{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/earth-observation/papers/4","list_of":"/task/earth-observation","task":"Earth Observation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":6,"rows_per_page":100,"rows":[301,400],"of":518,"counts":{"archive_papers_tagged":518,"with_a_code_link":216,"where_syntology_ran_a_sample":45,"not_listed_spam_title":0,"listed":518,"listed_where_code_ran":45,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":38,"every_run_a_failure_of_syntologys_instrument":7,"listed_with_a_run_with_no_instrument_failure":38,"listed_every_run_a_failure_of_syntologys_instrument":7,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/earth-observation","prev":"/task/earth-observation/papers/3","next":"/task/earth-observation/papers/5","papers":[{"url":null,"slug":"rapid-adaptation-of-earth-observation","title":"Rapid Adaptation of Earth Observation Foundation Models for Segmentation","date":"2024-09-16","arxiv_id":"2409.09907","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-masked-image-modeling-for","title":"Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing","date":"2024-09-13","arxiv_id":"2409.08885","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-and-generalizability-in","title":"Uncertainty and Generalizability in Foundation Models for Earth Observation","date":"2024-09-13","arxiv_id":"2409.08744","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-mismeasure-of-weather-using-remotely","title":"The Mismeasure of Weather: Using Remotely Sensed Earth Observation Data in Economic Context","date":"2024-09-11","arxiv_id":"2409.07506","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-evaluations-in-data-poor-settings-the","title":"Impact Evaluations in Data Poor Settings: The Case of Stress-Tolerant Rice Varieties in Bangladesh","date":"2024-09-03","arxiv_id":"2409.02201","repositories_listed":0,"syntology":null},{"url":null,"slug":"earth-observation-satellite-scheduling-with","title":"Earth Observation Satellite Scheduling with Graph Neural Networks","date":"2024-08-27","arxiv_id":"2408.15041","repositories_listed":0,"syntology":null},{"url":null,"slug":"satellite-sunroof-high-res-digital-surface","title":"Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping","date":"2024-08-26","arxiv_id":"2408.14400","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-centric-machine-learning-for-earth","title":"Data-Centric Machine Learning for Earth Observation: Necessary and Sufficient Features","date":"2024-08-21","arxiv_id":"2408.11384","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-fusion-of-sentinel-1-and-sentinel-2","title":"A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning","date":"2024-08-16","arxiv_id":"2409.00020","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-using-just-one-example","title":"Segment Using Just One Example","date":"2024-08-14","arxiv_id":"2408.07393","repositories_listed":0,"syntology":null},{"url":null,"slug":"specialized-change-detection-using-segment","title":"Specialized Change Detection using Segment Anything","date":"2024-08-13","arxiv_id":"2408.06644","repositories_listed":0,"syntology":null},{"url":null,"slug":"seg-cyclegan-sar-to-optical-image-translation","title":"Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task","date":"2024-08-11","arxiv_id":"2408.05777","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-earthquake-magnitude-in-sentinel-1","title":"Estimating Earthquake Magnitude in Sentinel-1 Imagery via Ranking","date":"2024-07-25","arxiv_id":"2407.18128","repositories_listed":0,"syntology":null},{"url":null,"slug":"quanv4eo-empowering-earth-observation-by","title":"Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks","date":"2024-07-24","arxiv_id":"2407.17108","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-and-benchmarking-foundation-models","title":"Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI","date":"2024-06-26","arxiv_id":"2406.18295","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-power-ship-detection-in-satellite-images","title":"Low-power Ship Detection in Satellite Images Using Neuromorphic Hardware","date":"2024-06-17","arxiv_id":"2406.11319","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-in-earth-observation-a","title":"Data Augmentation in Earth Observation: A Diffusion Model Approach","date":"2024-06-10","arxiv_id":"2406.06218","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-high-categorical-resolution-land-cover","title":"Global High Categorical Resolution Land Cover Mapping via Weak Supervision","date":"2024-06-02","arxiv_id":"2406.00891","repositories_listed":0,"syntology":null},{"url":null,"slug":"responsible-ai-for-earth-observation","title":"Responsible AI for Earth Observation","date":"2024-05-31","arxiv_id":"2405.20868","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-efficient-disaster-response-via-cost","title":"Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning","date":"2024-05-28","arxiv_id":"2405.17734","repositories_listed":0,"syntology":null},{"url":null,"slug":"serving-economic-prosperity-economic-impact","title":"Serving economic prosperity: economic impact assessments (EIA) on Earth observation-based services and tools by SERVIR","date":"2024-05-24","arxiv_id":"2405.15672","repositories_listed":0,"syntology":null},{"url":null,"slug":"eidos-efficient-imperceptible-adversarial-3d","title":"Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds","date":"2024-05-23","arxiv_id":"2405.14210","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-estimation-in-unsupervised-deep","title":"Confidence Estimation in Unsupervised Deep Change Vector Analysis","date":"2024-05-16","arxiv_id":"2405.09896","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-sensor-self-supervised-training-and","title":"Cross-sensor self-supervised training and alignment for remote sensing","date":"2024-05-16","arxiv_id":"2405.09922","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-aware-text-image-retrieval-for","title":"Knowledge-aware Text-Image Retrieval for Remote Sensing Images","date":"2024-05-06","arxiv_id":"2405.03373","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatio-temporal-swinmae-a-swin-transformer","title":"SatSwinMAE: Efficient Autoencoding for Multiscale Time-series Satellite Imagery","date":"2024-05-03","arxiv_id":"2405.02512","repositories_listed":0,"syntology":null},{"url":null,"slug":"geollm-engine-a-realistic-environment-for","title":"GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots","date":"2024-04-23","arxiv_id":"2404.15500","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-out-of-distribution-earth","title":"Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models","date":"2024-04-19","arxiv_id":"2404.12667","repositories_listed":0,"syntology":null},{"url":null,"slug":"equivariant-imaging-for-self-supervised","title":"Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting","date":"2024-04-19","arxiv_id":"2404.13159","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-data-islands-geographic","title":"Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation","date":"2024-04-14","arxiv_id":"2404.09292","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-national-urban-map-extraction","title":"Automated National Urban Map Extraction","date":"2024-04-09","arxiv_id":"2404.06202","repositories_listed":0,"syntology":null},{"url":null,"slug":"onboard-processing-of-hyperspectral-imagery","title":"Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends","date":"2024-04-09","arxiv_id":"2404.06526","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-satellite-image-time-series","title":"Deep Learning for Satellite Image Time Series Analysis: A Review","date":"2024-04-05","arxiv_id":"2404.03936","repositories_listed":0,"syntology":null},{"url":null,"slug":"satsynth-augmenting-image-mask-pairs-through","title":"SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation","date":"2024-03-25","arxiv_id":"2403.16605","repositories_listed":0,"syntology":null},{"url":null,"slug":"assimilation-of-swot-altimetry-and-sentinel-1","title":"Assimilation of SWOT Altimetry and Sentinel-1 Flood Extent Observations for Flood Reanalysis -- A Proof-of-Concept","date":"2024-03-21","arxiv_id":"2403.14394","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-flood-warning-using-satellite-derived","title":"Early Flood Warning Using Satellite-Derived Convective System and Precipitation Data -- A Retrospective Case Study of Central Vietnam","date":"2024-03-21","arxiv_id":"2403.14395","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-geospatial-approach-to-predicting-desert","title":"A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa","date":"2024-03-11","arxiv_id":"2403.06860","repositories_listed":0,"syntology":null},{"url":null,"slug":"impacts-of-color-and-texture-distortions-on","title":"Impacts of Color and Texture Distortions on Earth Observation Data in Deep Learning","date":"2024-03-07","arxiv_id":"2403.04385","repositories_listed":0,"syntology":null},{"url":null,"slug":"portraying-the-need-for-temporal-data-in","title":"Portraying the Need for Temporal Data in Flood Detection via