Video and Image Analytics for Multiple Environments
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Updated
Jun 24, 2024 - Python
Video and Image Analytics for Multiple Environments
Tools for detecting wildlife in aerial images using active learning
trends.earth - measure land change
A Python package for identifying 42 kinds of animals, training custom models, and estimating distance from camera trap videos
Simplify camera trap image analysis with ML species recognition models based around the MegaDetector model
ArcGIS tools to automate mapping and prioritization of wildlife habitat corridors
The Image Level Label to Bounding Box (IL2BB) pipeline automates the generation of labeled bounding boxes by leveraging an organization’s previous labeling efforts.
Filter cameras traps images with megadetector on a cluster
Pytorch implementation for "Iterative Human and Automated Identification of Wildlife Images" (Nature -Machine Intelligence, 2021)
A collection of modules to programmatically search for/download imagery from live cam feeds across the state of California.
User Interface for PisaPy
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.
Investigating biases in scientific publications
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.
Estopinan J, Servajean M, Bonnet P, Joly A, Munoz F. Mapping global orchid assemblages with deep learning provides novel conservation insights. Ecological Informatics. 2024 May 10:102627
Ballini & Staffoni et al. (submitted to Hydrobiologia) workflow
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.
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