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Found 35 papers, 15 papers with code
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
To fill this gap, we propose TorchSpatial, a learning framework and benchmark for location (point) encoding, which is one of the most fundamental data types of spatial representation learning.
MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
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 constraints.
Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation
Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval. Traditional methods typically employ either classification, which dividing the Earth surface into grid cells and classifying images accordingly, or retrieval, which identifying locations by matching images with a database of image-location pairs.
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
Existing digital sensors capture images at fixed spatial and spectral resolutions (e. g., RGB, multispectral, and hyperspectral images), and each combination requires bespoke machine learning 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).
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
To directly leverage the abundant geospatial information associated with images in pre-training, fine-tuning, and inference stages, we present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images.
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI.
RARR: Researching and Revising What Language Models Say, Using Language Models
Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.
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.
Narrative Cartography with Knowledge Graphs
With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format which is ready for spatial analysis and mapping.
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.
Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval
Existing methods for open-retrieval question answering in lower resource languages (LRLs) lag significantly behind English.
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.
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.
Integrated Triaging for Fast Reading Comprehension
This paper introduces Integrated Triaging, a framework that prunes almost all context in early layers of a network, leaving the remaining (deep) layers to scan only a tiny fraction of the full corpus.
Neural Program Planner for Structured Predictions
We consider the problem of weakly supervised structured prediction (SP) with reinforcement learning (RL) – for example, given a database table and a question, perform a sequence of computation actions on the table, which generates a response and receives a binary success-failure reward.
FastFusionNet: New State-of-the-Art for DAWNBench SQuAD
In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12].
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.
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES
Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 2014) need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size.
Learning to Organize Knowledge and Answer Questions with N-Gram Machines
Though deep neural networks have great success in natural language processing, they are limited at more knowledge intensive AI tasks, such as open-domain Question Answering (QA).
Fast Reading Comprehension with ConvNets
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets.
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)
In this work, we propose the Manager-Programmer-Computer framework, which integrates neural networks with non-differentiable memory to support abstract, scalable and precise operations through a friendly neural computer interface.
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base.
Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields
We prove that the gradient of candidate features can be represented solely as a function of signals and errors, and that CFI is an efficient approximation of gradient-based evaluation methods.
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic
We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank (PPR) on a linearized version of the proof space, and using on this connection, we develop a proveably-correct approximate grounding scheme, based on the PageRank-Nibble algorithm.
Efficient Relational Learning with Hidden Variable Detection
Markov networks (MNs) can incorporate arbitrarily complex features in modeling relational data.
