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Remote Sensing
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lehaifeng/DASNet
공식 코드https://github.com/lehaifeng/RSI-CB
공식 코드RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data
https://github.com/likyoo/ban
공식 코드A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection
https://github.com/likyoo/open-cd
공식 코드Changer: Feature Interaction is What You Need for Change Detection
https://github.com/links-ads/igarss-fmars
공식 코드FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models
https://github.com/links-ads/igarss-spada
공식 코드Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
https://github.com/ltdung/snt-sentry
공식 코드Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models
https://github.com/oucailab/msfmamba
공식 코드https://github.com/peeples-lab/xai_analysis
공식 코드https://github.com/perfect-you/sdacd
공식 코드An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection
https://github.com/raoofnaushad/eurosat_lulc
공식 코드Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study
https://github.com/rmkemker/EarthMapper
공식 코드EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery
https://github.com/schmitt-muc/SEN12MS
공식 코드Remote Sensing Image Classification with the SEN12MS Dataset
https://github.com/sstary/ssrs
공식 코드SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
https://github.com/stevinc/TheColorOutOfSpace
공식 코드The color out of space: learning self-supervised representations for Earth Observation imagery
https://github.com/summitgao/msfmamba
공식 코드https://github.com/summitgao/ss-mae
공식 코드SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification
https://github.com/toelt-llc/FlightScope_Bench
공식 코드https://github.com/vitae-transformer/mtp
공식 코드MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
https://github.com/vitae-transformer/rsp
공식 코드https://github.com/vitae-transformer/vitae-transformer-remote-sensing
공식 코드https://github.com/vladan-stojnic/CMC-RSSR
공식 코드Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding
https://github.com/walking-shadow/Official_Remote_Sensing_Mamba
공식 코드https://github.com/wgcban/changeformer
공식 코드https://github.com/woodfrog/heat
공식 코드HEAT: Holistic Edge Attention Transformer for Structured Reconstruction
https://github.com/x-ytong/DPA
공식 코드Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery
https://github.com/xwmaxwma/rssegmentation
공식 코드https://github.com/yeshwanth95/pix2poly
공식 코드https://github.com/yueatsprograms/ttt_cifar_release
공식 코드Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
https://github.com/yueatsprograms/ttt_imagenet_release
공식 코드Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
https://github.com/zhu-xlab/unida
공식 코드Universal Domain Adaptation for Remote Sensing Image Scene Classification
https://gitlab.com/sbonnefoy/siamese_net_change_detection
공식 코드https://github.com/Bobholamovic/CDLab
https://github.com/Bobholamovic/ESCNet
https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.