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To promote the transformation of remote sensing (RS) data into geoscience knowledge, it is necessary to provide better data discovery capabilities, especially when large amounts of RS data have been accumulated. Spatio-temporal range query is one of them to enable data discovery on RS images by the spatio-temporal range. Although existing RS image indexing methods are suitable for data discovery based on spatio-temporal range queries, they do not fully consider the problem of low data retrieval efficiency when the temporal and spatial range scales are unbalanced. To address this problem, we propose a multi-scale spatio-temporal grid index model (MSTGI). First, we divide the time dimension into three levels according to the granularity of year, month and day, and build an independent grid index structure for the object at different levels. Second, we design an adaptive hierarchical indexing strategy to perform parallel retrieval in an appropriate combination of partitions. MSTGI linearizes the grid obtained after global geospatial subdivision using Hilbert curves. Our experimental results, obtained on the Landsat series satellite dataset, reveal that the proposed method improves query efficiency levels by approximately 12.582% and 87.754% compared with GeoSOT-ST and AGMC at various spatial scales, respectively, and reaches 100% recall. [ABSTRACT FROM AUTHOR]

As a classic problem of high-resolution remote sensing image tasks, land cover classification has many challenges, for instance, variable scales and low-edge discrimination. Traditional spatial semantic segmentation methods based on single-scale feature extraction are challenging to model multi-scale features. They cannot effectively fit high-resolution remote sensing images with variable scales and are prone to misclassification issues. On the other hand, spatial multi-scale semantic segmentation methods lack edge constraints, which can easily lead to edge segmentation ambiguity issues. We propose a segmentation network based on a multi-scale frequency-domain attention gating mechanism (MFAGNet) to solve the above problems. Specifically, we obtain multi-scale features through multi-scale input and design the discrete cosine transform channel attention module (DCTCAM). In DCTCAM, we extract global low-frequency features and effectively alleviate misclassification caused by scale changes. The edge features of the image are obtained through the high-frequency feature extraction module (HFEM) by using edge detection operators to enhance the model's ability to recognize edges. Additionally, no further pooling operations are employed except for a single max pooling layer in the backbone network. In this paper, we design a new gating mechanism to dynamically control the extraction ratio of various frequency features so that it can adapt to input images of different scales. This paper conducts comparative experiments on three high-resolution land cover datasets: GID, Vaihingen, and Potsdam. The new algorithm proposed compares with classic semantic segmentation models such as DANet, PSPNet, SegNet, UNet, DeepLabv3+, FcaNet, BiSeNet, AFN, and FastFCN. The experimental results show that MFAGNet is superior to the comparison models. With no significant increase in computing overhead, MFAGNet obtains MIoU of 69.05%, 73.40%, and 79.42% on the standard data sets GID, Vaihingen, and Potsdam, respectively. [ABSTRACT FROM AUTHOR]