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Earth Observation

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Earth Observation (EO) refers to the use of remote sensing technologies to monitor land, marine (seas, rivers, lakes) and atmosphere. Satellite-based EO relies on the use of satellite-mounted payloads to gather imaging data about the Earth’s characteristics. The images are then processed and analyzed in order to extract different types of information that can serve a very wide range of applications and industries.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/VoyagerXvoyagerx/InstructSAM

공식 코드

InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition

https://github.com/WangLibo1995/GeoSeg

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PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery

https://github.com/Z-Zheng/FarSeg

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Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

https://github.com/agri-hub/adc

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Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

https://github.com/agri-hub/cotton-phenology-dataset

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Fuzzy clustering for the within-season estimation of cotton phenology

https://github.com/aiandglobaldevelopmentlab/eo-poverty-review

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A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty

https://github.com/aidotse/DecentralizedAI

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Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellites

https://github.com/aimiokab/misr-s2

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Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series

https://github.com/aleksispi/ml-cloud-opt-thick

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Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI

https://github.com/allenai/lfmc

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High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data

https://github.com/amazon-science/earth-forecasting-transformer

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Earthformer: Exploring Space-Time Transformers for Earth System Forecasting

https://github.com/aspiaspace/earthpt

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EarthPT: a time series foundation model for Earth Observation

https://github.com/biasvariancelabs/aitlas

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AiTLAS: Artificial Intelligence Toolbox for Earth Observation

https://github.com/biasvariancelabs/aitlas-arena

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Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification

https://github.com/bipulneupane/dualskipunets

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Dual skip connections in U-Net, ResUnet and U-Net3+ for remote extraction of buildings

https://github.com/blutjens/eie-earth-public

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Generating Physically-Consistent Satellite Imagery for Climate Visualizations

https://github.com/burakekim/mapinwild

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MapInWild: A Remote Sensing Dataset to Address the Question What Makes Nature Wild

https://github.com/cassiofragadantas/LUCAS_LULC

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Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping

https://github.com/chenhongruixuan/bright

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BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

https://github.com/chenyizi086/wu.2023.sigspatial

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Cross-attention Spatio-temporal Context Transformer for Semantic Segmentation of Historical Maps

https://github.com/chnja/rdpnet

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RDP-Net: Region Detail Preserving Network for Change Detection

https://github.com/circlelzy/mtkd-cd

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JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework

https://github.com/cjerzak/causalimages-software

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Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities

https://github.com/ckaparakis/wf-unet

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WF-UNet: Weather Fusion UNet for Precipitation Nowcasting

https://github.com/clarkcga/gfm-gap-filling-td

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Foundation Models for Generalist Geospatial Artificial Intelligence

https://github.com/cyx669521/self-supervised_change_detetction

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Self-supervised Change Detection in Multi-view Remote Sensing Images

https://github.com/dberga/iquaflow-qmr-eo

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QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

https://github.com/dberga/iquaflow-qmr-loss

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QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

https://github.com/deepextremes/txyxai

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Explainable Earth Surface Forecasting under Extreme Events

https://github.com/delair-ai/DISCA

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Interactive Learning for Semantic Segmentation in Earth Observation

https://github.com/dfki-earth-and-space-applications/flood_mapping_feature_space_importance

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On the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches

https://github.com/dhsvendsen/rtm_vi_mcem_inference

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Inference over radiative transfer models using variational and expectation maximization methods

https://github.com/dlr-mf-das/ssl4eo-s12-v1.1

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SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, Updated

https://github.com/dyzy41/lenet

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A Remote Sensing Image Change Detection Method Integrating Layer Exchange and Channel-Spatial Differences

https://github.com/e-sensing/sits

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Satellite Image Time Series Analysis for Big Earth Observation Data

https://github.com/earth-intelligence-lab/localizedweathergnn

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Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.