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Jian Chen 0047, Peilin Zhou, Yining Hua, Dading Chong, Meng Cao 0002, Yaowei Li 0001, Wei Chen 0070, Bing Zhu, Junwei Liang 0001, Zixuan Yuan

Meteorological heatmaps play a vital role in deciphering extreme weather phenomena, yet their inherent complexities-marked by irregular contours, unstructured patterns, and complex color variations-present unique analytical hurdles for state-of-the-art Vision-Language Models (VLMs). Current state-of-the-art models like GPT-4o, Qwen-VL, and LLaVA 1.6 struggle with tasks such as precise color identification and spatial localization, resulting in inaccurate or incomplete interpretations. To address these challenges, we introduce Sparse Position and Outline Tracking (SPOT), a novel algorithm specifically designed to process irregularly shaped colored regions in visual data. SPOT identifies and localizes these regions by extracting their spatial coordinates, enabling structured representations of irregular shapes. Building on SPOT, we construct ClimateIQA, a novel meteorological visual question answering (VQA) dataset, comprising 26,280 high-resolution heatmaps and 762,120 instruction samples for wind gust, total precipitation, wind chill index and heat index analysis. ClimateIQA enhances VLM training by incorporating spatial cues, geographic metadata, and reanalysis data, improving model accuracy in interpreting and describing extreme weather features. Furthermore, we develop Climate-Zoo, a suite of fine-tuned VLMs based on SPOT-empowered ClimateIQA, which significantly outperforms existing models in meteorological heatmap tasks.

Shailik Sarkar, Raquib Bin Yousuf, Linhan Wang, Brian Mayer, Thomas Mortier, Victor Deklerck, Jakub Truszkowski, John Simeone, Marigold Norman, Jade Saunders 等

Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the globe. Stable isotope ratio analysis (SIRA) is rapidly becoming an important tool for determining the harvest location of traded, organic, products. The spatial pattern in stable isotope ratio values depends on factors such as atmospheric and environmental conditions and can thus be used for geographic origin identification. We present here the results of a deployed machine learning pipeline where we leverage both isotope values and atmospheric variables to determine timber harvest location. Additionally, the pipeline incorporates uncertainty estimation to facilitate the interpretation of harvest location determination for analysts. We present our experiments on a collection of oak (Quercus spp.) tree samples from its global range. Our pipeline outperforms comparable state-of-the-art models determining geographic harvest origin of commercially traded wood products, and has been used by European enforcement agencies to identify harvest location misrepresentation. We also identify opportunities for further advancement of our framework and how it can be generalized to help identify the origin of falsely labeled organic products throughout the supply chain.

Xingchen Zou, Weilin Ruan, Siru Zhong, Yuehong Hu, Yuxuan Liang 0002

Climate change and rapid urbanization have led to the Urban Heat Island (UHI) effect, resulting in higher temperatures in metropolitan areas and negatively impacting urban communities. Accurate UHI forecasting is crucial for identifying high-risk periods and locations, especially in cities with vulnerable populations. Current methods are limited by data granularity and inadequate modeling of regional thermodynamics, which affects both accuracy and spatio-temporal granularity. In this paper, we propose DeepUHI, a data-driven context-aware framework for modeling local thermodynamics based on the heat equation, alongside the SeoulTemp dataset, the first multi-modal dataset for UHI effect predictions at the street level. Our framework utilizes a heat decomposition method to represent urban thermodynamics through thermodynamic cycles and thermal flows, effectively integrating urban environmental data. Extensive experiments show that our framework improves accuracy in UHI effect prediction and warning tasks, outperforming leading models. We have integrated DeepUHI into our SeoUHI platform to provide hourly street-level UHI forecasting for Seoul. The code, platform, and dataset are accessible at https://github.com/CityMind-Lab/DeepUHI.

