https://github.com/konstantin-a-maslov/towards_global_glacier_mapping
Globally Scalable Glacier Mapping by Deep Learning Matches Expert Delineation Accuracy
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://github.com/kyoukuntaro/FCSN_for_ChangeDetection_IGARSS2018
https://github.com/lizhuohong/buildingmap
https://github.com/lizhuohong/paraformer
https://github.com/maxbeauchamp/2020a_IMT_SSH_mapping_NATL60
End-to-end learning of energy-based representations for irregularly-sampled signals and images
https://github.com/ming71/UCAS-AOD-benchmark
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
https://github.com/moradisten/PForests
Proximity Forest: An effective and scalable distance-based classifier for time series
https://github.com/moradisten/ProximityForests-python
Proximity Forest: An effective and scalable distance-based classifier for time series
https://github.com/ntnu-smallsat-lab/s_l_c_segm_hyp_img
An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite
https://github.com/open-mmlab/mmrotate
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
https://github.com/panpeiwen/abc
AGPCNet: Attention-Guided Pyramid Context Networks for Infrared Small Target Detection
https://github.com/rcdaudt/fully_convolutional_change_detection
https://github.com/rcdaudt/patch_based_change_detection
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
https://github.com/reidfalconer/landcover_classification
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
https://github.com/remicres/otbtf
M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion
https://github.com/satellitevu/satellitevu-aws-disaster-response-hackathon
https://github.com/taegoobot/aerial-object-detector
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
https://github.com/tasnimul-unipotsdam/EuroSAT
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
https://github.com/timeseriesAI/tsai/tree/main/tsai/models
Self-attention for raw optical Satellite Time Series Classification
https://github.com/wanglibo1995/unetformer
https://github.com/waterdisappear/sar-jepa
SARATR-X: Toward Building A Foundation Model for SAR Target Recognition
https://github.com/waterdisappear/sar-kpga
SARATR-X: Toward Building A Foundation Model for SAR Target Recognition
https://github.com/yb23/HySCDG
https://github.com/yeliudev/catnet
Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.