Pinned Repositories
covid19-healthsystemcapacity
Open geospatial work to support health systems' capacity (providers, supplies, ventilators, beds, meds) to effectively care for rapidly growing COVID19 patient needs
chip-n-scale-queue-arranger
Chip 'n scale: Queue Arranger helps you run machine learning models over satellite imagery at scale
label-maker
Data Preparation for Satellite Machine Learning
unicef-schools
AI assisted school detection from high-res satellite imagery with UNICEF
Cleaning-Titanic-Data
One of the most popular starter data sets in data science, the Titanic data set. This is a data set that records various attributes of passengers on the Titanic, including who survived and who didn’t. Here I have detected some missing value, replace the missing values and also create new values added to the dataset. There are two csv files, first one is titanic_original.csv and second one is tatanic_clean.csv. Second csv is generated from the R code, called 'titanic.r' here. Have fun.
FOSS_4g_Pixel_decoder
pixel-decoder
A tool for running deep learning algorithms for semantic segmentation with satellite imagery
eo-learn
Earth observation processing framework for machine learning in Python
Geoyi's Repositories
Geoyi/pixel-decoder
A tool for running deep learning algorithms for semantic segmentation with satellite imagery
Geoyi/FOSS_4g_Pixel_decoder
Geoyi/awesome-satellite-imagery-datasets
List of satellite imagery datasets with annotations for computer vision and deep learning
Geoyi/satlas-super-resolution
Geoyi/segment-anything-eo
Earth observation tools for Meta AI Segment Anything
Geoyi/agoro-field-boundary-detector
Detect field boundaries using satellite imagery.
Geoyi/ARSET23
Geoyi/covid19-healthsystemcapacity
Open geospatial work to support health systems' capacity (providers, supplies, ventilators, beds, meds) to effectively care for rapidly growing COVID19 patient needs
Geoyi/decode
Python code for the DECODE method and mxnet code for Fractal ResUNet
Geoyi/Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Geoyi/deep-learning-resource
Geoyi/dl_landscapes_paper
Buscombe & Ritchie (2018) Landscape Classification with Deep Neural Networks. Geosciences 2018, 8(7), 244
Geoyi/elects
Geoyi/eo-flow
Geoyi/eo-learn
Earth observation processing framework for machine learning in Python
Geoyi/fc
Fractional Cover
Geoyi/galileo
The Galileo family of pretrained remote sensing models
Geoyi/Geoyi.github.io
Zhuang-Fang NaNa Yi's profile and work
Geoyi/geoyi_dockers
Geoyi/landlab
Landlab codebase, wiki, and tests
Geoyi/lightweight-temporal-attention-pytorch
A PyTorch implementation of the Light Temporal Attention Encoder (L-TAE) for satellite image time series. classification
Geoyi/nvidia-anaconda-docker
Docker container for running JUPYTER Notebook within an ANACONDA environment, exploiting NVIDIA-DOCKER for GPUs
Geoyi/Object-Detection-Metrics
Most popular metrics used to evaluate object detection algorithms.
Geoyi/parkingdirty
Using computer vision to detect if bike lanes are blocked
Geoyi/segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Geoyi/SITS-Former
Geoyi/small_utils
small util script to share with my team!
Geoyi/stable-diffusion
Geoyi/style-transfer
style transfer web app [FastAPI + streamlit + Docker]
Geoyi/utae-paps
PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation.