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Ryan Avery's Projects

2018-10-25-odu icon 2018-10-25-odu

Website for this repository with installation instructions, schedule, syllabus links:

aiforearth-api-development icon aiforearth-api-development

The AI for Earth Engineering Team works to empower AI for Earth Grantees by providing base images to quickly move from a local model to an model-backed API that any permitted user can utilize.

amazon-asdi icon amazon-asdi

Docs and supporting material for the Amazon Sustainability Data Initiative.

aquacrop icon aquacrop

AquaCrop-OSPy: Python implementation of AquaCrop-OS

bayts icon bayts

Set of tools to apply the probabilistic approach of Reiche et al. (2015, 2018) to combine multiple optical and/or Radar satellite time series and to detect deforestation/forest cover loss in near real-time. The package includes functions to apply the approach to both, single pixel time series and raster time series. Examples and test data are provided below.

cocoapi icon cocoapi

COCO API - Dataset @ http://cocodataset.org/

cropmask_rcnn icon cropmask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

cv icon cv

Ryan Avery's academic CV

daymetpy icon daymetpy

A python script to download Daymet data

env-optics icon env-optics

python notebooks summarizing lab work and answering challenge problems

excimer_referencer icon excimer_referencer

WIP to spatially reference images produced from a laser ablation system with Python

geospatial-workshop icon geospatial-workshop

Overview, setup instructions, and data presentation for the Data Carpentry Geospatial Workshop

git-novice icon git-novice

Software Carpentry introduction to Git for novices.

hexutil icon hexutil

Classes and functions to deal with hexagonal grids.

highrescanopyheight icon highrescanopyheight

This repository provides inference code to compute canopy height maps from aerial images, as described in the paper "Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar".

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