The COVID-19 outbreak has become a global pandemic. The spatial variation in the environmental, health, socioeconomic, and demographic risk factors of COVID-19 death rate is not well understood. Global models and local linear models were used to estimate the impact of risk factors of the COVID-19, but these do not account for the nonlinear relationships between the risk factors and the COVID-19 death rate at various geographical locations. We proposed a local nonlinear nonparametric regression model named geographically weighted random forest (GW-RF) to estimate the nonlinear relationship between COVID-19 death rate and 47 risk factors derived from the US Environmental Protection Agency, National Center for Environmental Information, Centers for Disease Control and the US census. The COVID-19 data were employed to a global regression model random forest (RF) and a local model GW-RF. The adjusted R² of the RF is 0.69. The adjusted R² of the proposed GW-RF is 0.78. The result of GW-RF showed that the risk factors (i.e. going to work by walking, airborne benzene concentration, householder with a mortgage, unemployment, airborne PM_2.5 concentration and per cent of the black or African American) have a high correlation with the spatial distribution of the COVID-19 death rate, and these key factors driven from the GW-RF were mapped, which could provide useful implications for controlling the spread of the COVID-19 pandemic.

We demonstrate the capabilities of a published MADNet monocular height estimation network in producing a refined digital terrain model (DTM) mosaic at 50 cm/pixel resolution for the Mars 2020 Perseverance rover landing site in Jezero crater on Mars. Our approach utilizes the publicly available Mars 2020 Terrain Relative Navigation (TRN) High‐Resolution Imaging Science Experiment (HiRISE) Digital Terrain Model (DTM) mosaic, which was originally created by the United States Geological Survey (USGS) Astrogeology Science Centre. Our resultant HiRISE MADNet DTM mosaic is strictly matched with the original HiRISE TRN DTM and orthoimage mosaics. These mosaics are themselves co‐aligned with the USGS TRN Context Camera (CTX) based DTM and orthoimage mosaics, as well as the ESA/DLR/FUB (European Space Agency/German Aerospace Center/Free University Berlin) High Resolution Stereo Camera (HRSC) level 5 DTM and orthoimage mosaics. In this paper, we provide a brief description of the technical details, and present both visual and quantitative assessments of the refined MADNet HiRISE Jezero DTM mosaic product. This DTM product is now publicly available at http://dx.doi.org/10.17169/refubium-38359 .

Variations in the Martian surface temperature indicate patterns of surface energy exchange. The Martian surface temperature at a location is similar to those in adjacent locations; but, an understanding of temperature clusters in multiple locations will deepen our knowledge of planetary surface processes overall. The spatial coherence of the Martian surface temperature (ST) at different locations, the spatio-temporal variations in temperature clusters, and the relationships between ST and near-surface environmental factors, however, are not well understood. To fill this gap, we studied an area to the south of Utopia Planitia, the landing zone for the Tianwen-1 Mars Exploration mission. The spatial aggregation of three Martian ST indicators (STIs), including sol average temperature (SAT), sol temperature range (STR), and sol-to-sol temperature change (STC), were quantitatively evaluated using clustering analysis at the global and local scale. In addition, we also detected the spatio-temporal variations in relations between the STIs and seven potential driving factors, including thermal inertia, albedo, dust, elevation, slope, and zonal and meridional winds, across the study area during 81 to 111 sols in Martian years 29–32, based on a geographically and temporally weighted regression model (GTWR). We found that the SAT, STR, and STC were not randomly distributed over space but exhibited signs of significant spatial aggregation. Thermal inertia and dust made the greatest contribution to the fluctuation in STIs over time. The local surface temperature was likely affected by the slope, wind, and local circulation, especially in the area with a large slope and low thermal inertia. In addition, the sheltering effects of the mountains at the edge of the basin likely contributed to the spatial difference in SAT and STR. These results are a reminder that the spatio-temporal variation in the local driving factors associated with Martian surface temperature cannot be neglected. Our research contributes to the understanding of the surface environment that might compromise the survival and operations of the Tianwen-1 lander on the Martian surface.

