TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning

• URL: http://arxiv.org/abs/2406.15658v3
• Date: Sun, 19 Jan 2025 22:25:54 GMT
• Title: TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
• Authors: Nemin Wu, Qian Cao, Zhangyu Wang, Zeping Liu, Yanlin Qi, Jielu Zhang, Joshua Ni, Xiaobai Yao, Hongxu Ma, Lan Mu, Stefano Ermon, Tanuja Ganu, Akshay Nambi, Ni Lao, Gengchen Mai,
• Abstract summary: We propose TorchSpatial, a learning framework and benchmark for location (point) encoding. TorchSpatial contains three key components: 1) a unified location encoding framework that consolidates 15 commonly recognized location encoders; 2) the LocBench benchmark tasks encompassing 7 geo-aware image classification and 10 geo-aware image regression datasets; and 3) a comprehensive suite of evaluation metrics to quantify geo-aware model's overall performance as well as their geographic bias, with a novel Geo-Bias Score metric.
• Score: 36.725822223732635
• License:
• Abstract: Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e.g., points, polylines, polygons, networks, images, etc.) in their native formats. Learning good spatial representations is a fundamental problem for various downstream applications such as species distribution modeling, weather forecasting, trajectory generation, geographic question answering, etc. Even though SRL has become the foundation of almost all geospatial artificial intelligence (GeoAI) research, we have not yet seen significant efforts to develop an extensive deep learning framework and benchmark to support SRL model development and evaluation. 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. TorchSpatial contains three key components: 1) a unified location encoding framework that consolidates 15 commonly recognized location encoders, ensuring scalability and reproducibility of the implementations; 2) the LocBench benchmark tasks encompassing 7 geo-aware image classification and 10 geo-aware image regression datasets; 3) a comprehensive suite of evaluation metrics to quantify geo-aware model's overall performance as well as their geographic bias, with a novel Geo-Bias Score metric. Finally, we provide a detailed analysis and insights into the model performance and geographic bias of different location encoders. We believe TorchSpatial will foster future advancement of spatial representation learning and spatial fairness in GeoAI research. The TorchSpatial model framework and LocBench benchmark are available at https://github.com/seai-lab/TorchSpatial, and the Geo-Bias Score evaluation framework is available at https://github.com/seai-lab/PyGBS.

Related papers

• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components. GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric. We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks [84.86699025256705]
  We present GEOBench-VLM, a benchmark specifically designed to evaluate Vision-Language Models (VLMs) on geospatial tasks. Our benchmark features over 10,000 manually verified instructions and covers a diverse set of variations in visual conditions, object type, and scale. We evaluate several state-of-the-art VLMs to assess their accuracy within the geospatial context.
  arXiv  Detail & Related papers  (2024-11-28T18:59:56Z)
• SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery [22.716322265391852]
  We introduce Satellite Contrastive Location-Image Pretraining (SatCLIP) SatCLIP learns an implicit representation of locations by matching CNN and ViT inferred visual patterns of openly available satellite imagery with their geographic coordinates. In experiments, we use SatCLIP embeddings to improve prediction performance on nine diverse location-dependent tasks including temperature prediction, animal recognition, and population density estimation.
  arXiv  Detail & Related papers  (2023-11-28T19:14:40Z)
• CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage Refinement [11.108860387261508]
  Visual geolocalization is a cost-effective and scalable task that involves matching one or more query images taken at some unknown location, to a set of geo-tagged reference images. We develop CurriculumLoc, a novel keypoint detection and description with global semantic awareness and a local geometric verification. We achieve new high recall@1 scores of 62.6% and 94.5% on ALTO, with two different distances metrics, respectively.
  arXiv  Detail & Related papers  (2023-11-20T08:40:01Z)
• GeoLLM: Extracting Geospatial Knowledge from Large Language Models [49.20315582673223]
  We present GeoLLM, a novel method that can effectively extract geospatial knowledge from large language models. We demonstrate the utility of our approach across multiple tasks of central interest to the international community, including the measurement of population density and economic livelihoods. Our experiments reveal that LLMs are remarkably sample-efficient, rich in geospatial information, and robust across the globe.
  arXiv  Detail & Related papers  (2023-10-10T00:03:23Z)
• GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
  Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
  arXiv  Detail & Related papers  (2023-09-27T20:54:56Z)
• Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking [61.60169764507917]
  Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. We propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines.
  arXiv  Detail & Related papers  (2023-09-04T13:44:50Z)
• TorchGeo: deep learning with geospatial data [24.789143032205736]
  We introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides benchmark datasets, composable datasets for generic geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery. TorchGeo is also the first library to provide pre-trained models for multispectral satellite imagery.
  arXiv  Detail & Related papers  (2021-11-17T02:47:33Z)
• A Review of Location Encoding for GeoAI: Methods and Applications [14.279748049042665]
  A common need for artificial intelligence models in the broader geoscience is to represent and encode various types of spatial data. One fundamental step is to encode a single point location into an embedding space. This embedding is learning-friendly for downstream machine learning models such as support vector machines and neural networks.
  arXiv  Detail & Related papers  (2021-11-07T05:25:49Z)
• Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells [11.071527762096053]
  We propose a representation learning model called Space2Vec to encode the absolute positions and spatial relationships of places. Results show that because of its multi-scale representations, Space2Vec outperforms well-established ML approaches.
  arXiv  Detail & Related papers  (2020-02-16T04:22:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.