187 citations · 469 across the 14 of their papers we have counts for
18 papers
StraTune: Adaptive Selection of Revision Operators for Self-Evolving LLM Skills
Zeping Liu, Yan Li, Ni Lao +2
Large language models (LLMs) can learn reusable textual skills from execution feedback without updating their parameters, but effectively deciding how to revise these skills remain…
PolyTopoBench: A Benchmark for Complex Vector Polygon Generation from Remote Sensing Imagery
Zeping Liu, Ni Lao, Weiwei Sun +5
Vector polygon generation converts visual inputs, e.g., remote sensing (RS) images, into vectorized polygonal geometries, supporting applications such as autonomous driving, vector…
TrajGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
Maria Despoina Siampou, Gengchen Mai, Ni Lao +4
Many urban prediction tasks depend not only on the static attributes of a location, such as the visual appearance of its built environment, but also on its dynamic function: how pe…
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…
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…
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…
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…
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…
Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation
Zhongliang Zhou, Jielu Zhang, Zihan Guan +5
Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval.Traditional methods typically employ either classification, wh…
Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages
Yingjie Hu, Gengchen Mai, Chris Cundy +6
Social media messages posted by people during natural disasters often contain important location descriptions, such as the locations of victims. Recent research has shown that many…
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
Gengchen Mai, Ni Lao, Weiwei Sun +7
Existing digital sensors capture images at fixed spatial and spectral resolutions (e.g., RGB, multispectral, and hyperspectral images), and each combination requires bespoke machin…
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…
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
Gengchen Mai, Weiming Huang, Jin Sun +11
Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by…
Towards General-Purpose Representation Learning of Polygonal Geometries
Gengchen Mai, Chiyu Jiang, Weiwei Sun +6
Neural network representation learning for spatial data is a common need for geographic artificial intelligence (GeoAI) problems. In recent years, many advancements have been made…
Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions
Gengchen Mai, Yao Xuan, Wenyun Zuo +2
Generating learning-friendly representations for points in a 2D space is a fundamental and long-standing problem in machine learning. Recently, multi-scale encoding schemes (such a…
Narrative Cartography with Knowledge Graphs
Gengchen Mai, Weiming Huang, Ling Cai +2
Narrative cartography is a discipline which studies the interwoven nature of stories and maps. However, conventional geovisualization techniques of narratives often encounter sever…
A Review of Location Encoding for GeoAI: Methods and Applications
Gengchen Mai, Krzysztof Janowicz, Yingjie Hu +5
A common need for artificial intelligence models in the broader geoscience is to represent and encode various types of spatial data, such as points (e.g., points of interest), poly…