remote-sensing

Satellite interferometry from ESA's Sentinel-1 shows ground displacement up to 30 centimeters after Venezuela's twin earthquakes, revealing deformation along the San Sebastián fault system.

NASA satellites are providing critical support for earthquake response in Venezuela, capturing data to help assess impacts and guide efforts.

This paper applies topological data analysis to flood detection by extracting topological features from satellite imagery and incorporating them into neural networks, demonstrating improved robustness and interpretability over conventional methods.

A deep learning framework for probabilistic CO2 column retrieval from OCO-2 spectra using Laplace approximations and normalizing flows, achieving faster inference and better uncertainty quantification than traditional methods.

Presents an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries, with a focus on safety and adversarial robustness. The system integrates three agents for intent interpretation, API call generation, and risk management.

A new study in PNAS introduces the concept of an 'urban pulse' measured via remote sensing data, revealing three key vital signs of urbanization that could inform urban planning policy.

OSMGraphCLIP is a model that learns global location embeddings from OpenStreetMap data using a graph-based encoder and contrastive alignment with a spherical-harmonics location encoder. It achieves strong performance across diverse geospatial tasks, often matching or exceeding satellite-based methods.

A lightweight set-based deep learning framework using a transformer is presented for atmospheric compensation in passive long-wave infrared hyperspectral imaging, jointly estimating transmittance, atmospheric path radiance, and downwelling spectrum from multi-range radiance measurements.

This paper introduces a novel uncertainty-aware PINN framework for flood inference from SAR data, addressing 'physics shock' by dynamically relaxing physical constraints in noisy regions. Evaluated on Sen1Floods11, the method achieves a 25% improvement in IoU and provides calibrated uncertainty bounds for operational disaster response.

This paper presents a unified benchmark for composed image retrieval in Earth observation, evaluating vision-language backbones and introducing a change-centric dataset (xView2-CIR) for disaster monitoring, highlighting distinct challenges compared to attribute-based retrieval.

OlmoEarth v1.1 is a new family of satellite imagery analysis models from Allen AI that reduces compute costs by up to 3x while maintaining performance, achieved by decreasing token sequence lengths in transformer-based models.

SENSE is a generative urban building energy modeling framework that synthesizes satellite imagery and energy data using diffusion models, achieving high-fidelity results with reduced labeled data requirements.

RS-Claw proposes an active tool exploration paradigm for remote sensing agents using hierarchical skill trees, enabling on-demand sequential decision-making and achieving up to 86% input token compression while outperforming passive selection baselines on Earth-Bench.

ChangeFlow presents a generative framework for remote sensing change detection that synthesizes change masks in latent space using rectified flow, achieving improved accuracy and robustness through sampling-based prediction ensembling, with an average F1 of 80.4% across four benchmarks.

This paper investigates using large vision-language models for built environment reasoning tasks, such as design suggestions and risk identification, leveraging remote sensing imagery. It evaluates models like InternVL and Qwen, highlighting their potential for supporting smart city decision-making and quantitative reasoning.

This paper evaluates whether geospatial foundation model embeddings like Prithvi-EO improve cross-country crop yield prediction in Sub-Saharan Africa compared to traditional Sentinel-2 features. The study finds that frozen embeddings do not significantly outperform spectral medians under rigorous Leave-One-Country-Out validation, suggesting country-level distribution shift is the primary bottleneck rather than feature representation quality.

This paper presents a Bayesian inverse problem framework for rain field reconstruction using Commercial Microwave Links and Diffusion Model priors, demonstrating improved accuracy over existing baselines.

RemoteZero is a framework that eliminates the need for human-annotated box supervision in geospatial reasoning by leveraging the semantic verification capabilities of multimodal large language models (MLLMs) to enable self-evolving localization from unlabeled remote sensing data.

EyeOnBlue is a remote sensing and AI platform that leverages satellite imagery for analysis and insights from space.

World Resources Institute announces Canopy Height Maps v2, an open-source model and associated world-scale maps designed for mapping global forests with greater precision.