Anthony Ortiz

Anthony Ortiz

Principal Research Science Manager · Microsoft AI for Good Lab

I lead and conduct AI research at Microsoft AI for Good, building machine learning systems for Earth observation, climate and sustainability, conservation, medical imaging, and humanitarian response.

  • Geospatial ML & Earth Observation
  • AI for Climate & Sustainability
  • Conservation & Biodiversity
  • Medical Imaging
  • Disaster & Humanitarian Response
Citations
20
h-index
30+
Disaster Responses
60+
Publications

Research

My research focuses on robust and scalable AI systems for satellite imagery, geospatial data, and high-impact scientific applications. I co-lead Microsoft’s Geospatial Machine Learning Center and work on generalization, transfer learning, domain adaptation, and human-AI collaboration.

Selected Work

Projects I’m proud of. See Research for more and Publications for the full list.

Geospatial ML · Open Source

TorchGeo

A PyTorch domain library for geospatial data — datasets, samplers, transforms, and pretrained models that make Earth-observation deep learning as easy as torchvision.

Earth Observation · 2025

Global Renewables Watch

A living atlas of the world’s utility-scale solar and onshore wind, mapped quarterly from satellite imagery with deep learning — built with Planet and The Nature Conservancy.

Humanitarian · Disaster Response

Rapid Building Damage Assessment

Open-source pipeline that turns post-event satellite imagery into building-damage estimates within hours — deployed across 30+ responses spanning floods, earthquakes, wildfires, hurricanes, and tornadoes, with results browsable in our public visualizer.

Humanitarian · 2024 GitHub Award for Good

Mapping Refugee Settlements with AI

AI-assisted mapping of the Kakuma and Kalobeyei refugee settlements in Kenya from drone and satellite imagery — with UNHCR and Humanitarian OpenStreetMap Team — to improve infrastructure planning, services, and energy access for displaced communities.

Health · 2025

AI-enabled screening for Retinopathy of Prematurity in low-resource settings

Deep-learning system to triage ROP from neonatal fundus images, validated in clinical settings where pediatric ophthalmologists are scarce.

Earth Observation · 2022

An AI inventory of every utility-scale solar farm in India

National-scale dataset built from satellite imagery to inform renewable-energy policy and siting decisions.

Earth Observation · 2021

Tracking glacial melt at the top of the world

Open data and ML to monitor the Hindu Kush Himalaya glaciers — work funded by an $18K Microsoft AI Research Grant.

Conservation · 2022

Detecting beached whales (and elk and cattle) from space

Very-high-resolution satellite imagery + deep learning for biodiversity and wildlife monitoring at scale.

Core ML · CVPR 2020

Local Context Normalization

A drop-in normalization layer that improves generalization of dense-prediction networks across geographies.

Selected Publications

See all publications →

Experience

Education

Ph.D., Computer Science

University of Texas at El Paso (UTEP) · 2015–2020

GPA 4.0 · Anita Mochen Loya Graduate Fellowship

B.S., Telematics Engineering

Pontificia Universidad Católica Madre y Maestra (PUCMM) · 2010–2014

Summa Cum Laude

News & Media

Mentoring

Selected interns and students I have had the privilege to mentor — current roles in parentheses.

Contact

Email

For collaborations, talks, and academic inquiries. Click to compose.

Affiliation

Microsoft AI for Good Lab
Redmond, WA, USA