Sentinel-1","date":"2024-03-06","arxiv_id":"2403.03671","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-autonomous-cooperation-in","title":"Toward Autonomous Cooperation in Heterogeneous Nanosatellite Constellations Using Dynamic Graph Neural Networks","date":"2024-03-01","arxiv_id":"2403.00692","repositories_listed":0,"syntology":null},{"url":null,"slug":"quick-unsupervised-hyperspectral","title":"Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison","date":"2024-02-26","arxiv_id":"2402.16566","repositories_listed":0,"syntology":null},{"url":null,"slug":"solid-waste-detection-in-remote-sensing","title":"Solid Waste Detection, Monitoring and Mapping in Remote Sensing Images: A Survey","date":"2024-02-14","arxiv_id":"2402.09066","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-for-captioning-and","title":"Large Language Models for Captioning and Retrieving Remote Sensing Images","date":"2024-02-09","arxiv_id":"2402.06475","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai4fapar-how-artificial-intelligence-can-help","title":"Ai4Fapar: How artificial intelligence can help to forecast the seasonal earth observation signal","date":"2024-02-08","arxiv_id":"2402.06684","repositories_listed":0,"syntology":null},{"url":null,"slug":"qspecklefilter-a-quantum-machine-learning","title":"QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering","date":"2024-02-02","arxiv_id":"2402.01235","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-latent-space-metric-for-enhancing","title":"A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data","date":"2024-01-30","arxiv_id":"2401.17342","repositories_listed":0,"syntology":null},{"url":null,"slug":"rs-dgc-exploring-neighborhood-statistics-for","title":"RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation","date":"2023-12-29","arxiv_id":"2312.17530","repositories_listed":0,"syntology":null},{"url":null,"slug":"metasegnet-metadata-collaborative-vision","title":"MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images","date":"2023-12-20","arxiv_id":"2312.12735","repositories_listed":0,"syntology":null},{"url":null,"slug":"mapping-housing-stock-characteristics-from","title":"Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean","date":"2023-12-16","arxiv_id":"2312.10306","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-centric-machine-learning-for-geospatial","title":"Better, Not Just More: Data-Centric Machine Learning for Earth Observation","date":"2023-12-08","arxiv_id":"2312.05327","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-physical-parameters-of","title":"Artificial Neural Network for Estimation of Physical Parameters of Sea Water using LiDAR Waveforms","date":"2023-12-05","arxiv_id":"2312.10068","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-deep-learning-for-mapping-forest","title":"Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data","date":"2023-11-20","arxiv_id":"2311.11777","repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-in-data-based-geospatial-modeling","title":"Challenges in data-based geospatial modeling for environmental research and practice","date":"2023-11-18","arxiv_id":"2311.11057","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-precision-floating-point-for-efficient-on","title":"Low-Precision Floating-Point for Efficient On-Board Deep Neural Network Processing","date":"2023-11-18","arxiv_id":"2311.11172","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-models-for-earth-observation-use","title":"Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection","date":"2023-11-10","arxiv_id":"2311.06222","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-ai-for-earth-observation-current","title":"Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities","date":"2023-11-08","arxiv_id":"2311.04491","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-knowledge-transfer","title":"Supervised domain adaptation for building extraction from off-nadir aerial images","date":"2023-11-07","arxiv_id":"2311.03867","repositories_listed":0,"syntology":null},{"url":null,"slug":"standardized-analysis-ready-star-data-cube","title":"Standardized Analysis Ready (STAR) data cube for high-resolution Flood mapping using Sentinel-1 data","date":"2023-11-07","arxiv_id":"2311.14694","repositories_listed":0,"syntology":null},{"url":null,"slug":"forest-aboveground-biomass-estimation-using","title":"Forest aboveground biomass estimation using GEDI and earth observation data through attention-based deep