Papers Earth Observation
“Earth Observation” 태그가 달린 논문 518편 · 필터 해제
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
Most existing remote sensing instance segmentation approaches are designed for close-vocabulary prediction, limiting their ability to recognize novel categories or generalize across datasets. This restricts their applica…
Earth ObservationInstance SegmentationSegmentationSemantic SegmentationHigh-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data
Wildfires are increasing in intensity and severity at an alarming rate. Recent advances in AI and publicly available satellite data enable monitoring critical wildfire risk factors globally, at high resolution and low la…
Earth ObservationTESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis
Satellite remote sensing (RS) enables a wide array of downstream Earth observation (EO) applications, including climate modeling, carbon accounting, and strategies for conservation and sustainable land use. We present TE…
Earth ObservationSelf-Supervised LearningTowards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition
Foundation models are rapidly transforming Earth Observation data mining by enabling generalizable and scalable solutions for key tasks such as scene classification and semantic segmentation. While most efforts in the ge…
Earth ObservationKnowledge DistillationScene ClassificationSemantic SegmentationSpiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing
We present the first theoretical framework for applying spiking neural networks (SNNs) to synthetic aperture radar (SAR) interferometric phase unwrapping. Despite extensive research in both domains, our comprehensive lit…
Earth ObservationVideo Compression for Spatiotemporal Earth System Data
Large-scale Earth system datasets, from high-resolution remote sensing imagery to spatiotemporal climate model outputs, exhibit characteristics analogous to those of standard videos. Their inherent spatial, temporal, and…
Earth ObservationVideo CompressionMulti-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study
The exponential growth of Low Earth Orbit (LEO) satellites has revolutionised Earth Observation (EO) missions, addressing challenges in climate monitoring, disaster management, and more. However, autonomous coordination …
Earth ObservationManagementMulti-agent Reinforcement Learningreinforcement-learning+2Scaling-Up the Pretraining of the Earth Observation Foundation Model PhilEO to the MajorTOM Dataset
Today, Earth Observation (EO) satellites generate massive volumes of data, with the Copernicus Sentinel-2 constellation alone producing approximately 1.6TB per day. To fully exploit this information, it is essential to p…
Density EstimationEarth ObservationregressionSemantic SegmentationAtomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars
The growing number of Earth observation satellites has led to increasingly diverse remote sensing data, with varying spatial, spectral, and temporal configurations. Most existing models rely on fixed input formats and mo…
Earth ObservationDeep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis
This paper presents a novel methodology for generating realistic abundance maps from hyperspectral imagery using an unsupervised, deep-learning-driven approach. Our framework integrates blind linear hyperspectral unmixin…
BenchmarkingData AugmentationEarth ObservationHyperspectral Unmixing+1CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities
Canada experienced in 2023 one of the most severe wildfire seasons in recent history, causing damage across ecosystems, destroying communities, and emitting large quantities of CO2. This extreme wildfire season is sympto…
Deep LearningEarth ObservationFLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping
The growing availability of high-quality Earth Observation (EO) data enables accurate global land cover and crop type monitoring. However, the volume and heterogeneity of these datasets pose major processing and annotati…
Earth ObservationMulti-Task LearningFPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review
New UAV technologies and the NewSpace era are transforming Earth Observation missions and data acquisition. Numerous small platforms generate large data volume, straining bandwidth and requiring onboard decision-making t…
Decision MakingEarth ObservationTraining-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks
Earth observations from low Earth orbit satellites provide vital information for decision makers to better manage time-sensitive events such as natural disasters. For the data to be most effective for first responders, l…
Change DetectionEarth ObservationShort-Term Power Demand Forecasting for Diverse Consumer Types to Enhance Grid Planning and Synchronisation
Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and…
Demand ForecastingEarth Observationfeature selectionLoad ForecastingGeospatial Foundation Models to Enable Progress on Sustainable Development Goals
Foundation Models (FMs) are large-scale, pre-trained AI systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They promise…
BenchmarkingEarth ObservationBeyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond na\"ive upsampling. This has particular relevance for Earth observation missions like Sentine…
Earth ObservationImage Super-ResolutionLand Cover ClassificationSuper-ResolutionGeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
Ultra-high-resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR…
8kAvgEarth ObservationLarge Language Model+2DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding
Multimodal large language models have demonstrated remarkable capabilities in visual understanding, but their application to long-term Earth observation analysis remains limited, primarily focusing on single-temporal or …
BenchmarkingChange DetectionEarth ObservationQuestion AnsweringRemoteSAM: Towards Segment Anything for Earth Observation
We aim to develop a robust yet flexible visual foundation model for Earth observation. It should possess strong capabilities in recognizing and localizing diverse visual targets while providing compatibility with various…
AttributeEarth ObservationReferring ExpressionReferring Expression Segmentation