A new study published in Big Earth Data applies the INFORM Climate Change model to project future risks of humanitarian crises and disasters by integrating climate hazards, population dynamics, conflict, and socioeconomic development pathways. Incorporating forward-looking projections of vulnerability and coping capacity under different Shared Socioeconomic Pathways, the analysis shows that global risk may decline under moderate and rapid development scenarios but could rise sharply in the high-emission, fragmented SSP3 pathway. The findings provide evidence for prioritizing vulnerable regions and guiding targeted risk reduction and climate adaptation strategies.

Space missions are complex, multidisciplinary tasks that involve high risk and high cost. Systems engineering (SE) technology is an emerging discipline used to manage project complexity and ensure mission success . As technology advances and systems become more complex and volatile, SE needs to accommodate the constant reassessment, upgrading, and development of systems . Traditional SE relies on a large number of decentralized documents that cannot keep up with the changes in the system. Therefore, SE is transforming toward digitalization and has led to model-based systems engineering (MBSE), which provides a way to address SE challenges and is emerging as a paradigm and principle of SE. According to the International Council on Systems Engineering, MBSE is “the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phase”.Space missions are complex, multidisciplinary tasks that involve high risk and high cost. Systems engineering (SE) technology is an emerging discipline used to manage project complexity and ensure mission success . As technology advances and systems become more complex and volatile, SE needs to accommodate the constant reassessment, upgrading, and development of systems . Traditional SE relies on a large number of decentralized documents that cannot keep up with the changes in the system. Therefore, SE is transforming toward digitalization and has led to model-based systems engineering (MBSE), which provides a way to address SE challenges and is emerging as a paradigm and principle of SE. According to the International Council on Systems Engineering, MBSE is “the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phase”.

Local photothermal effect of AuNRs gives rise to high local temperatures. Two methods based on electron spin resonance (ESR) technique were developed to characterize the local temperature (T_local) around the excited rod. The obtained T_local is 20-30℃ higher than the global temperature (T_global) of the illuminated suspension measured using thermocouple. The local photothermal effects of gold nanorods (AuNRs) can promote the thermal decomposition of H₂O₂ to generate hydroxyl radicals. The AuNRs + H₂O₂ system can be used as a light-triggered hydroxyl radical source to regulate the generation of hydroxyl radical by time and space.

A new study published in Big Earth Data proposes an AI cube framework that integrates GeoAI models into geospatial data cube infrastructures to enhance large-scale Earth Observation data analytics. By introducing a model warehouse, intelligent model selection, and parallel inference pipelines on the Open Geospatial Engine platform, the approach significantly improves analytical capability and reduces inference time by over 80%. The framework advances the transition from traditional data cube processing toward AI-ready spatial data infrastructures.

Vehicle re-identification (Re-ID) stands as a cornerstone technology in intelligent transportation systems, enabling the tracking of individual vehicles across non-overlapping surveillance cameras in urban environments. Despite substantial progress in deep learning approaches, real-world deployment faces persistent obstacles from diverse vehicle poses caused by varying camera angles, viewpoints, and driving directions. These pose variations scatter feature representations of the same vehicle in the embedding space, leading to reduced discriminative power and lower identification accuracy. Traditional methods relying on deep metric learning struggle to bridge these gaps, as pose differences create discrete clusters even for identical vehicles, complicating reliable matching in practical traffic scenarios.

A recent study introduces an innovative strategy to mitigate this challenge by projecting vehicle images from diverse poses into a unified target pose, generating synthetic images that serve as pose-invariant auxiliary information to strengthen Re-ID models. Recognizing the high costs and logistical difficulties of acquiring paired images of the same vehicle from different cameras, researchers developed VehicleGAN, the first pair-flexible pose-guided image synthesis framework tailored for vehicle Re-ID. This end-to-end Generative Adversarial Network accepts a source vehicle image and a target pose as inputs, synthesizing the vehicle in the desired pose without depending on detailed 3D geometric models. VehicleGAN operates effectively in both supervised settings, using paired data when available, and unsupervised scenarios through a novel AutoReconstruction mechanism. In this self-supervised approach, the model transfers an image to the target pose and back to the original, reconstructing the input to learn robust transformations without requiring expensive paired annotations. This flexibility addresses key limitations of prior 3D-based methods, which demand precise camera parameters often unavailable in real surveillance setups, and supervised 2D methods burdened by labor-intensive labeling.

A new study published in Big Earth Data introduces Geo-Disasters, a database that geocodes 9,217 climate-related disasters reported by EM-DAT from 1990 to 2023. The database enhances disaster risk analysis by providing accurate geocoded locations and offering an open, reproducible framework for integrating spatial climate and socioeconomic data. This framework supports machine learning applications for climate risk, vulnerability, and adaptation studies. The Geo-Disasters database aims to bridge critical data gaps in global climate-hazard risk assessment and inform more equitable adaptation planning. The link to Geo-Disasters Database is https://doi.org/10.5281/zenodo.15487667.