learning","date":"2023-11-06","arxiv_id":"2311.03067","repositories_listed":0,"syntology":null},{"url":null,"slug":"there-are-no-data-like-more-data-datasets-for","title":"There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation","date":"2023-10-30","arxiv_id":"2310.19231","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-dino-emergent-properties-and","title":"Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery","date":"2023-10-05","arxiv_id":"2310.03513","repositories_listed":0,"syntology":null},{"url":null,"slug":"fewshot-learning-on-global-multimodal","title":"Fewshot learning on global multimodal embeddings for earth observation tasks","date":"2023-09-29","arxiv_id":"2310.00119","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-satellite-borne","title":"Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection","date":"2023-09-05","arxiv_id":"2309.02150","repositories_listed":0,"syntology":null},{"url":null,"slug":"ms-net-a-multi-modal-self-supervised-network","title":"MS-Net: A Multi-modal Self-supervised Network for Fine-Grained Classification of Aircraft in SAR Images","date":"2023-08-28","arxiv_id":"2308.14613","repositories_listed":0,"syntology":null},{"url":null,"slug":"saan-similarity-aware-attention-flow-network","title":"SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images","date":"2023-08-28","arxiv_id":"2308.14570","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-technical-factors-to-consider","title":"A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery","date":"2023-08-18","arxiv_id":"2308.09221","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-model-transfer-in-forest","title":"Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images","date":"2023-08-09","arxiv_id":"2308.05005","repositories_listed":0,"syntology":null},{"url":null,"slug":"genco-an-auxiliary-generator-from-contrastive","title":"GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing","date":"2023-07-27","arxiv_id":"2307.14612","repositories_listed":0,"syntology":null},{"url":null,"slug":"poverty-rate-prediction-using-multi-modal","title":"Poverty rate prediction using multi-modal survey and earth observation data","date":"2023-07-21","arxiv_id":"2307.11921","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-impacts-of-crop","title":"Understanding the impacts of crop diversification in the context of climate change: a machine learning approach","date":"2023-07-17","arxiv_id":"2307.08617","repositories_listed":0,"syntology":null},{"url":null,"slug":"sephrnet-generating-high-resolution-crop-maps","title":"SepHRNet: Generating High-Resolution Crop Maps from Remote Sensing imagery using HRNet with Separable Convolution","date":"2023-07-11","arxiv_id":"2307.05700","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-graphical-linear-dynamical-systems","title":"Sparse Graphical Linear Dynamical Systems","date":"2023-07-06","arxiv_id":"2307.03210","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generic-self-supervised-learning-ssl","title":"A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery","date":"2023-06-27","arxiv_id":"2306.15836","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-multi-modal-self-supervised-pre","title":"Joint multi-modal Self-Supervised pre-training in Remote Sensing: Application to Methane Source Classification","date":"2023-06-16","arxiv_id":"2306.09851","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-change-detection-with-semi","title":"Context-Aware Change Detection With Semi-Supervised Learning","date":"2023-06-15","arxiv_id":"2306.08935","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-uncertainties-of-a-chained","title":"Reducing Uncertainties of a Chained Hydrologic-hydraulic Model to Improve Flood Forecasting Using Multi-source Earth Observation Data","date":"2023-06-14","arxiv_id":"2306.10059","repositories_listed":0,"syntology":null},{"url":null,"slug":"over-the-air-federated-learning-in-satellite","title":"Over-the-Air Federated Learning in Satellite systems","date":"2023-06-05","arxiv_id":"2306.02996","repositories_listed":0,"syntology":null},{"url":null,"slug":"improve-state-level-wheat-yield-forecasts-in","title":"Improve State-Level Wheat Yield Forecasts in Kazakhstan on GEOGLAM's EO Data by Leveraging A Simple Spatial-Aware