305 citations · 455 across the 6 of their papers we have counts for
6 papers
Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions
Gengchen Mai, Yao Xuan, Wenyun Zuo +5
Generating learning-friendly representations for points in space is a fundamental and long-standing problem in ML. Recently, multi-scale encoding schemes (such as Space2Vec and NeR…
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
Gengchen Mai, Ni Lao, Yutong He +2
Geo-tagged images are publicly available in large quantities, whereas labels such as object classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has…
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
Yezhen Cong, Samar Khanna, Chenlin Meng +6
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery pres…
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution
Yutong He, William Zhang, Chenlin Meng +3
Automated tracking of urban development in areas where construction information is not available became possible with recent advancements in machine learning and remote sensing. Un…
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
Chenlin Meng, Yutong He, Yang Song +4
Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user input (e.g., ha…
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
Yutong He, Dingjie Wang, Nicholas Lai +5
High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, amon…