Weijia Zhang 0003, Chenlong Yin, Hao Liu 0026, Hui Xiong 0001

Pre-trained Language Models (PLMs), such as ChatGPT, have significantly advanced the field of natural language processing. This progress has inspired a series of innovative studies that explore the adaptation of PLMs to time series analysis, intending to create a unified foundation model that addresses various time series analytical tasks. However, these efforts predominantly focus on Regularly Sampled Time Series (RSTS), neglecting the unique challenges posed by Irregularly Sampled Time Series (ISTS), which are characterized by uneven sampling intervals and prevalent missing data. To bridge this gap, this work takes the first step in exploring the potential of PLMs for ISTS analysis. We begin by investigating the effect of various methods for representing ISTS, aiming to maximize the efficacy of PLMs in the analysis. Furthermore, we propose a unified PLM-based framework, named ISTS-PLM, to address diverse ISTS analytical tasks. It integrates novel time-aware and variable-aware PLMs tailored to tackle the intractable intra- and inter-time series modeling in ISTS. Finally, extensive experiments on a comprehensive benchmark demonstrate that the ISTS-PLM, utilizing a structured and effective series-based representation for ISTS, consistently achieves state-of-the-art performance across various analytical tasks, such as classification, interpolation, extrapolation, few-shot and zero-shot learning scenarios, spanning scientific domains like healthcare, biomechanics, and climate science.

Jiawen Chen, Qi Shao, Duxin Chen, Wenwu Yu

Spatio-temporal prediction is a pivotal task with broad applications in traffic management, climate monitoring, energy scheduling, etc. However, existing methodologies often struggle to balance model expressiveness and computational efficiency, especially when scaling to large real-world datasets. To tackle these challenges, we propose STH-SepNet (Spatio-Temporal Hypergraph Separation Networks), a novel framework that decouples temporal and spatial modeling to enhance both efficiency and precision. Therein, the temporal dimension is modeled using lightweight large language models, which effectively capture low-rank temporal dynamics. Concurrently, the spatial dimension is addressed through an adaptive hypergraph neural network, which dynamically constructs hyperedges to model intricate, higher-order interactions. A carefully designed gating mechanism is integrated to seamlessly fuse temporal and spatial representations. By leveraging the fundamental principles of low-rank temporal dynamics and spatial interactions, STH-SepNet offers a pragmatic and scalable solution for spatio-temporal prediction in real-world applications. Extensive experiments on large-scale real-world datasets across multiple benchmarks demonstrate the effectiveness of STH-SepNet in boosting predictive performance while maintaining computational efficiency. This work may provide a promising lightweight framework for spatio-temporal prediction, aiming to reduce computational demands and while enhancing predictive performance. Our code is avaliable at https://github.com/SEU-WENJIA/ST-SepNet-Lightweight-LLMs-Meet-Adaptive-Hypergraphs.

Chi Xu, Yili Jin, Sami Ma, Rongsheng Qian, Hao Fang, Jiangchuan Liu, Xue Liu, Edith C.H. Ngai, William I. Atlas, Katrina M. Connors 等

Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.

Apoorva Upadhyaya, Wolfgang Nejdl, Marco Fisichella

Climate change is one of the most pressing global challenges that requires urgent adaptation and resilience efforts, highlighting the need for both scientific solutions and effective communication. In the digital age, online content plays a key role in shaping climate narratives. Therefore, previous research has mainly focused on public perception or categorized content by topics such as impacts, mitigation, policy, etc. Despite these efforts, identifying discussions that address climate change adaptation is crucial for monitoring resilience and assessing public sentiment, while recognizing denial narratives helps combat misinformation. Moreover, the public's exposure to online climate content can either lead to or hinder climate action, emphasizing the need for climate content moderation. To address these issues, we propose a novel multi-stage framework where stage 1 categorizes climate-related content into adaptation, resilience, and denial while stage 2 moderates content by enhancing or intervening based on its alignment with climate goals. We present a novel dataset by manually annotating publicly available tweets and news articles into different climate categories with the help of a taxonomy developed by domain experts. Extensive experiments with benchmark climate and other domain datasets validate the efficacy of our prediction stage, while human and external evaluations confirm the relevance of our moderation stage.

