elijahcole/sinr
Spatial Implicit Neural Representations for Global-Scale Species Mapping - ICML 2023
This helps conservationists, ecologists, and climate scientists understand where different species live globally. You provide a set of locations where a species has been observed, and it outputs a predicted map showing where that species is likely to be present or absent across the world. It is designed for researchers and practitioners working with species distribution data from sources like iNaturalist.
No commits in the last 6 months.
Use this if you need to estimate the geographical range of many species simultaneously, especially when working with sparse or crowdsourced observation data.
Not ideal if you need definitive, highly validated range maps for critical decision-making without further expert calibration and validation.
Stars
54
Forks
18
Language
Python
License
MIT
Category
Last pushed
Aug 05, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/elijahcole/sinr"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Westlake-AI/openmixup
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
YU1ut/MixMatch-pytorch
Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"
kamata1729/QATM_pytorch
Pytorch Implementation of QATM:Quality-Aware Template Matching For Deep Learning
nttcslab/msm-mae
Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representations
rgeirhos/generalisation-humans-DNNs
Data, code & materials from the paper "Generalisation in humans and deep neural networks" (NeurIPS 2018)