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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/ltdung/snt-sentry

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Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models

https://github.com/lukeyf/multiview_remote_sensing

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Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing

https://github.com/lulinzhang/sps-nerf

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SparseSat-NeRF: Dense Depth Supervised Neural Radiance Fields for Sparse Satellite Images

https://github.com/lx709/reobench

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REOBench: Benchmarking Robustness of Earth Observation Foundation Models

https://github.com/maja601/eurocrops

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Harnessing Administrative Data Inventories to Create a Reliable Transnational Reference Database for Crop Type Monitoring

https://github.com/majitao-xd/bsdm-had

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BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection

https://github.com/marccoru/crop-type-mapping

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Self-attention for raw optical Satellite Time Series Classification

https://github.com/maups/covid19-custom-script-contest

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Measuring economic activity from space: a case study using flying airplanes and COVID-19

https://github.com/mdchuc/hrsfm

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SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models

https://github.com/michaeltrs/deepsatmodels

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Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification

https://github.com/microsoft/geospatial-ood-detection

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Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation

https://github.com/microsoft/torchgeo

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SSL4EO-L: Datasets and Foundation Models for Landsat Imagery

https://github.com/miquel-espinosa/map-sat

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Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps

https://github.com/nadir121/Sat_DA

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Improving land cover segmentation across satellites using domain adaptation

https://github.com/nandometzger/ODEcrop

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Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations

https://github.com/navidfoumani/tsc_survey

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Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

https://github.com/neuromorphicsystems/event_warping

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Density Invariant Contrast Maximization for Neuromorphic Earth Observations

https://github.com/nhikieu/spatialvolumetricmultimodal

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General-Purpose Multimodal Transformer meets Remote Sensing Semantic Segmentation

https://github.com/nilsleh/tropical_cyclone_uq

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Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data

https://github.com/obaghirli/syn10-diffusion

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SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models

https://github.com/omarcastano/elevation-aware-ssl

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Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic Labels

https://github.com/orion-ai-lab/eouncertaintygeneralization

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On the Generalization of Representation Uncertainty in Earth Observation

https://github.com/orion-ai-lab/hephaestus

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Hephaestus: A large scale multitask dataset towards InSAR understanding

https://github.com/panopticon-fm/panopticon

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Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation

https://github.com/phelber/EuroSAT

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EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

https://github.com/pierreadorni/capabilities-encoding

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Towards Efficient Benchmarking of Foundation Models in Remote Sensing: A Capabilities Encoding Approach

https://github.com/pro1944191/semanticalignnet

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A Semantic Segmentation-guided Approach for Ground-to-Aerial Image Matching

https://github.com/protim1191/glodismo_classifier

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Compressed learning based onboard semantic compression for remote sensing platforms

https://github.com/pubgeo/monocular-geocentric-pose

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Learning Geocentric Object Pose in Oblique Monocular Images

https://github.com/qaz670756/mmcd

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Transformer-based Multimodal Change Detection with Multitask Consistency Constraints

https://github.com/rezafuru/the-fool

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FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression

https://github.com/rramosp/llpeo

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On-orbit model training for satellite imagery with label proportions

https://github.com/satellogic/iquaflow-airport-use-case

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Object Detection performance variation on compressed satellite image datasets with iquaflow

https://github.com/satellogic/iquaflow-dota-obb-use-case

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Object Detection performance variation on compressed satellite image datasets with iquaflow

https://github.com/seasfire/seasfire-datacube-paper

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SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics

https://github.com/shawnan-whu/choice

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CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models

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