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Found 20 papers, 7 papers with code
MobilityDL: A Review of Deep Learning From Trajectory Data
Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior.
Here Is Not There: Measuring Entailment-Based Trajectory Similarity for Location-Privacy Protection and Beyond
In this work, we present a different view on trajectory similarity by introducing a measure that utilizes logical entailment.
Where you go is who you are -- A study on machine learning based semantic privacy attacks
Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data.
Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models
Through this vision paper, we hope to draw the attention of researchers and policymakers in geospatial domains to these privacy and security risks inherent in GeoAI foundation models and advocate for the development of privacy-preserving and secure GeoAI foundation models.
Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions
So when applied to large-scale real-world GPS coordinate datasets, which require distance metric learning on the spherical surface, both types of models can fail due to the map projection distortion problem (2D) and the spherical-to-Euclidean distance approximation error (3D).
Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science
This chapter presents some of the fundamental assumptions and principles that could form the philosophical foundation of GeoAI and spatial data science.
Towards General-Purpose Representation Learning of Polygonal Geometries
For the spatial domain approach, we propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding to achieve loop origin invariance on simple polygons.
Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions
However, a map projection distortion problem rises when applying location encoding models to large-scale real-world GPS coordinate datasets (e. g., species images taken all over the world) - all current location encoding models are designed for encoding points in a 2D (Euclidean) space but not on a spherical surface, e. g., earth surface.
A Review of Location Encoding for GeoAI: Methods and Applications
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), polylines (e. g., trajectories), polygons (e. g., administrative regions), graphs (e. g., transportation networks), or rasters (e. g., remote sensing images), in a hidden embedding space so that they can be readily incorporated into deep learning models.
Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces
Location encoding is valuable for a multitude of tasks where both the absolute positions and local contexts (image, text, and other types of metadata) of spatial objects are needed for accurate predictions.
Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions
As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language.
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
In this work we propose a novel deep learning architecture called Traffic Transformer to capture the continuity and periodicity of time series and to model spatial dependency.
Ranked #1 on
Time Series Forecasting
on US Economy
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines.
Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online
In the geospatial aspect, we propose to enrich a query by using both place partonomy and distance decay.
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
The key idea is to use neural networks to convert words in texts to vector space representations based on word positions in a sentence and their contexts, which are suitable for end-to-end training of downstream tasks.
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction
Link prediction is an important and frequently studied task that contributes to an understanding of the structure of knowledge graphs (KGs) in statistical relational learning.
Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs
Recently, several studies have explored methods for using KG embedding to answer logical queries.
POIReviewQA: A Semantically Enriched POI Retrieval and Question Answering Dataset
To test a system's ability to understand the text we adopt an information retrieval evaluation by ranking all the review sentences for a question based on the likelihood that they answer this question.
An empirical study on the names of points of interest and their changes with geographic distance
While Points Of Interest (POIs), such as restaurants, hotels, and barber shops, are part of urban areas irrespective of their specific locations, the names of these POIs often reveal valuable information related to local culture, landmarks, influential families, figures, events, and so on.
Cultural Vocal Bursts Intensity Prediction
Information Retrieval
+1
SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators
SOSA also acts as a replacement of SSN's Stimulus Sensor Observation (SSO) core.
