satellite remote sensing

Continuous monitoring of tropical cyclones is hindered by the long revisit intervals of microwave satellite imagery, sensor heterogeneity, irregular spatiotemporal sampling, and geographic misalignment across observations. To address these challenges, this work proposes a multi-source generative model that leverages self-supervised learning to perform spatiotemporal interpolation of heterogeneous microwave and infrared satellite images, enabling the first joint modeling of unaligned, non-uniformly sampled data. The approach incorporates a random source masking and reconstruction strategy to enhance generalization. Experiments demonstrate that the model significantly outperforms supervised baselines, as measured by reduced Continuous Ranked Probability Score (CRPS), with performance further improved through infrared data fusion. The generated outputs exhibit power spectra closely matching real observations, and their ensemble mean rivals that of deterministic forecast models.

Satellite-derived 3D geospatial products—including point clouds, digital surface models (DSMs), and 3D meshes—lack a standardized, quantitative resolution metric. Method: This study proposes the first automated resolution assessment framework tailored to heterogeneous satellite-based 3D products. It integrates 3D metric analysis, robust point cloud registration with error mapping, surface geometric fidelity modeling, and a multi-scale resolution characterization workflow, enabling fully automatic comparison against high-accuracy airborne LiDAR reference data. Results: Validated across diverse satellite 3D datasets of varying sources and quality levels, the framework delivers quantitative resolution metrics, enables cross-source comparability, and supports accuracy闭环 validation for large-scale 3D scene modeling. It significantly enhances objectivity, reproducibility, and performance traceability of 3D geospatial information products.

This study addresses the lack of systematic best practices in large-scale Earth observation (EO) mapping, which often introduces errors during data preprocessing, model training, inference deployment, and validation, thereby compromising the reliability and scientific credibility of map products. To remedy this, we propose the first end-to-end best practice framework for EO mapping, encompassing the entire workflow from satellite data acquisition to operational map delivery. The framework integrates six core components: EO data infrastructure, preprocessing, machine learning dataset construction, uncertainty quantification, map production and dissemination, and independent validation. Emphasizing the interdependence of these stages, it embeds uncertainty quantification and independent validation as integral elements. By synergizing machine learning, distributed computing, and geospatial validation techniques, the framework establishes a reproducible and scalable mapping pipeline that substantially enhances the quality, consistency, and scientific rigor of EO-derived maps, supported by open-source resources to foster community adoption.

Remote sensing simulation faces challenges including heavy reliance on LiDAR data, extensive manual intervention, and difficulty in cross-spectral band modeling. Method: This paper proposes an end-to-end, physically grounded simulation framework driven solely by commercial satellite imagery. It automatically constructs 3D geometry from digital surface models (DSMs), infers material properties by fusing multi-source satellite imagery, and integrates physics-based rendering with radiative transfer modeling across a broad spectral range (200 nm–20 μm) to achieve fully automated, high-fidelity reconstruction of terrain, buildings, vegetation, and dynamic vehicles. Contribution/Results: To our knowledge, this is the first framework to eliminate LiDAR dependence and generate novel regional scenes without any manual intervention. Experiments demonstrate substantial reduction in modeling cost, full-spectrum support—from ultraviolet (UV) to long-wave infrared (LWIR)—for algorithm development and processing pipeline validation, and significant improvements in realism, generalizability, and scalability of geospatial scene simulation.

This work addresses the challenge that Python scripts authored by remote sensing scientists often lack scalability for large-scale satellite data processing. To bridge this gap, the authors propose an intelligent agent system that automatically translates existing Python geospatial workflows into efficient Apache Spark programs without requiring users to learn new frameworks. The system innovatively enhances the Scala-based RDPro library’s compatibility with large language models through structured API wrappers, function alias mapping, and an error-log-driven repair mechanism. Built upon LangGraph, it implements a staged pipeline for code generation and localized correction. Experiments on real-world geospatial workflows demonstrate that the approach correctly and efficiently processes massive remote sensing datasets, substantially improving scalability while preserving the original workflow semantics.

To address rare, transient, and precisely localized dynamic scientific phenomena—such as volcanic eruption plumes—this paper proposes an onboard real-time perception–decision–response closed-loop framework. Methodologically, it fuses forward-looking satellite imagery with lightweight CNNs and traditional machine learning models for edge-based plume detection, and integrates multi-objective trajectory planning to autonomously generate optimal high-resolution sensor pointing paths. Its key contribution lies in the first deep integration of event-driven real-time detection and online trajectory planning on an edge computing platform, enabling fully autonomous onboard observation scheduling. Simulation results demonstrate that, compared to baseline approaches, the framework achieves over a tenfold increase in scientific return, while total inference and planning latency remains below typical revisit intervals—substantially improving capture probability and data value for sparse dynamic events.

To address low efficiency and poor consistency in glacier boundary extraction across large-scale, multi-temporal, and heterogeneous glacial environments, this study proposes GeoSAM-Glacier, the first semi-automated workflow for glacier mapping. The method integrates multi-temporal Sentinel-2 surface reflectance composites, spectral pre-screening using NDWI/NDSI, RGB-rendered prompting for segmentation, and post-processing constrained by terrain slope and radiometric physics. Its key innovation lies in the first adaptation of GeoSAM to glacier remote sensing interpretation, enabling synergistic integration of domain-specific remote sensing priors with foundation model segmentation capabilities. Applied to western Svalbard, the framework achieves high spatiotemporal consistency in annual glacier outline delineation, with excellent accuracy for major ice bodies; residual errors in small targets primarily stem from water bodies and cast shadows. Manual correction effort is reduced by over 70%. The framework demonstrates strong transferability across regions and sensor platforms.

Existing foundation models for Earth observation struggle to effectively incorporate hyperspectral imagery (HSI), while specialized HSI models lack joint pretraining with multimodal remote sensing data. This work proposes a hierarchical Transformer architecture that, for the first time, enables unified pretraining of HSI alongside multispectral and SAR data through spectral tokenization, sensor-specific encoders, and a cross-sensor fusion module. The authors also introduce SpectralEarth-MM, a large-scale co-located multimodal dataset. Leveraging a JEPA-style joint embedding prediction objective, the model achieves state-of-the-art performance on both hyperspectral downstream tasks and general Earth observation benchmarks, significantly enhancing its generalization and multimodal fusion capabilities.

Geospatial foundation models (GFMs) face two critical deployment bottlenecks: lack of automated data processing and model bloat after fine-tuning. This paper proposes an end-to-end geospatial machine learning framework integrating automatic multispectral image annotation, unified data pipeline orchestration, task-aware knowledge distillation, and lightweight model architecture—designed for open-source remote sensing data (e.g., Landsat, Sentinel-2). Our approach reduces model size by 8× and significantly cuts carbon footprint while preserving or improving accuracy: crop segmentation achieves 60.65% mIoU—12 percentage points above state-of-the-art—and matches or exceeds baseline performance in flood mapping and desert locust forecasting. The entire workflow—from raw imagery to web-map integration—is completed within 24 hours, substantially enhancing GFMs’ practicality and real-world deployability.