6 papers
TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
Maria Despoina Siampou, Gengchen Mai, Ni Lao +4
Multimodal self-supervised learning (MSSL) has emerged as a key paradigm for pretraining geospatial foundation models. However, existing geospatial MSSL methods are mainly designed…
GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations
Zeping Liu, Ni Lao, Zhangyu Wang +2
Vision Transformer (ViT) has been widely used in computer vision tasks with excellent results by providing representations for a whole image or image patches. However, ViT lacks de…
LocDiff: Identifying Locations on Earth by Diffusing in the Hilbert Space
Zhangyu Wang, Zeping Liu, Jielu Zhang +8
Image geolocalization is a fundamental yet challenging task, aiming at inferring the geolocation on Earth where an image is taken. State-of-the-art methods employ either grid-based…
GeoBS: Information-Theoretic Quantification of Geographic Bias in AI Models
Zhangyu Wang, Nemin Wu, Qian Cao +8
The widespread adoption of AI models, especially foundation models (FMs), has made a profound impact on numerous domains. However, it also raises significant ethical concerns, incl…
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
Nemin Wu, Qian Cao, Zhangyu Wang +12
Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e.g., points, polylines, polygons, network…
MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
Zhangyu Wang, Gengchen Mai, Krzysztof Janowicz +1
A wide range of (multivariate) temporal (1D) and spatial (2D) data analysis tasks, such as grouping vehicle sensor trajectories, can be formulated as clustering with given metric c…