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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/elliesch/flipnslide

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A Concise Tiling Strategy for Preserving Spatial Context in Earth Observation Imagery

https://github.com/elliotvincent/agriitsc

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Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach

https://github.com/ericssonresearch/uda-4-tsc

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Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark

https://github.com/ericyu97/uad-rs

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Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models

https://github.com/ermongroup/teochat

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TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data

https://github.com/esa-philab/learning_from_unlabeled_data_for_domain_adaptation_for_semantic_segmentation

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Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Images

https://github.com/esa-philab/pyraws

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Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence

https://github.com/esa/snerf

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Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry

https://github.com/fmenat/com-views

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Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning Approach

https://github.com/fmenat/missingviews-study-eo

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Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications

https://github.com/fmenat/multiviewRS-models

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Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications

https://github.com/fmenat/mvlearning

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Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation

https://github.com/fpetitjean/ProximityForest

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Proximity Forest: An effective and scalable distance-based classifier for time series

https://github.com/gastruc/anysat

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AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities

https://github.com/gastruc/omnisat

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OmniSat: Self-Supervised Modality Fusion for Earth Observation

https://github.com/geethen/geeconformal

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Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

https://github.com/haonanguo/deepcl

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DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

https://github.com/haoning724/obbstacking

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OBBStacking: An Ensemble Method for Remote Sensing Object Detection

https://github.com/hsg-aiml/ben-ge

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Ben-ge: Extending BigEarthNet with Geographical and Environmental Data

https://github.com/huangwenwenlili/aca-crnet

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Attentive Contextual Attention for Cloud Removal

https://github.com/ibm/neural-embedding-compression

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Neural Embedding Compression For Efficient Multi-Task Earth Observation Modelling

https://github.com/ipl-uv/xarrayvideo

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Video Compression for Spatiotemporal Earth System Data

https://github.com/jaearly/mil-land-cover-classification

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Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification

https://github.com/jejjohnson/uncertain_gps

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Accounting for Input Noise in Gaussian Process Parameter Retrieval

https://github.com/jimmyma99/sarchat

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SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image Interpretation

https://github.com/jingtao-li-cver/uniadrs

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Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery

https://github.com/jingwu6/extended-agriculture-vision-dataset

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Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis

https://github.com/joanna-0421/cosmic

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COSMIC: Compress Satellite Images Efficiently via Diffusion Compensation

https://github.com/josesosajs/multimae-meets-eo

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MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks

https://github.com/keillernogueira/data-centric-rs-classification

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Core-Set Selection for Data-efficient Land Cover Segmentation

https://github.com/konstantin-a-maslov/scalable_glacier_mapping

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Globally Scalable Glacier Mapping by Deep Learning Matches Expert Delineation Accuracy

https://github.com/krisrs1128/geo_mlvis

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Interactive Visualization and Representation Analysis Applied to Glacier Segmentation

https://github.com/lccol/vig-eo

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ViGEO: an Assessment of Vision GNNs in Earth Observation

https://github.com/likyoo/open-cd

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Changer: Feature Interaction is What You Need for Change Detection

https://github.com/lizhuohong/segland

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Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework

https://github.com/lizzy8587/castdet

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Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation

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