agri-hub Goto Github PK

.github

Research group: AI and Earth observation for sustainable ecosystems

aaai23-eval-agrirecommendations

"Evaluating Digital Agriculture Recommendations with Causal Inference". It was accepted and presented in the special track on Artificial Intelligence for Social Impact, AAAI-23

adc

A Data Cube-based Framework for Utilizing Big Satellite Data for Agriculture Monitoring

agri-hub.github.io

Agri-Hub ressearch group

callisto

A Callisto repository that will hold relevant jupyter notebooks and other related work

callisto-dataset-collection

A list of datasets aiming to enable Artificial Intelligence applications that use Copernicus data.

ccai-neurips22-personalizing

"Personalizing Sustainable Agriculture with Causal Machine Learning". Best Proposal Paper of "Tackling Climate Change with Machine Learning" Workshop, NeurIPS'22.

cotton-phenology-dataset

datacap

An end-to-end framework for monitoring the CAP, which utilizes Open Data Cube, Sentinel-2 data and Streel-level images.

deep-learning-for-cloud-gap-filling-on-normalized-difference-vegetation-index

A CNN-RNN based model that identifies correlations between optical and SAR data and exports dense Normalized Difference Vegetation Index (NDVI) time-series of a static 6-day time resolution and can be used for Events Detection tasks

educational

Educational Scripts related to Geoprocessing with Python

eoprocessors

eshape_phenology

igarss2024

Summer School

mapillary_annotation

rice_map

This dataset includes three paddy rice maps in South Korea for the year 2018. Specifically, the three maps are for the regions of i) Seosan and Dangjin, ii) Haenam and iii) Cheorwon. The paddy rice maps are a product Random Forest predictions and DO NOT represent ground truth information. The predictions were made based on a semi-supervised approach that first generates pseudo-labels using a dynamic k-means algorithm and then trains the Random Forest Classifier. The classifier has been trained on pseudo-labels that stem from the Seosan and Dangjin site (i).

space2ground

A dataset with Space (Sentinel-1/2) and Ground (street-level images) components, annotated with crop-type labels for agriculture monitoring.