Video and Image Analytics for Multiple Environments
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Updated
Nov 18, 2024 - Python
Video and Image Analytics for Multiple Environments
Tools for detecting wildlife in aerial images using active learning
Simplify camera trap image analysis with ML species recognition models based around the MegaDetector model
A Python package for identifying 42 kinds of animals, training custom models, and estimating distance from camera trap videos
trends.earth - measure land change
ArcGIS tools to automate mapping and prioritization of wildlife habitat corridors
Distance Estimation for Estimating Animal Abundance
Pytorch implementation for "Iterative Human and Automated Identification of Wildlife Images" (Nature -Machine Intelligence, 2021)
Automated Camera Trapping Identification and Organization Network (ACTION)
Restor's ML pipeline for tree crown mapping in aerial images
A collection of modules to programmatically search for/download imagery from live cam feeds across the state of California.
The Image Level Label to Bounding Box (IL2BB) pipeline automates the generation of labeled bounding boxes by leveraging an organization’s previous labeling efforts.
ACES is a workflow to query small sequences in a large set of genomes. It provides several outputs including BLAST results, a multiple sequence alignment file, a graphical fragment assembly file, and a phylogenetic tree file.
Code for training and evaluating a detector for the USGS Izembek goose survey dataset
Segmentation of coral reefs in benthic imagery to quantify the long-term growth or decline of coral cover within marine protected areas.
The pipeline used to calculate evolutionary conservation featured in the INTAA web server.
A Flask web application for the classification of bird species built using TFServing, Docker and MySQL.
Python script to semiautomatically calculate coral bleaching on photos by counting pixels of a certain colour
Noninvasive technologies to detect, identify and monitor bears, facilitating their conservation.
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