Papers with Code 아카이브
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://gitlab.com/tarasiewicztomasz/deepsent
공식 코드Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images
https://github.com/Abysinikal/ML_FOR_RS
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
https://github.com/Abysinikal/S2_CLS
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
https://github.com/Bobholamovic/CDLab
https://github.com/CAPTAIN-WHU/DOTA_devkit
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
https://github.com/CIA-Oceanix/DINAE_keras
End-to-end learning of energy-based representations for irregularly-sampled signals and images
https://github.com/CIA-Oceanix/DinAE
End-to-end learning of energy-based representations for irregularly-sampled signals and images
https://github.com/ESA-PhiLab/Major-TOM
https://github.com/I-Hope-Peace/ChangeDetectionRepository
https://github.com/I-Hope-Peace/KPCAMNet
https://github.com/IonutMotoi/CutPasteSatSeg
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
https://github.com/MonashTS/tempo
Proximity Forest: An effective and scalable distance-based classifier for time series
https://github.com/NikoKVCS/proximity_forest
Proximity Forest: An effective and scalable distance-based classifier for time series
https://github.com/PaddlePaddle/PaddleDetection
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
https://github.com/PaddlePaddle/PaddleRS
https://github.com/PaddlePaddle/PaddleScience
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
https://github.com/PatrickTUM/SEN12MS-CR-TS
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
https://github.com/VSAnimator/stgan
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
https://github.com/YeLyuUT/semantic_segmentation
Bidirectional Multi-scale Attention Networks for Semantic Segmentation of Oblique UAV Imagery
https://github.com/canturan10/image.classification.on.EuroSAT
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
https://github.com/canturan10/satellighte
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
https://github.com/chenhongruixuan/changedetectionrepository
https://github.com/come880412/CTGAN
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
https://github.com/czarmanu/deeplab-lakeice-webcams
Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring
https://github.com/czarmanu/sentinel_lakeice
Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring
https://github.com/dotnet54/multivariate-measures
Proximity Forest: An effective and scalable distance-based classifier for time series
https://github.com/earthnet2021/earthnet-model-intercomparison-suite
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
https://github.com/ec-jrc/GHS-S2Net
Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery
https://github.com/gaozhihan/PreDiff
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
https://github.com/gomezzz/MSMatch
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
https://github.com/hewanshrestha/why-self-supervision-in-time
https://github.com/ignf/flair-2
https://github.com/ignf/flair-2-ai-challenge
https://github.com/jack-bo1220/awesome-remote-sensing-foundation-models
https://github.com/jessemelpolio/Faster_RCNN_for_DOTA
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.