Jung-Sup Um

This study shows that the photosynthesis of Arctic terrestrial plants is more vulnerable and sensitive to heatwaves’ impacts than plants in other European climate zones. The results of this study indicate distinct effects of heatwaves among different climate zones in Europe (2009–2017). The Arctic climate zone shows a steeper decline in photosynthesis during heatwaves, as revealed by geographically and temporally weighted regression analysis based on biophysical factors such as the Normalized Difference Vegetation Index and Leaf Area Index, compared to arid, temperate, and cold climate zones. As Arctic temperatures continue to rise, it would lead to photosynthetic inhibition from Arctic terrestrial plants, irreversibly turning them into one of the largest CO ₂ sources. The results of this study are expected to be a valuable reference as they provide European-wide quantitative evidence that global warming has caused comparably stronger negative consequences on the photosynthetic physiology of plants living in the Arctic compared to other climatic zones.

Accurate identification of residential areas from remote sensing (RS) imagery plays a pivotal role in urban planning, infrastructure development, and environmental monitoring. Existing convolutional neural network (CNN) and transformer-based models have demonstrated strong performance on the EuroSAT benchmark; however, they often struggle to effectively capture both long-range dependencies and fine-grained spectral-spatial features, particularly in complex residential environments. To address this gap, we propose HSS-KAMNet, a novel hybrid spectral-spatial Kolmogorov-Arnold Mamba network that integrates three core cutting-edge paradigms-Kolmogorov-Arnold Networks (KANs), State Space Models (Mamba), and hybrid multi-scale attention mechanisms-into a unified architecture. HSS-KAMNet leverages a Spectral-Spatial KAN Encoder with learnable B-spline activations to extract adaptive, interpretable features from all 13 Sentinel-2 spectral bands. A dual-branch Mamba processor employs spatial and spectral Mamba streams to efficiently model long-range dependencies, with a cross-modal fusion mechanism enhancing joint feature representation. A novel hybrid attention mechanism fuses CNN-local and transformer-global features, enabling robust context awareness. We further introduce a spectral-spatial boundary loss to enforce spectral consistency, sharpen spatial boundaries, and regulate activation smoothness. Evaluated on different benchmark datasets for residential land cover classification, HSS-KAMNet achieves a new state-of-the-art accuracy of 99.42%, significantly improving boundary delineation and generalization to unseen regions. Our results demonstrate that HSS-KAMNet not only advances classification accuracy but also enhances model interpretability, making it a practical and explainable tool for residential area mapping from RS imagery.

This study assesses the practical implications of ChatGPT’s ability to identify hotspots by comparing its performance to Geographical Information System (GIS) software in detecting CO₂ sources and sinks observed by the Orbiting Carbon Observatory-3 (OCO-3) satellite. ChatGPT exhibited performance comparable to ArcGIS in both z-score statistics and spatial distribution patterns of XCO₂ hot and cold spots. The results generated by ChatGPT showed a strong correlation with ArcGIS-generated hotspots, demonstrating a z-score correlation coefficient of R²=0.82 and a cosine similarity score of 0.90. As multimodal artificial intelligence becomes more prevalent in earth monitoring, ChatGPT is expected to be a valuable tool for identifying CO₂ emission patterns, particularly for users who lack specialized GIS expertise. These findings establish a significant benchmark for ChatGPT’s potential in this field, offering a novel approach to identifying area-wide spatial patterns of CO₂ emissions compared to conventional GIS software.

Chat Generative Pre-trained Transformer (ChatGPT), developed by OpenAI, is a prominent AI model capable of understanding and generating human-like text based on input. Since terms and concepts of spatial information are contextual, the applications of ChatGPT on spatial information disciplines can be biased by the perceptions and perspectives of ChatGPT towards spatial information. Therefore, a thorough understanding of the real magnitude and level of comprehension of spatial information by ChatGPT is essential before exploring its potential applications in spatial information disciplines. This article aims to investigate how ChatGPT evaluates spatial information and its potential contributions to 4th Industrial Revolution (Industry 4.0). ChatGPT has summarized a notable perspective on evaluating and utilizing spatial information in the context of the Industry 4.0. The result of this study shows that ChatGPT has a good understanding on contextual concepts related to spatial information. However, it exhibits potential biases and challenges, as its responses lean towards the technological and analytical aspects. The results provide a crucial understanding on how to leverage ChatGPT’s benefits to the fullest while recognizing its constraints, with the aim to enhance the efficacy from the perspective of applications linked to spatial information.