Isotopic ratios of hydrogen and oxygen in water serve as powerful tracers of the Earth's hydrological cycle, offering insights into the origins of water vapor, large-scale atmospheric circulation, and moisture transport dynamics. However, integrating water isotopes into fully coupled global climate models (GCMs) is both scientifically and technically challenging due to the complex interactions between water isotopes and the atmosphere, hydrosphere, and cryosphere, as well as the extensive modifications to model physics and dynamics. As a result, most GCMs lack support for isotopes and even the few existing isotope-enabled GCMs still remain highly expensive to run, significantly limiting their usability. Machine learning (ML) offers promising opportunities to emulate the complex process as powerful mathematical approximators. The water isotope fields from the emulators bring potential for applications in isotope-unenabled GCMs. However, the absence of a publicly available ML-ready dataset has hindered the development of robust ML-based emulators. To address this gap, we introduce IsoSim, the first ML-ready benchmark dataset designed to facilitate the development of ML emulators for water isotopes in GCMs. This dataset includes global climate variables and water isotope fields across three spatial dimensions (latitude, longitude, and height) from isotope-enabled GCM simulations, spanning 500 years at a monthly resolution. We also include different climatic scenarios and a diverse set of learning-based emulators to carry out extensive evaluations and build the benchmarks. The dataset and results serve as reference points to compare machine learning models' ability in approximating complex physical relationships.

Probabilistic time-series forecasting plays a vital role in decisionmaking under uncertainty, especially in applications like solar energy, where forecast reliability directly impacts energy planning and grid stability. While recent models have improved in generating predictive distributions rather than single-point estimates, existing evaluations often focus on average performance and overlook how model quality varies across different real-world scenarios. In solar energy monitoring, for example, the difficulty of forecasting can change significantly due to atmospheric variability, sensor types, and climate conditions. This work addresses the need for scenario-aware evaluation of probabilistic models by benchmarking state-of-the-art forecasting methods using SolarCube-a large-scale solar radiation dataset spanning diverse regions, cloud regimes, and environmental conditions. We define structured "easy" and "hard" cases across four scenarios and examine how different probabilistic model families (e.g., diffusion, VAE, flow-based) capture uncertainty under these conditions. Our goal is to move beyond overall metrics and reveal how model reliability changes across scenarios that are critical for downstream applications.

Coincident satellite data refer to remote sensing observations from different platforms that capture the same geographic location within a short temporal window. Such data enable multi-modal and multi-view analysis—particularly for dynamic systems like the Arctic, where images captured just hours apart can reflect vastly different conditions (e.g., moving sea ice), complicating data integration. However, acquiring coincident data from different satellite platforms remains labor-intensive and computationally demanding. Finding coincident data is challenging due to separate query systems, inconsistent file formats, and the lack of built-in tools to compute cross-platform time differences. Researchers often settle for loosely aligned observations, compromising temporal alignment precision and analysis quality. To address this challenge, we introduce the Coincident Data Discovery Engine (CoDD), a platform designed to facilitate the discovery and access to spatially and temporally coincident remote sensing data across multiple platforms. CoDD provides data from seven widely used satellite missions spanning optical, SAR, and LiDAR modalities, with a maximum temporal gap of 72 hours and temporal granularity down to one second. The platform features an intuitive user interface that enables efficient querying, visualization, and download of coincident data. CoDD is already supporting diverse ongoing research such as Arctic change monitoring, satellite product validation, and ground truth generation for super-resolution models.

Spatial pattern mining (SPM) aims to detect geographic locations or areas that present interesting, nontrivial, and potentially useful patterns. Traditional formulations of point-based SPM tasks are mainly based on true observations, which tend to have limited spatial coverage, availability, and timeliness. While machine learning (ML) has the potential to extend the range of usable data, the uncertainty of model-predicted labels presents new challenges for their usability in the SPM context. This paper formulates the task of ML-enabled SPM using predicted labels by ML models. Given the ever-expanding family of spatial patterns, we consider four widely-adopted patterns – hotspots, co-locations, mixture patterns, and spatial outliers – to scope our study to make the discussion concrete. We develop soft-label versions of SPM algorithms that can directly execute on uncertain predictions generated by ML models. Additionally, we evaluate the ML-enabled SPM results for both categorical and real-valued datasets across a spectrum of prediction quality. The results show that certain spatial patterns such as multinomial scan statistic-based mixture patterns and normal-model-based hotspots can more robustly maintain the detection quality at different error levels, while others such as spatial outliers are more sensitive to incorrect predictions. This provides helpful guidance on using learning-based predictions for SPM.

Although standards of living are increasing rapidly worldwide, a considerable segment of the global population continues to live in poverty. Local governments and decision makers urgently need actionable fine-scale poverty maps to know the locations of the low income population for operational resource distribution. However, most existing studies focus on coarse-resolution poverty maps (e.g., county level) and offer limited information to help deliver the resources to the right locations. Moreover, coarse-resolution maps generated by machine learning models are often trained on higher-level economic statistics that have greater availability. However, such labels at the fine-scale remain very scarce, and existing maps are commonly based on household-level visits that are highly expensive and time-consuming, making them only available in a limited number of cities. We develop a cost-effective approach to tackle the challenge. First, we design a multi-view training data construction approach using data from both street views and very-high-resolution satellite images. Next, we integrate different types of foundation models including the general-purpose vision transformer ViT and the segmentation-focused SegFormer for training and map generation in new cities. Via the use of pretrained large models, the goal is to enhance the generalizability with a smaller amount of samples. To validate the approach, we carried out a case study in Ghana with the cities of Accra, Kumasi, and Tamale. The results showed the effectiveness of the cost-effective approach in capturing low-income areas with unique characteristics, and the foundation models also demonstrated enhanced ability in generalization with smaller training data sizes.

Airborne LiDAR point clouds record three-dimensional structures of ground surfaces with high precision, and have been widely used to identify geospatial objects, facilitating the understanding of the distribution and changing dynamics of the environment. Detection can be complicated by the complex structures of ground objects and noises in LiDAR point clouds. Related work has explored the use of deep learning techniques such as YOLO in detecting geospatial objects (e.g., building footprints) on both optical imagery and LiDAR point clouds. However, deep networks are data hungry and there are often limited labeled samples available for many geospatial object mapping tasks, making it difficult for the models to generalize to unseen test regions. This paper describes the framework used in the 11th SIGSPATIAL Cup Competition (GIS CUP 2022), which received the top-3 performance. Our framework incorporates domain knowledge to reduce the difficulty of learning and the model's reliance on large training sets. Specifically, we present knowledge-informed feature generation and filtering based on morphological characteristics to improve the generalizability of learned features. Then, we use a deep segmentation backbone (U-Net) with training- and test-time augmentation to generate preliminary candidates for building footprints. Finally, we utilize domain rules (e.g., geometric properties) to regularize and filter the detections to create the final map of building footprints. Experiment results show that the strategies can effectively improve detection results in different landscapes.