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crop-forecasting

Predicting rice field yields through the integration of Microsoft Planetary satellite images, meteorological data, and field information in the 2023 EY Open Science Data Challenge - Crop Forecasting.

crop-yield-estimate

Harness the power of machine learning to forecast rice and wheat crop yields per acre in India, aiming to empower smallholder farmers, combat poverty and malnutrition, utilizing data from Digital Green surveys to revolutionize agriculture and promote sustainable practices in the face of climate change for enhanced global food security.

flair-2

Engage in a semantic segmentation challenge for land cover description using multimodal remote sensing earth observation data, delving into real-world scenarios with a dataset comprising 70,000+ aerial imagery patches and 50,000 Sentinel-2 satellite acquisitions.

reflection-connection

This repository hosts code and resources for a project using Siamese Neural Networks (SNN) for seismic data analysis in geophysics. The goal is to develop an algorithm that identifies and matches similar seismic features, assisting geoscientists in interpreting subsurface geological structures.

segmenting-subsurface

Deep Learning solution for multi-layer seismic data segmentation using Meta's SAM, trained on a dataset of 9,000 volumes for improved subsurface mapping.

stranger-sections-2

Machine Learning challenge to classify and segment kerogen macerals in thin rock sections, using labeled and unlabeled images. Focused on identifying liptinite, vitrinite, and inertinite types, the competition emphasizes model accuracy measured by the Jaccard Score to enhance sedimentary rock analysis and hydrocarbon exploration.

vesuvius-challenge

Unveiling the secrets of an ancient library buried by Mount Vesuvius, this Kaggle competition, supported by the Vesuvius Challenge organization, tasked participants with detecting ink from 3D X-ray scans of charred scrolls preserved in a Roman villa in Herculaneum.