Yue Mao, Zhongdi Qu, Imanol Miqueleiz, Aaron Ferber, Sami Wolf, Marc Grimson, Sebastian Heilpern, Felipe S. Pacheco, Alexander S. Flecker, Peter B. McIntyre 等

Climate change and biodiversity loss are among humanity’s most pressing challenges. In 2022, under the auspices of the United Nations, over 190 countries reached a historic agreement to address the alarming loss of biodiversity and restore natural ecosystems. Target 3, often referred to as ``30x30'', seeks to effectively protect and manage 30% of the world’s terrestrial, inland water, coastal, and marine areas by 2030. In this work, we address the UN 30x30 target in the context of global freshwater fish conservation. Freshwater ecosystems are disproportionately unprotected, and their biota are declining at an alarming rate. Our goal is to select new protected areas that protect freshwater fish species as much as possible without exceeding total coverage of 30% of land area. To support this goal, we introduce the Expansion of Connected Components from Alternative Terminals Problem, a graph-based optimization problem that captures ecological priorities and connectivity constraints. We analyze its computational complexity, propose novel integer programming formulations, and develop scalable solution methods. We further evaluate its typical-case complexity under diverse settings and demonstrate that our approach scales to a global real-world scope, encompassing approximately 200,000 freshwater basins and 13,000 species, paving the way for implementing the 30x30 target on a worldwide scale.

Ke Liu, Shangde Gao, Yichao Fu, Xiaoliang Wu, Shuo Tong, Ajitha Rajan, Hao Xu

Recent advancements in large language models (LLMs) have revolutionized research discovery across various scientific disciplines, including materials science. The discovery of novel materials, particularly crystal materials, is essential for achieving sustainable development goals (SDGs), as they drive breakthroughs in climate change mitigation, clean and affordable energy, and the promotion of industrial innovation. However, unlocking the full potential of LLMs in materials research remains challenging due to the lack of high-quality, diverse, and instruction-based datasets. Such datasets are crucial for guiding these models in understanding and predicting the structure, property, and function of materials across various tasks. To address this limitation, we introduce Mat-Instruction, a large-scale inorganic material instruction dataset, specifically designed to unlock the potential of LLMs in materials science. Extensive experiments on fine-tuning LLaMA with our Mat-Instruction dataset demonstrate its effectiveness in advancing progress for materials science. The code and dataset are available at https://github.com/zjuKeLiu/Mat-Instructions

Wentao Gao, Jiuyong Li, Debo Cheng, Lin Liu, Jixue Liu, Thuc Le, Xiaojing Du, Xiongren Chen, Yun Chen, Yanchang Zhao

Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation of complex climate phenomena. Traditional bias correction methods, which rely on historical observation data and statistical techniques, often neglect unobserved confounders, leading to biased results. This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders. Inspired by recent advances in causality based time series deconfounding, our method first constructs a factor model to learn latent confounders from historical data and then applies them to enhance the bias correction process using advanced time series forecasting models. The experimental results demonstrate significant improvements in the accuracy of precipitation outputs. By addressing unobserved confounders, our approach offers a robust and theoretically grounded solution for climate model bias correction.

Palok Biswas, Zuzanna Osika, Isidoro Tamassia, Adit Whorra, Jazmin Zatarain-Salazar, Jan Kwakkel, Frans A. Oliehoek, Pradeep K. Murukannaiah

Addressing climate change requires coordinated policy efforts of nations worldwide. These efforts are informed by scientific reports, which rely in part on Integrated Assessment Models (IAMs), prominent tools used to assess the economic impacts of climate policies. However, traditional IAMs optimize policies based on a single objective, limiting their ability to capture the trade-offs among economic growth, temperature goals, and climate justice. As a result, policy recommendations have been criticized for perpetuating inequalities, fueling disagreements during policy negotiations. We introduce JUSTICE, the first framework integrating IAM with Multi-Objective Multi-Agent Reinforcement Learning (MOMARL). By incorporating multiple objectives, JUSTICE generates policy recommendations that shed light on equity while balancing climate and economic goals. Further, using multiple agents can provide a realistic representation of the interactions among the diverse policy actors. We identify equitable Pareto-optimal policies using our framework, which facilitates deliberative decision-making by presenting policymakers with the inherent trade-offs in climate and economic policy.

