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Human activities increase significantly over Antarctic ice shelves. However, they are constantly faced with danger posed by the harsh environment. For decision-making, it is a prerequisite to have the macro-scale suitability information about human activity site selection. Here, we define a new index, the human activity suitability (HAS) index, to quantitatively analyze the locational suitability of human activity sites over Antarctic ice shelves. Two multi-criteria decision analysis methods (AHP + Entropy, TOPSIS) and three machine learning methods (support vector machine, random forest and logistic regression) are tested to develop HAS maps. Nine conditioning factors about ice surface features, ice shelf stability, meteorology and topography are generated as input parameters. The accuracy of the proposed models is evaluated using metrics such as the area under curve (AUC-ROC), root mean square error, overall accuracy and kappa index. The results indicate that the Random Forest performs best. The HAS map exhibits great heterogeneity driven by the synergistic influence of multiple factors. Areas in low HAS classes are concentrated at Crosson, Brunt, Thwaites and the edge of some ice shelves, implying the complex environment in these regions. The findings can provide a new insight for forecasting the potential human footprint and support sustainability research in Antarctica. [ABSTRACT FROM AUTHOR]

Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster prevention. However, due to interference occasioned by density, overlap, and coverage, the tiny object detection in remote sensing images has always been a difficult problem. Therefore, we propose a novel TO–YOLOX(Tiny Object–You Only Look Once) model. TO–YOLOX possesses a MiSo(Multiple-in-Single-out) feature fusion structure, which exhibits a spatial-shift structure, and the model balances positive and negative samples and enhances the information interaction pertaining to the local patch of remote sensing images. TO–YOLOX utilizes an adaptive IOU-T (Intersection Over Uni-Tiny) loss to enhance the localization accuracy of tiny objects, and it applies attention mechanism Group-CBAM (group-convolutional block attention module) to enhance the perception of tiny objects in remote sensing images. To verify the effectiveness and efficiency of TO–YOLOX, we utilized three aerial-photography tiny object detection datasets, namely VisDrone2021, Tiny Person, and DOTA–HBB, and the following mean average precision (mAP) values were recorded, respectively: 45.31% (+10.03%), 28.9% (+9.36%), and 63.02% (+9.62%). With respect to recognizing tiny objects, TO–YOLOX exhibits a stronger ability compared with Faster R-CNN, RetinaNet, YOLOv5, YOLOv6, YOLOv7, and YOLOX, and the proposed model exhibits fast computation. [ABSTRACT FROM AUTHOR]

Big Earth Data—big data associated with Earth sciences—can potentially revolutionize research on climate change, sustainable development, and other issues of global concern. For example, analyzing massive amounts of satellite imagery of polar environments, which are sensitive to the effects of climate change, provides insights into global climate trends. This study proposes a method to use Big Earth Data to explore changes in snowmelt over the Antarctic ice sheet from 1979 to 2016. The method uses Zernike moments to observe melt area in Antarctica and uses the Mann-Kendall test to detect temporal changes and abnormal information about the continent's melt area. The melting trend in the time-series data matched the changes in temperature and seasonal transitions. The results do not demonstrate significant change in the area of surface melt; however, abrupt changes in melt conditions linked to temperature changes over the Antarctic ice sheet were observed within the time series. The experiment results demonstrate that the proposed method is robust, adaptive, and capable of extracting the core features of melting snow. [ABSTRACT FROM AUTHOR]

The Belt and Road (B&R) region, a vital area with historical, economic, cultural and political significance, has undergone rapid urbanization in the past several decades, especially in the form of urban expansion. In this study, 20 megacities in the B&R region were selected to explore different spatiotemporal patterns of urban expansion. Object-oriented support vector machines (SVM), annual growth rate (AGR) models, and landscape metrics were employed to delineate the urban areas and characterize spatiotemporal characteristics and landscape patterns of these megacities during 1975–2015. All urban maps presented high overall accuracies (80.70%–95.90%) and overall Kappa coefficients (0.76–0.95). The study revealed that megacities in the B&R region have undergone different types of urban sprawl, mainly adopting a 'concentric circle' pattern in inland areas and a 'sector' pattern in coastal areas. Besides, six expansion modes were summarized according to the AGRs of individual megacities. Differences existed in megacities of the developing and developed countries and among five sub-regions. Moreover, 'dispersion, gathering, and re-dispersion' and 'coalescence' were two major landscape patterns of megacities in developing and developed countries. Results of this study can provide a scientific reference for urban planning and aid in sustainable development of local areas. [ABSTRACT FROM AUTHOR]

