https://github.com/shendb2022/htd-mamba
공식 코드HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space Model
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/sizhuoli/Domain_adaptive_regression_GOL
공식 코드Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring
https://github.com/spaceml-org/m3leo
공식 코드https://github.com/stevinc/TheColorOutOfSpace
공식 코드The color out of space: learning self-supervised representations for Earth Observation imagery
https://github.com/sumn2u/lulc-mapping
공식 코드Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer Learning
https://github.com/supreethmv/q-seg
공식 코드Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation
https://github.com/tianfang-zhang/agpcnet
공식 코드AGPCNet: Attention-Guided Pyramid Context Networks for Infrared Small Target Detection
https://github.com/totony4real/deeprandomfeatures
공식 코드Deep Random Features for Scalable Interpolation of Spatiotemporal Data
https://github.com/vishalned/MMEarth-data
공식 코드MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning
https://github.com/vishalned/MMEarth-train
공식 코드MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning
https://github.com/vmarsocci/pangaea-bench
공식 코드PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
https://github.com/walking-shadow/Semantic-guidance-and-spatial-localization-network
공식 코드https://github.com/waterdisappear/SARATR-X
공식 코드SARATR-X: Toward Building A Foundation Model for SAR Target Recognition
https://github.com/waterdisappear/nudt4mstar
공식 코드NUDT4MSTAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild
https://github.com/wgcban/SemiCD
공식 코드Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
https://github.com/whu-sigma/hypersigma
공식 코드HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model
https://github.com/whu-usi3dv/deepaat
공식 코드DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based Mapping
https://github.com/wtseng530/avlocalization
공식 코드CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data
https://github.com/xdeng7/scale-aware_da
공식 코드Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery
https://github.com/xiaoaoran/awesome-rsfms
공식 코드Foundation Models for Remote Sensing and Earth Observation: A Survey
https://github.com/xiong-zhitong/geolb-siglip
공식 코드GeoLangBind: Unifying Earth Observation with Agglomerative Vision-Language Foundation Models
https://github.com/xmba15/ai4eo_map_your_city
공식 코드Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge
https://github.com/yichiac/crop-type-transfer-learning
공식 코드On the Generalizability of Foundation Models for Crop Type Mapping
https://github.com/yihshe/ai-refined-rtm
공식 코드From Spectra to Biophysical Insights: End-to-End Learning with a Biased Radiative Transfer Model
https://github.com/zarhenry96/local-qtrained-svms
공식 코드Local Binary and Multiclass SVMs Trained on a Quantum Annealer
https://github.com/zhaoyutim/ts-satfire
공식 코드TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction
https://github.com/zhu-xlab/ChatEarthNet
공식 코드ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
https://github.com/zhu-xlab/aio2
공식 코드https://github.com/zhu-xlab/copernicus-fm
공식 코드Towards a Unified Copernicus Foundation Model for Earth Vision
https://github.com/zhu-xlab/dino-mm
공식 코드Self-supervised Vision Transformers for Joint SAR-optical Representation Learning
https://github.com/zhu-xlab/dofa
공식 코드Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
https://github.com/zhu-xlab/softcon
공식 코드Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining
https://github.com/zhu-xlab/ssl4eo-review
공식 코드https://github.com/zhu-xlab/ssl4eo-s12
공식 코드https://github.com/zytx121/awesome-vlgfm
공식 코드https://gitlab.com/meehai/neo-vcl-iccv-2023
공식 코드Multi-Task Hypergraphs for Semi-supervised Learning using Earth Observations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.