Ecology and environment

Publication

A Deep Active Learning System for Species Identification and Counting in Camera Trap Images

Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery, Neel Joshi, Nebojsa Jojic, Jeff Clune

Methods in Ecology and Evolution | December 2020, Vol 12(1): pp. 150-161

Publication

Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya

Shimaa Baraka, Benjamin Akera, Bibek Aryal, Tenzing Sherpa, Finu Shresta, Anthony Ortiz, Kris Sankaran, Juan M. Lavista Ferres, Mir Matin, Yoshua Bengio

NeurIPS CCAI | December 2020

Project Project Project

Publication

AIDE: Accelerating Image-Based Ecological Surveys with Interactive Machine Learning

Benjamin Kellenberger, Devis Tuia, Dan Morris

Methods in Ecology and Evolution | November 2020, Vol 11(12): pp. 1716-1727

Project

Publication

Estimating Crop Yields With Remote Sensing And Deep Learning

Renato L. de F. Cunha, Bruno Silva

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences | October 2020, pp. 59-64

Group

AI For Good Lab

The Microsoft AI For Good team helps researchers and organizations reach solutions on some of the world’s biggest problems. Providing technology, resources, and expertise to empower those working to solve humanitarian issues to create a…

Tool

Pytorch-wildlife

At the core of our mission is the desire to create a harmonious space where conservation scientists from all over the globe can unite. Where they’re able to share, grow, use datasets and deep learning…

GitHub

Publication

Weakly Supervised Semantic Segmentation in the 2020 IEEE GRSS Data Fusion Contest

Caleb Robinson, Kolya Malkin, Lucas Hu, Bistra Dilkina, Nebojsa Jojic

IEEE International Geoscience and Remote Sensing Symposium (IGARSS) | September 2020

1st place in 2020 IEEE GRSS Data Fusion Contest

Project Project

Tool

Land Cover Orinoquia

This AI for Earth project, in collaboration with the Wildlife Conservation Society Colombia (WCS Colombia (opens in new tab)) was developed to create up-to-date land cover maps of the Orinoquía (opens in new tab) region…

GitHub

Publication

Multispecies Bioacoustic Classification Using Transfer Learning of Deep Convolutional Neural Networks with Pseudo-Labeling

Ming Zhong, Jack LeBien, Marconi Campos-Cerqueira, Rahul Dodhia, Juan M. Lavista Ferres, Julian P.Velev, T. Mitchell Aide

Applied Acoustics | September 2020, Vol 166

Project Project Project

Publication

A Pipeline for Identification of Bird and Frog Species in Tropical Soundscape Recordings Using a Convolutional Neural Network

Jack LeBien, Ming Zhong, Marconi Campos-Cerqueira, Julian P. Velev , Rahul Dodhia, Juan M. Lavista Ferres, Mitchell Aidead

Science Direct | September 2020

Project Project Project

Project FarmVibes | Research for Industry

Bioacoustics: Leveraging AI to process audio recordings of diverse species

Geospatial Machine Learning

Advance sustainability initiative | AI For Good Lab

A Deep Active Learning System for Species Identification and Counting in Camera Trap Images

Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya

AIDE: Accelerating Image-Based Ecological Surveys with Interactive Machine Learning

Estimating Crop Yields With Remote Sensing And Deep Learning

AI For Good Lab

Pytorch-wildlife

Weakly Supervised Semantic Segmentation in the 2020 IEEE GRSS Data Fusion Contest

Land Cover Orinoquia

Multispecies Bioacoustic Classification Using Transfer Learning of Deep Convolutional Neural Networks with Pseudo-Labeling

A Pipeline for Identification of Bird and Frog Species in Tropical Soundscape Recordings Using a Convolutional Neural Network

Ecology and environment

Highlights