The Moon is a potential new platform for Earth observation. The advantages of its large-scale observational scope, long temporal duration, and multi-layer detecting of the Earth will undoubtedly advance our understanding of the Earth system. To carry out the observations from a Moon-based optical sensor, the geolocation error caused by exterior orientation elements need to be investigated. This paper analyses the error effects of exterior orientation elements on geolocation for an optical sensor. To estimate the error, we present a geometric image model and utilise some parameters to measure the image offsets. Through a large number of numerical simulations, the results demonstrate that the image offsets are not obvious influenced by the distance and observation angle at mid-high latitude of the Moon and have linear correlation with the increasing errors of the exterior orientation elements. Further, the relationship between the spatial resolution and errors of exterior orientation elements are revealed. Finally, the error characteristics for Moon-based Earth observation are discussed. It is expected that the conclusion drawn in this paper could support the study of a Moon-based Earth observation optical sensor. [ABSTRACT FROM AUTHOR]

The Moon, Earth's only natural satellite, is a potential new platform for Earth observation. Moreover, with the wide applicability of the angular information from remote sensing data, it has been attracting increasingly more attention. Accordingly, this study focuses on the angular characteristics of Moon-based Earth observations. Using ephemeris DE430 and Earth orientation parameters, the position and attitude of the Sun, Earth, and Moon were obtained and their coordinates normalized to a single framework using coordinate transformations between the related reference systems. Then, an angular geometric model of Moon-based Earth observations was constructed, and the corresponding angular algorithms were presented. The results revealed the angular range and distribution characteristics of Moon-based Earth observations. For every point on the surface of the Earth, the view and solar zenith angles all vary widely, which decreases with increasing latitude. The view and solar zenith angles all vary widely with the largest range of values in the equatorial and polar regions and a smaller range of values in mid-latitudes. Furthermore, the range of solar angles of Moon-based Earth observations is the same as that of all-time solar angles, indicating the potential for monitoring and understanding large-scale geoscientific phenomena using Moon-based Earth observations. [ABSTRACT FROM AUTHOR]

Although Earth’s surface parameters obtained from satellite data have become more and more precise, it is still difficult to guarantee temporal consistency and spatial continuity for large-scale geoscience phenomena. Developing new Earth observation platforms is a feasible way to improve the consistency and continuity of such data. As the planet’s only natural satellite, the Moon has special advantages as a platform for observing Earth, including long lifetime, whole disk view, tectonic stability and unique perspective. After presenting the observation geometry constructed by using the ephemeris, this paper mainly discusses the characteristics of a lunar platform and the proper Moon-based sensors, as well as the scientific objectives of Moon-based Earth observation. Solid Earth dynamics, the energy budget of Earth, Earth’s environmental elements and the Earth-space environment are four potential applications analysed in this paper. [ABSTRACT FROM AUTHOR]

This study mainly focuses on revealing an ancient water landscape at the Longcheng site in the northern Chaohu Lake Basin using very high-resolution (VHR) GeoEye-1 imagery. First, prior to classification, the GeoEye-1 image was processed following atmospheric and geometric correction. The supervised classification was carried out in order to show the land-cover situation in the Longcheng area. The overall classification accuracy was 89.98%, with a kappa coefficient of 0.87. The moat system around the city walls was discovered by using rule-based object-oriented segmentation of the postclassified image, and the other walls of ancient Longcheng were manually identified from the pansharpened VHR GeoEye-1 image. Finally, a map of the ancient water landscape containing the ancient city, wall and moat at the Longcheng site was produced. This paper demonstrates that VHR remote sensing has the ability to uncover an ancient water landscape and provide new insights for archaeological and paleoenvironmental studies. [ABSTRACT FROM AUTHOR]

Digital Earth has seen great progress during the last 19 years. When it entered into the era of big data, Digital Earth developed into a new stage, namely one characterized by ‘Big Earth Data’, confronting new challenges and opportunities. In this paper we give an overview of the development of Digital Earth by summarizing research achievements and marking the milestones of Digital Earth’s development. Then, the opportunities and challenges that Big Earth Data faces are discussed. As a data-intensive scientific research approach, Big Earth Data provides a new vision and methodology to Earth sciences, and the paper identifies the advantages of Big Earth Data to scientific research, especially in knowledge discovery and global change research. We believe that Big Earth Data will advance and promote the development of Digital Earth. [ABSTRACT FROM PUBLISHER]

The frequency of coastal flood damages is expected to increase significantly during the twenty-first century as sea level rises in the coastal floodplain. Coastal digital elevation model (DEM) data describing coastal topography are essential for assessing future flood-related damages and understanding the impacts of sea-level rise. The Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) are currently the most accurate and freely available DEM data. However, an accuracy assessment specifically targeted at DEMs over low elevation coastal plains is lacking. The present study focuses on these areas to assess the vertical accuracy of SRTM and ASTER GDEM using Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic (RTK) Global Positioning System (GPS) field survey data. The findings show that DEM accuracy is much better than the mission specifications over coastal plains. In addition, optical remote sensing image analysis further reveals the relationship between DEM vertical accuracy and land cover in these areas. This study provides a systematic approach to assess the accuracy of DEMs in coastal zones, and the results highlight the limitations and potential of these DEMs in coastal applications. [ABSTRACT FROM PUBLISHER]