and inefficient process. In order to cope with these problems of traditional remote sensing image retrieval methods, this paper proposes a remote sensing image retrieval algorithm for dense data (DD-RSIRA). The algorithm establishes evaluation metrics based on factors like imaging time, cloud coverage, and image coverage. The algorithm utilizes the global grids to create an ensemble coverage relation between images and grids. A locally optimal initial solution is obtained by a greedy algorithm, and then a local search is performed to search for the optimal solution by combining the strategies of weighted gain-loss scheme and novel mechanism. Ultimately, it achieves an optimal coverage of remote sensing images within the region of interest. In this paper, it is shown that the method obtains a smaller number of datasets with lower redundancy and higher data utilization and ensures the data quality to a certain extent in order to accurately meet the requirements of the regional coverage of remote sensing images.

With the rapid growth of remote sensing data, efficiently managing and retrieving large-scale remote sensing images has become a significant challenge. Specifically, for multi-label image retrieval, single-scale feature extraction methods often fail to capture the rich and complex information inherent in these images. Additionally, the sheer volume of data creates challenges in retrieval efficiency. Furthermore, leveraging semantic information for more accurate retrieval remains an open issue. In this paper, we propose a multi-label remote sensing image retrieval method based on an improved Swin Transformer, called Semantically Guided Deep Supervised Hashing (SGDSH). The method aims to enhance feature extraction capabilities and improve retrieval precision. By utilizing multi-scale information through an end-to-end learning approach with a multi-scale feature fusion module, SGDSH effectively integrates both shallow and deep features. A classification layer is introduced to assist in training the hash codes, incorporating RS image category information to improve retrieval accuracy. The model is optimized for multi-label retrieval through a novel loss function that combines classification loss, pairwise similarity loss, and hash code quantization loss. Experimental results on three publicly available remote sensing datasets, with varying sizes and label distributions, demonstrate that SGDSH outperforms state-of-the-art multi-label hashing methods in terms of average accuracy and weighted average precision. Moreover, SGDSH returns more relevant images with higher label similarity to query images. These findings confirm the effectiveness of SGDSH for large-scale remote sensing image retrieval tasks and provide new insights for future research on multi-label remote sensing image retrieval.

The rapid growth of remote sensing data offers unprecedented opportunities for global environmental monitoring and resource assessment, yet poses significant challenges for efficient selection of large-scale image datasets. Traditional conditional retrieval methods often return extensive sets with substantial spatial redundancy, imposing heavy selection burdens on users. Existing automated selection methods struggle to balance coverage accuracy, redundancy control, and computational efficiency in large-scale scenarios, making efficient and accurate image selection a critical challenge for large-scale applications. To address this, we propose SCOPES (Spatially-Constrained Optimization for Efficient Image Selection), a novel spatial constraint optimization framework. SCOPES operates directly on actual image footprints in continuous space, thereby circumventing the limitations of traditional discretization-based modeling. We design a unit area cost function aimed at balancing image quality with spatial contribution. To ensure computational efficiency and solution optimization, SCOPES adopts a three-stage “preliminary selection-structural optimization-supplementary selection” strategy: employing lazy greedy for efficient initial selection, spatial Boolean overlay for redundancy control, and supplementary selection for coverage gap repair. Experiments conducted in four regions of different scales demonstrate that compared to baseline methods, SCOPES minimizes the number of selected images and maximizes coverage while achieving a near-universally minimal redundancy ratio. Meanwhile, the introduction of the lazy greedy algorithm significantly improves computational efficiency, achieving up to a 229-fold speedup in the large-scale East Asia region. Overall, SCOPES provides an efficient, accurate, and scalable solution for remote sensing data selection, substantially reducing the manual selection workload for platform users.