Philipe Ambrozio Dias R&D Associate in Computer Vision and Machine Learning Contact AMBROZIODIAP@ORNL.GOV All Publications A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation Interactive Rotated Object Detection for Novel Class Detection in Remotely Sensed Imagery OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery Inferring building height from footprint morphology data Introducing SpaceNet 9 - Cross-Modal Satellite Imagery Registration for Natural Disaster Responses Conditional Experts for Improved Building Damage Assessment Across Satellite Imagery View Angles Towards Diverse and Representative Global Pretraining Datasets for Remote Sensing Foundation Models... Pretraining Billion-Scale Geospatial Foundational Models on Frontier Benchmarking and end-to-end considerations for GeoAI-enabled decision making Chapter "GeoAI for Humanitarian Assistance" in Book "Handbook of Geospatial Artificial Intelligence" Scaling Automatic Vector Data Alignment to Satellite Imagery An Agenda for Multimodal Foundation Models for Earth Observation Towards Geospatial Knowledge Graph Infused Neuro-Symbolic AI for Remote Sensing Scene Understanding TOWARDS RAPID RESPONSE UPDATES OF POPULATIONS AT RISK... Post-Invasion Damage Assessment: Ukraine’s Crop Storage Infrastructure Embedding Ethics and Trustworthiness for Sustainable AI in Earth Sciences: Where Do We Begin? Semi-automated Design of Artificial Intelligence Earth Science Models Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation Key Links Curriculum Vitae Google Scholar Web of Science ORCID Google Scholar (publications) Organizations National Security Sciences Directorate Geospatial Science and Human Security Division Geographic Data Science Section GeoAI Group
