170 citations · 634 across the 30 of their papers we have counts for
32 papers
Geographic Bias and Diversity in AI Evaluation
Zilong Liu, Krzysztof Janowicz, Gengchen Mai +2
Among the many challenges hindering the responsible development and deployment of AI, arguably none has faced more intense scrutiny than bias in its various forms. This underscores…
Geography According to ChatGPT -- How Generative AI Represents and Reasons about Geography
Krzysztof Janowicz, Gengchen Mai, Rui Zhu +4
Understanding how AI will represent and reason about geography should be a key concern for all of us, as the broader public increasingly interacts with spaces and places through th…
Whose Truth? Pluralistic Geo-Alignment for (Agentic) AI
Krzysztof Janowicz, Zilong Liu, Gengchen Mai +5
AI (super) alignment describes the challenge of ensuring (future) AI systems behave in accordance with societal norms and goals. While a quickly evolving literature is addressing b…
The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment
Rui Zhu, Cogan Shimizu, Shirly Stephen +25
Global challenges such as food supply chain disruptions, public health crises, and natural hazard responses require access to and integration of diverse datasets, many of which are…
The KnowWhereGraph Ontology
Cogan Shimizu, Shirly Stephe, Adrita Barua +21
KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate…
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…
Probing the Information Theoretical Roots of Spatial Dependence Measures
Zhangyu Wang, Krzysztof Janowicz, Gengchen Mai +1
Intuitively, there is a relation between measures of spatial dependence and information theoretical measures of entropy. For instance, we can provide an intuition of why spatial da…
Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models
Jinmeng Rao, Song Gao, Gengchen Mai +1
In recent years we have seen substantial advances in foundation models for artificial intelligence, including language, vision, and multimodal models. Recent studies have highlight…
GeoLLM: Extracting Geospatial Knowledge from Large Language Models
Rohin Manvi, Samar Khanna, Gengchen Mai +3
The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can…
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…
Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications
Saed Rezayi, Zhengliang Liu, Zihao Wu +8
This paper explores new frontiers in agricultural natural language processing by investigating the effectiveness of using food-related text corpora for pretraining transformer-base…
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
Haixing Dai, Yiwei Li, Zhengliang Liu +14
In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which…
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…
ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs
Yucheng Shi, Hehuan Ma, Wenliang Zhong +5
ChatGPT, as a recently launched large language model (LLM), has shown superior performance in various natural language processing (NLP) tasks. However, two major limitations hinder…
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…
AGI for Agriculture
Guoyu Lu, Sheng Li, Gengchen Mai +11
Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being…
EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System
Yanlin Qi, Gengchen Mai, Rui Zhu +1
Over the past decade, the electric vehicle industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multiface…
AGI: Artificial General Intelligence for Education
Ehsan Latif, Gengchen Mai, Matthew Nyaaba +6
Artificial general intelligence (AGI) has gained global recognition as a future technology due to the emergence of breakthrough large language models and chatbots such as GPT-4 and…
Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models
Jielu Zhang, Zhongliang Zhou, Gengchen Mai +4
Remote sensing imagery has attracted significant attention in recent years due to its instrumental role in global environmental monitoring, land usage monitoring, and more. As imag…
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…
Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes
Ling Cai, Krzysztof Janowic, Bo Yan +2
Almost all statements in knowledge bases have a temporal scope during which they are valid. Hence, knowledge base completion (KBC) on temporal knowledge bases (TKB), where each sta…