Hao Wang, Jindong Han, Wei Fan, Leilei Sun, Hao Liu

Spatio-temporal forecasting is pivotal in numerous real-world applications, including transportation planning, energy management, and climate monitoring. In this work, we aim to harness the reasoning and generalization abilities of Pre-trained Language Models (PLMs) for more effective spatio-temporal forecasting, particularly in data-scarce scenarios. However, recent studies uncover that PLMs, which are primarily trained on textual data, often falter when tasked with modeling the intricate correlations in numerical time series, thereby limiting their effectiveness in comprehending spatio-temporal data. To bridge the gap, we propose RePST, a semantic-oriented PLM reprogramming framework tailored for spatio-temporal forecasting. Specifically, we first propose a semantic-oriented decomposer that adaptively disentangles spatially correlated time series into interpretable sub-components, which facilitates PLM to understand sophisticated spatio-temporal dynamics via a divide-and-conquer strategy. Moreover, we propose a selective discrete reprogramming scheme, which introduces an expanded spatio-temporal vocabulary space to project spatio-temporal series into discrete representations. This scheme minimizes the information loss during reprogramming and enriches the representations derived by PLMs. Extensive experiments on real-world datasets show that the proposed RePST outperforms twelve state-of-the-art baseline methods, particularly in data-scarce scenarios, highlighting the effectiveness and superior generalization capabilities of PLMs for spatio-temporal forecasting. Codes and Appendix can be found at https://github.com/usail-hkust/REPST.

Ron Van Bree, Diego Marcos, Ioannis N. Athanasiadis

Biophysical models offer valuable insights into climate-phenology relationships in both natural and agricultural settings. However, there are substantial structural discrepancies across models which require site-specific recalibration, often yielding inconsistent predictions under similar climate scenarios. Machine learning methods offer data-driven solutions, but often lack interpretability and alignment with existing knowledge. We present a phenology model describing dormancy in fruit trees, integrating conventional biophysical models with a neural network to address their structural disparities. We evaluate our hybrid model in an extensive case study predicting cherry tree phenology in Japan, South Korea and Switzerland. Our approach consistently outperforms both traditional biophysical and machine learning models in predicting blooming dates across years. Additionally, the neural network's adaptability facilitates parameter learning for specific tree varieties, enabling robust generalization to new sites without site-specific recalibration. This hybrid model leverages both biophysical constraints and data-driven flexibility, offering a promising avenue for accurate and interpretable phenology modeling.

Xuwei Tan, Qian Zhao, Yanlan Liu, Xueru Zhang

Drought is one of the most destructive and expensive natural disasters, severely impacting natural resources and risks by depleting water resources and diminishing agricultural yields. Under climate change, accurately predicting drought is critical for mitigating drought-induced risks. However, the intricate interplay among the physical and biological drivers that regulate droughts limits the predictability and understanding of drought, particularly at a subseasonal to seasonal (S2S) time scale. While deep learning has demonstrated the potential to address climate forecasting challenges, its application to drought prediction has received relatively less attention. In this work, we propose a new dataset, DroughtSet, which integrates relevant predictive features and three drought indices from multiple remote sensing and reanalysis datasets across the contiguous United States (CONUS). DroughtSet specifically provides the machine learning community with a new real-world dataset to benchmark drought prediction models and more generally, time-series forecasting methods. Furthermore, we propose a spatial-temporal model SPDrought to predict and interpret S2S droughts. Our model learns from the spatial and temporal information of physical and biological features to predict three types of droughts simultaneously. Multiple strategies are employed to quantify the importance of physical and biological features for drought prediction. Our results provide insights for researchers to better understand the predictability and sensitivity of drought to biological and physical conditions. We aim to contribute to the climate field by proposing a new tool to predict and understand the occurrence of droughts and provide the AI community with a new benchmark to study deep learning applications in climate science.