Spatio-temporal variation in the Martian surface temperature (MST) is an indicator of ground level thermal processes and hence a building block for climate models. However, the distribution of MST exhibits different levels of spatial aggregation or heterogeneity, and varies in space and time. Furthermore, the effect of regional differences in meteorological or environmental factors on the MST is not well understood. Thus, we investigated the degree of spatial autocorrelation of MST across the surface of Mars globally by Moran’s I, and identified the hot spots by GetisOrd G i * . We also estimated the regional differences in the influence of seasonally dominant factors including thermal inertia (TI), albedo, surface pressure, latitude, dust and slope on MST by a geographically weighted regression model. The results indicate (1) that MST is spatially aggregated and hot and cold spots varied over time and space. (2) Hemispheric differences in topography, surface TI and albedo were primarily responsible for the hemispheric asymmetry of hot spots. (3) The dominant factors varied by geographical locations and seasons. For example, the seasonal Hadley circulation dominates at the low-latitudes and CO ₂ circulation at the high-latitudes. (4) Regions with extreme variations in topography and low TI were sensitive to meteorological and environmental factors such as dust and CO ₂ ice. We conclude that the spatial autocorrelation of MST and the spatial and seasonal heterogeneity of influencing factors must be considered when simulating Martian climate models. This work provides a reference for further exploration of Martian climatic processes.

Deltas record fluvial and sedimentary activities on the early Mars. Yet, quantitative hydrologic and sedimentary processes related with their formation need further investigation. Here we quantitatively analysed the fluvial and sedimentary process of seven deltas exhibiting diverse morphologies across different Martian locations. Identification of Martian rivers from valleys formed the basis of our analysis, with estimates made for paleo discharge, sediment transport rates, timescales and requisite water for deltas construction. We considered various water supply processes, and classified the seven deltas into three types based on geological contexts of their supplying valleys and water source. We suppose that the delta formation potentially associated with various scenarios, including retreating erosion, groundwater sapping, ice melting, precipitation, impact events and variations in Martian obliquity, unveiling that a warm climate is not a fundamental requirement for the formation. Based on mass balances, the continuous formation timescales of these deltas range from days to thousands of Martian years. Considering the intermittency of water supply and flow dynamics, these timescales can extend to hundreds of millions of years. These findings allow for quantitatively analysing paleo-sedimentary processes that may preserve geological and climatic cues.

On the lunar surface, wrinkle ridges, grabens, and lobate scarps represent key tectonic landforms that reflect the evolution of the Moon’s stress field and its tectonic processes. However, these linear structures often exhibit weak textures, low contrast, and large scale variations, making manual interpretation inefficient and subjective. To address this issue, this study introduces an improved YOLOv8 model, termed HL-YOLOv8, for the automated detection of lunar linear features. The model incorporates a multiscale lightweight channel attention (C2f_MLCA) module into the backbone network to enhance the extraction of fine-grained and weak-texture features and integrates a multihead self-attention (C2f_MHSA) module in the feature fusion stage to improve the modelling of long-range spatial dependencies. In addition, the combination of a dual focal loss and a diversified data augmentation strategy effectively mitigates the detection difficulties caused by class imbalance and weak-feature samples. The experimental results obtained using the global LROC-WAC image dataset demonstrate that HL-YOLOv8 significantly outperforms the baseline YOLOv8 and other comparative models in terms of precision, recall, and mAP@0.5. Specifically, the proposed model achieved an average precision of 73.5%, an average recall of 73.1%, and an average mAP@0.5 of 74.6% on the evaluation dataset, showing particularly strong performance in detecting elongated grabens and boundary-blurred lobate scarps. The global distribution maps derived from the model predictions indicate that HL-YOLOv8 can be applied to comprehensively reconstruct the spatial patterns of the three types of linear structures and identify potential new features in high-latitude and geologically complex regions, demonstrating excellent generalizability and robustness. This study provides an efficient and reliable framework for the automated identification and global mapping of lunar linear features and offers a transferable methodological reference for the tectonic interpretation of terrestrial planets.