Papers with Code 아카이브

Earth Observation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

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/1e12Leon/RemoteSAM

공식 코드

RemoteSAM: Towards Segment Anything for Earth Observation

https://github.com/AIandGlobalDevelopmentLab/causalimages-software

공식 코드

CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images

https://github.com/Att100/CP2M

공식 코드

CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation

https://github.com/Bissmella/Small-object-detection-transformers

공식 코드

Multimodal Transformer Using Cross-Channel attention for Object Detection in Remote Sensing Images

https://github.com/CIA-Oceanix/4dvarnet-core

공식 코드

Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields

https://github.com/CedricLeon/Survey_RS-ML-FPGA

공식 코드

FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review

https://github.com/DeepExtremes/minicube-generation

공식 코드

DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes

https://github.com/Depanshu-Sani/SICKLE

공식 코드

SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters

https://github.com/ESA-PhiLab/CAS_Confidence_Assessment

공식 코드

Improving EO Foundation Models with Confidence Assessment for enhanced Semantic segmentation

https://github.com/ESA-PhiLab/PhilEO-Bench

공식 코드

PhilEO Bench: Evaluating Geo-Spatial Foundation Models

https://github.com/ESA-PhiLab/PhilEO-MajorTOM

공식 코드

Scaling-Up the Pretraining of the Earth Observation Foundation Model PhilEO to the MajorTOM Dataset

https://github.com/ESA-PhiLab/QNN4EO

공식 코드

On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

https://github.com/Earth-Intelligence-Lab/vleo-bench

공식 코드

Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data

https://github.com/ElementAI/HighRes-net

공식 코드

HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery

https://github.com/GFDRR/caribbean-rooftop-classification

공식 코드

Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean

https://github.com/IBM/terratorch

공식 코드

TerraTorch: The Geospatial Foundation Models Toolkit

https://github.com/IPL-UV/pvl8dagans

공식 코드

Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

https://github.com/JTRNEO/SynRS3D

공식 코드

SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery

https://github.com/JomaMinoza/Ocean-Floor-Mapping-with-Uncertainty-Aware-Deep-Learning

공식 코드

Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor Mapping

https://github.com/JonathanPrexl/SenPa-MAE

공식 코드

SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised Pretraining

https://github.com/MatteoPannacci/SemanticAlignNet-QUAD

공식 코드

Enhancing Ground-to-Aerial Image Matching for Visual Misinformation Detection Using Semantic Segmentation

https://github.com/MiliLab/GeoLLaVA-8K

공식 코드

GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution

https://github.com/NASA-IMPACT/Prithvi-EO-2.0

공식 코드

Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications

https://github.com/NJU-LHRS/LHRS-Bot

공식 코드

LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation

https://github.com/NJU-LHRS/official-CMID

공식 코드

CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding

https://github.com/Rennes-LiDAR-Platform/lidar_platform

공식 코드

3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data

https://github.com/SBCV/EarthObservationTiles

공식 코드

Geo-Tiles for Semantic Segmentation of Earth Observation Imagery

https://github.com/STORMTROOPERRR/RSISCD

공식 코드

A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection

https://github.com/Sebbyraft/CNNSpeckleFilter

공식 코드

A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks

https://github.com/Sebbyraft/S1-S2-DataFusion

공식 코드

Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification

https://github.com/Sebbyraft/sentinel_1_GRD_dataset

공식 코드

A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks

https://github.com/TUM-LMF/MTLCC

공식 코드

Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery

https://github.com/VMarsocci/geomultitasknet

공식 코드

GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates

https://github.com/VSainteuf/lightweight-temporal-attention-pytorch

공식 코드

Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series

https://github.com/VSainteuf/utae-paps

공식 코드

Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks

https://github.com/VedikaSrivastava/GeoMapper

공식 코드

Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic Mapping

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.