Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing.

The human thermal stress indices and datasets are vital for promoting public health and reducing negative environmental impacts as global climate change and extreme meteorological events increase. The current thermal indices generally use an instantaneous or average value to describe thermal stress which cannot reflect the distribution of thermal comfort conditions over time, and there are no global-scale thermal stress datasets with both 0.1° or higher spatial resolution and hourly temporal resolution available yet. A novel human thermal metric, Thermal Stress Duration (TSD), is proposed to represent the accumulative time of different thermal stress levels within a certain period. A high temporal resolution global gridded dataset of human thermal stress metrics (HiGTS) is presented, which consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily TSD at 0.1° × 0.1° spatial resolution over the global land surface, spanning from January 1, 2000, to December 31, 2023.

The detection of primary and secondary schools (PSSs) is a meaningful task for composite object detection in remote sensing images (RSIs). As a typical composite object in RSIs, PSSs have diverse appearances with complex backgrounds, which makes it difficult to effectively extract their features using the existing deep-learning-based object detection algorithms. Aiming at the challenges of PSSs detection, we propose an end-to-end framework called the attention-guided dense network (ADNet), which can effectively improve the detection accuracy of PSSs. First, a dual attention module (DAM) is designed to enhance the ability in representing complex characteristics and alleviate distractions in the background. Second, a dense feature fusion module (DFFM) is built to promote attention cues flow into low layers, which guides the generation of hierarchical feature representation. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods and achieves 79.86% average precision. The study proves the effectiveness of our proposed method on PSSs detection.

Object detection in remote sensing images (RSIs) is currently one of the most important topics, which can promote the understanding of the earth and better serve for the construction of digital earth. In addition to single objects, there are many composite objects in RSIs, especially primary and secondary schools (PSSs), which are composed of some parts and surrounded by complex background. Existing deep learning methods have difficulty detecting composite objects effectively. In this article, we propose a feature enhanced Network (FENet) based on an anchor-free method for PSSs detection. FENet can not only realize more accurate pixel-level detection based on enhanced features but also simplify the training process by avoiding hyper-parameters. First, an enhanced feature module (EFM) is designed to improve the representation ability of complex features. Second, a context-aware strategy is used for alleviating the interference of background information. In addition, complete intersection over union (CIoU) loss is employed for bounding box regression, which can obtain better convergence speed and accuracy. At the same time, we build a PSSs dataset for composite object detection. This dataset contains 1685 images of PSSs in Beijing–Tianjin–Hebei region. Experimental results demonstrate that FENet outperforms several object detectors and achieves 78.7% average precision. The study demonstrates the advantage of our proposed method on PSSs detection.