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- research-articleJuly 2024
On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)
- Gengchen Mai,
- Weiming Huang,
- Jin Sun,
- Suhang Song,
- Deepak Mishra,
- Ninghao Liu,
- Song Gao,
- Tianming Liu,
- Gao Cong,
- Yingjie Hu,
- Chris Cundy,
- Ziyuan Li,
- Rui Zhu,
- Ni Lao
ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 10, Issue 2Article No.: 11, Pages 1–46https://doi.org/10.1145/3653070Large 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 fine-tuning, few-shot, or even zero-shot learning. Despite their successes ...
- research-articleOctober 2020
SCPP: A Point Process--based Clustering of Spatial Visiting Patterns
ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 7, Issue 1Article No.: 5, Pages 1–30https://doi.org/10.1145/3423405A collection of individuals is represented by point patterns. Each individual is a finite set of geographical locations representing their visiting pattern to places in a region. We present SCPP, an algorithm for clustering these individuals considering ...
- research-articleJune 2020
Coupled IGMM-GANs with Applications to Anomaly Detection in Human Mobility Data
ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 6, Issue 4Article No.: 24, Pages 1–14https://doi.org/10.1145/3385809Detecting anomalous activity in human mobility data has a number of applications, including road hazard sensing, telematics-based insurance, and fraud detection in taxi services and ride sharing. In this article, we address two challenges that arise in ...
- research-articleApril 2020
Incorporating LSTM Auto-Encoders in Optimizations to Solve Parking Officer Patrolling Problem
ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 6, Issue 3Article No.: 20, Pages 1–21https://doi.org/10.1145/3380966The smart parking system is one of the most important problems in smart cities. Recently, an increasing number of sensors installed in parking spaces have provided big spatio-temporal data that be used to analyze parking situations in the city and help ...
- research-articleApril 2020
A Deep Learning Approach for Identifying User Communities Based on Geographical Preferences and Its Applications to Urban and Environmental Planning
ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 6, Issue 3Article No.: 17, Pages 1–24https://doi.org/10.1145/3380970Understanding human mobility plays a vital role in urban and environmental planning as cities continue to grow. Ubiquitous geo-location, localization technology, and availability of big-data-ready computing infrastructure have enabled the development of ...
- research-articleSeptember 2016
Mining At Most Top-K% Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations
ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 2, Issue 3Article No.: 10, Pages 1–27https://doi.org/10.1145/2936775Spatiotemporal co-occurrence patterns (STCOPs) in datasets with extended spatial representations are two or more different event types, represented as polygons evolving in time, whose instances often occur together in both space and time. Finding STCOPs ...