Minhyuk Song, Sungwon Han, Seungeon Lee, Donghyun Ahn, Jihee Kim, Meeyoung Cha

Recent studies on the urban heat island phenomenon reveal how rapid urbanization intensifies temperature disparities in urban cores, highlighting the need for sustainable urban planning solutions. Analyzing the problems caused by these effects requires high-resolution climate data; however, physical weather stations often lack sufficient regional coverage and resolution. Proposals for alternative methods have attempted to bridge this gap, but they fall short in capturing regional characteristics adequately or necessitate obtaining difficult-to-get input data. This research proposes to use satellite data, where the visual spectrum provides rich information about the degree of human development and is easy to obtain, to measure urban air temperature. Our model, UrbanHeat, uses multi-resolution satellite imagery and employs land surface temperature and global climate data as proxy labels to predict air temperature at a granular scale. The results show that the model provides predictions at a much finer scale while showing superior performance in measuring ordinal relationships between points by capturing both local and broad land cover details of the region. Our case studies demonstrate how predictions at high resolution can help protect vulnerable populations from extreme heat (e.g., elders or developing countries) and contribute to sustainable urban development worldwide.

Shuaike Shen, Ke Liu, Muzhi Zhu, Hao Chen

Crystal materials play an important role in the development of society. The discovery of new materials is critical to achieving sustainable development goals (SDGs), such as climate change mitigation, affordable and clean energy, and fostering innovation in industry and infrastructure. Recent advances in deep learning for crystal property prediction have accelerated material discovery, but these methods typically rely on labeled data, which is often limited and varies across different properties. This limitation hinders the full utilization of the vast amount of unlabeled data in materials science. To overcome this challenge, we introduce an unsupervised Denoising Pre-training Framework (DPF) tailored for crystal structures. DPF trains a model to reconstruct the original crystal structure by recovering the masked atom types, perturbed atom positions, and perturbed crystal lattices. Through pre-training, models learn the intrinsic features of crystal structures and capture the key features influencing crystal properties. We pre-train models on a dataset of 380,743 unlabeled crystal structures and fine-tune them on downstream property prediction tasks. Extensive experiments demonstrate the effectiveness of our framework, showing its potential to significantly advance material science and contribute to the development of society by accelerating the discovery of materials crucial for sustainable technologies.

Julia Peters, Anja Neumann, Marco Jaeger, Lukas Gienapp, Josefine Umlauft

Rapidly changing climate conditions and the increase in extreme events are posing severe challenges to human life and infrastructure, requiring sophisticated analytical capabilities for hazard prediction and disaster risk management. Earth System Data Cubes (ESDCs) have become an essential tool in Earth System Sciences (ESS) by organizing large-scale, multivariate environmental datasets into a structured, scalable and analysis-ready format. However, modern machine learning techniques are not yet being utilized to their full potential on ESDCs. This is due to the lack of proper tooling, domain-specific challenges, and high barriers of entry for practitioners. We introduce ml4xcube, an open-source Python framework designed to assist ESS domain experts in applying ML techniques on ESDCs for advanced analysis and prediction of environmental variables and impacts. Through a comprehensive suite of tools, it addresses specific challenges associated with the nature of ESS data, such as the non-uniform data distribution due to dynamic gaps, or spatio-temporal autocorrelation of environmental variables. Due to its modular architecture, it covers the complete analysis process, from data exploration, and preparation, to model development, result interpretation and evaluation. With support for distributed computing, it handles large ESDC datasets efficiently. In order to ease the adoption it includes extensive documentation and tutorial notebooks. We demonstrate ml4xcube's capabilities through three examples, showcasing its potential and capabilities for integrating machine learning with ESDC data.