Technique","date":"2023-06-01","arxiv_id":"2306.04646","repositories_listed":0,"syntology":null},{"url":"/paper/cloud-removal-in-remote-sensing-using","slug":"cloud-removal-in-remote-sensing-using","title":"Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models","date":"2023-05-31","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-board-change-detection-for-resource","title":"On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites","date":"2023-05-17","arxiv_id":"2305.10119","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-to-advance-earth","title":"Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward","date":"2023-05-15","arxiv_id":"2305.08413","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-processing-training-data-improves","title":"Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation","date":"2023-04-28","arxiv_id":"2304.14625","repositories_listed":0,"syntology":null},{"url":null,"slug":"physical-knowledge-enhanced-deep-neural","title":"Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction","date":"2023-04-19","arxiv_id":"2304.09376","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-multimodal-data-fusion-occlusion","title":"Explaining Multimodal Data Fusion: Occlusion Analysis for Wilderness Mapping","date":"2023-04-05","arxiv_id":"2304.02407","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-anamorphosis-for-ensemble-kalman","title":"Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations","date":"2023-04-03","arxiv_id":"2304.01058","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-selection-and-clustering-for-federated","title":"Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation","date":"2023-03-25","arxiv_id":"2303.16071","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-algorithms-applied-to-satellite","title":"Quantum algorithms applied to satellite mission planning for Earth observation","date":"2023-02-14","arxiv_id":"2302.07181","repositories_listed":0,"syntology":null},{"url":null,"slug":"we-are-going-to-the-space-part-1-which-device","title":"Reaching the Edge of the Edge: Image Analysis in Space","date":"2023-01-12","arxiv_id":"2301.04954","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-security-for-geoscience-and-remote-sensing","title":"AI Security for Geoscience and Remote Sensing: Challenges and Future Trends","date":"2022-12-19","arxiv_id":"2212.09360","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-flood-detection-on-sar-time","title":"Unsupervised Flood Detection on SAR Time Series","date":"2022-12-07","arxiv_id":"2212.03675","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-semisupervised-contrastive-regression","title":"A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping with Multisensor Satellite Data","date":"2022-12-01","arxiv_id":"2212.00246","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-neural-optimal-interpolation-models","title":"Learning Neural Optimal Interpolation Models and Solvers","date":"2022-11-14","arxiv_id":"2211.07209","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-purpose-neural-architecture-for","title":"A General Purpose Neural Architecture for Geospatial Systems","date":"2022-11-04","arxiv_id":"2211.02348","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-interpretable-deep-semantic-segmentation","title":"An Interpretable Deep Semantic Segmentation Method for Earth Observation","date":"2022-10-23","arxiv_id":"2210.12820","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-label-efficiency-of","title":"Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery","date":"2022-10-13","arxiv_id":"2210.06786","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-we-sense-the-call-of-the-ocean-current","title":"On Advances, Challenges and Potentials of Remote Sensing Image Analysis in Marine Debris and Suspected Plastics Monitoring","date":"2022-10-12","arxiv_id":"2210.06090","repositories_listed":0,"syntology":null},{"url":null,"slug":"earthnets-empowering-ai-in-earth-observation","title":"EarthNets: Empowering AI in Earth Observation","date":"2022-10-10","arxiv_id":"2210.04936","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-geography-generalization-of-machine","title":"Cross-Geography Generalization of Machine Learning Methods for Classification of Flooded Regions in Aerial Images","date":"2022-10-04","arxiv_id":"2210.01588","repositories_listed":0,"syntology":null},{"url":null,"slug":"advances-in-fine-line-of-sight-control-for","title":"Advances in Fine Line-Of-Sight Control for Large Space Flexible Structures","date":"2022-09-27","arxiv_id":"2209.13374","repositories_listed":0,"syntology":null},{"url":null,"slug":"eod-the-ieee-grss-earth-observation-database","title":"EOD: The IEEE GRSS Earth Observation Database","date":"2022-09-26","arxiv_id":"2209.12480","repositories_listed":0,"syntology":null}],"record_sha256":"ff1ec3e7773f42ae8e0e3c05b7499be77797cf16d774d43e4e5145c0be0404f4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}