170 citations · 634 across the 30 of their papers we have counts for
13 papers · 1 filter
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…