Ellen M. Considine, Rachel C. Nethery, Gregory A. Wellenius, Francesca Dominici, Mauricio Tec

A key strategy in societal adaptation to climate change is using alert systems to prompt preventative action and reduce the adverse health impacts of extreme heat events. This paper implements and evaluates reinforcement learning (RL) as a tool to optimize the effectiveness of such systems. Our contributions are threefold. First, we introduce a new publicly available RL environment enabling the evaluation of the effectiveness of heat alert policies to reduce heat-related hospitalizations. The rewards model is trained from a comprehensive dataset of historical weather, Medicare health records, and socioeconomic/geographic features. We use scalable Bayesian techniques tailored to the low-signal effects and spatial heterogeneity present in the data. The transition model uses real historical weather patterns enriched by a data augmentation mechanism based on climate region similarity. Second, we use this environment to evaluate standard RL algorithms in the context of heat alert issuance. Our analysis shows that policy constraints are needed to improve RL's initially poor performance. Third, a post-hoc contrastive analysis provides insight into scenarios where our modified heat alert-RL policies yield significant gains/losses over the current National Weather Service alert policy in the United States.

Nikolaos Ioannis Bountos, Arthur Ouaknine, Ioannis Papoutsis, David Rolnick

Forests are vital to ecosystems, supporting biodiversity and essential services, but are rapidly changing due to land use and climate change. Understanding and mitigating negative effects requires parsing data on forests at global scale from a broad array of sensory modalities, and using them in diverse forest monitoring applications. Such diversity in data and applications can be effectively addressed through the development of a large, pre-trained foundation model that serves as a versatile base for various downstream tasks. However, remote sensing modalities, which are an excellent fit for several forest management tasks, are particularly challenging considering the variation in environmental conditions, object scales, image acquisition modes and spatio-temporal resolutions, etc. With that in mind, we present the first unified Forest Monitoring Benchmark (FoMo-Bench), carefully constructed to evaluate foundation models with such flexibility. FoMo-Bench consists of 15 diverse datasets encompassing satellite, aerial, and inventory data, covering a variety of geographical regions, and including multispectral, red-green-blue, synthetic aperture radar and LiDAR data with various temporal, spatial and spectral resolutions. FoMo-Bench includes multiple types of forest-monitoring tasks, spanning classification, segmentation, and object detection. To enhance task and geographic diversity in FoMo-Bench, we introduce TalloS, a global dataset combining satellite imagery with ground-based annotations for tree species classification across 1,000+ categories and hierarchical taxonomic levels. Finally, we propose FoMo-Net, a pre-training framework to develop foundation models with the capacity to process any combination of commonly used modalities and spectral bands in remote sensing. This work aims to inspire research collaborations between machine learning and forest biology researchers in exploring scalable multi-modal and multi-task models for forest monitoring and beyond. All code, data and appendices are published in the repository and on ArXiv.

Zhe Zhao, Pengkun Wang, Haibin Wen, Shuang Wang, Liheng Yu, Yang Wang

Time series forecasting plays a crucial role in domains such as finance, healthcare, and climate science. However, as modern time series data become increasingly complex, featuring high dimensionality, intricate spatiotemporal dependencies, and multi-scale evolutionary patterns, traditional analytical methods and existing predictive models face significant challenges. Although Large Language Models (LLMs) excel in capturing long-range dependencies, they still struggle with multi-scale dynamics and seasonal patterns. Moreover, while LLMs' semantic representation capabilities are rich, they often lack explicit alignment with the numerical patterns and temporal structures of time series data, leading to limitations in predictive accuracy and interpretability. To address these challenges, this paper proposes a novel framework, STEM-LTS (Semantic-TEmporal Modeling for Large-scale Time Series). STEM-LTS enhances the ability to capture complex spatiotemporal dependencies by integrating time series decomposition techniques with LLM-based modeling. The semantic-temporal alignment mechanism within the framework significantly improves LLMs' ability to interpret and forecast time series data. Additionally, we develop an adaptive multi-task learning strategy to optimize the model's performance across multiple dimensions. Through extensive experiments on various real-world datasets, we demonstrate that STEM-LTS achieves significant improvements in prediction accuracy, robustness to noise, and interpretability. Our work not only advances LLM-based time series analysis but also offers new perspectives on handling complex temporal data.