PhD Student · UW–Madison · AI · Remote Sensing · Agriculture
Songxi
Yang
Hello! I work at the intersection of satellite remote sensing and artificial intelligence, developing methods
to learn representations from large-scale Earth observation data.
I am broadly interested in how modern machine learning, from generative models to foundation models,
can be adapted to the unique challenges of geospatial data: label scarcity, cross-sensor domain gaps, and satellite scale effects.
I am a member of the
Spatial Computing and Data Mining Lab
at UW–Madison, advised by
Prof. Qunying Huang.
Madison, WI 53705
Deep learning for single-image super-resolution in remote sensing: a review
International Journal of Remote Sensing · 2026
GeoAwareDiffSR: geospatial-aware remote sensing super resolution with diffusion priors and multi-modal constraints for crop mapping
GIScience & Remote Sensing · 2025
Remote Sensing Super Resolution Exploiting Diffusion Priors for Crop Type Mapping
AGU25 · 2025
Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework
Remote Sensing · 2025
Self-supervised Pretraining with Edge Guidance for Building Damage Assessment
Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery · 2024
Advancements in remote sensing for active fire detection: A review of datasets and methods
Science of The Total Environment · 2024
The rising impact of urbanization-caused CO2 emissions on terrestrial vegetation
Ecological Indicators · 2023
An exploration of solar-induced chlorophyll fluorescence (SIF) factors simulated by SCOPE for capturing GPP across vegetation types
Ecological Modelling · 2022
GEOG 574 · Geospatial Database Design and Development
Spring 2026 · Class size: 40
GEOG 170 · Our Digital Globe
Fall 2025 · Online · Class size: 270
GEOG 574 · Geospatial Database Design and Development
Fall 2024 · Class size: 60
GEOG 578 · GIS Applications
Spring 2024 · Class size: 20