MobCLIP: Learning General-purpose Geospatial Representation at Scale

• URL: http://arxiv.org/abs/2506.01297v3
• Date: Wed, 04 Jun 2025 02:07:51 GMT
• Title: MobCLIP: Learning General-purpose Geospatial Representation at Scale
• Authors: Ya Wen, Jixuan Cai, Qiyao Ma, Linyan Li, Xinhua Chen, Chris Webster, Yulun Zhou,
• Abstract summary: We present MobCLIP, the first nationwide general-purpose location encoder.<n>Adopting a novel CLIP-based architecture, our framework aligns 100M+ POIs, nationwide remote sensing imagery, and structured demographic statistics with a billion-edge mobility graph.<n>We show MobCLIP achieves significantly superior general-purpose predictive performances than state-of-the-art models by an average of 35%.
• Score: 0.7224497621488285
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence. Current embedding methods often lack versatility, limiting their utility across diverse tasks in both human and natural domains. We present MobCLIP, the first nationwide general-purpose location encoder, integrating an unprecedented diversity of data modalities through effective and scalable multimodal fusion. Adopting a novel CLIP-based architecture, our framework aligns 100M+ POIs, nationwide remote sensing imagery, and structured demographic statistics with a billion-edge mobility graph. By tokenizing spatial locations into grid cells inspired by Vision Transformers, we establish a unified representation space bridging mobility patterns and multimodal features. To rigorously evaluate the general-purpose effectiveness of MobCLIP, we construct a benchmark dataset composed of 11 downstream prediction tasks across social, economic, and natural domains. Experiments show that MobCLIP, with four input modalities and a compact 128-dimensional representation space, achieves significantly superior general-purpose predictive performances than state-of-the-art models by an average of 35%. Thanks to the effective integration of human-centric modalities, the performance gain is particularly profound in human-centric tasks, such as energy consumption (+260%), offline retail consumption amount (+98%), and crime cases (+95%) predictions. Echoing LLM scaling laws, we further demonstrate the scaling behavior in geospatial representation learning. We open-source code and pretrained models at: https://github.com/ylzhouchris/MobCLIP.

Related papers

• POIFormer: A Transformer-Based Framework for Accurate and Scalable Point-of-Interest Attribution [3.729614737011418]
  textsfPOIFormer is a novel Transformer-based framework for accurate and efficient POI attribution.<n>textsfPOIFormer enables accurate, efficient attribution in large, noisy mobility datasets.
  arXiv  Detail & Related papers  (2025-07-12T04:37:52Z)
• TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation [65.74990259650984]
  We introduce TerraFM, a scalable self-supervised learning model that leverages globally distributed Sentinel-1 and Sentinel-2 imagery.<n>Our training strategy integrates local-global contrastive learning and introduces a dual-centering mechanism.<n>TerraFM achieves strong generalization on both classification and segmentation tasks, outperforming prior models on GEO-Bench and Copernicus-Bench.
  arXiv  Detail & Related papers  (2025-06-06T17:59:50Z)
• WorldPM: Scaling Human Preference Modeling [130.23230492612214]
  We propose World Preference Modeling$ (WorldPM) to emphasize this scaling potential.<n>We collect preference data from public forums covering diverse user communities.<n>We conduct extensive training using 15M-scale data across models ranging from 1.5B to 72B parameters.
  arXiv  Detail & Related papers  (2025-05-15T17:38:37Z)
• LaDi-WM: A Latent Diffusion-based World Model for Predictive Manipulation [51.834607121538724]
  We propose LaDi-WM, a world model that predicts the latent space of future states using diffusion modeling.<n>We show that LaDi-WM significantly enhances policy performance by 27.9% on the LIBERO-LONG benchmark and 20% on the real-world scenario.
  arXiv  Detail & Related papers  (2025-05-13T04:42:14Z)
• RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings [7.431269929582643]
  We propose a novel retrieval-augmented strategy called RANGE.<n>We build our method on the intuition that the visual features of a location can be estimated by combining the visual features from multiple similar-looking locations.<n>Our results show that RANGE outperforms the existing state-of-the-art models with significant margins in most tasks.
  arXiv  Detail & Related papers  (2025-02-27T05:45:51Z)
• SaliencyI2PLoc: saliency-guided image-point cloud localization using contrastive learning [17.29563451509921]
  SaliencyI2PLoc is a contrastive learning architecture that fuses the saliency map into feature aggregation.<n>Our method achieves a Recall@1 of 78.92% and a Recall@20 of 97.59% on the urban scenario evaluation dataset.
  arXiv  Detail & Related papers  (2024-12-20T05:20:10Z)
• Federated Multi-Agent Mapping for Planetary Exploration [0.4143603294943439]
  We propose a federated multi-agent mapping approach that jointly trains a global map model across agents without transmitting raw data.<n>Our method leverages implicit neural mapping to generate parsimonious, adaptable representations, reducing data transmission by up to 93.8% compared to raw maps.<n>We demonstrate the efficacy of our approach on Martian terrains and glacier datasets, achieving downstream path planning F1 scores as high as 0.95 while outperforming on map reconstruction losses.
  arXiv  Detail & Related papers  (2024-04-02T20:32:32Z)
• Recognize Any Regions [55.76437190434433]
  RegionSpot integrates position-aware localization knowledge from a localization foundation model with semantic information from a ViL model.<n>Experiments in open-world object recognition show that our RegionSpot achieves significant performance gain over prior alternatives.
  arXiv  Detail & Related papers  (2023-11-02T16:31:49Z)
• 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)
• City Foundation Models for Learning General Purpose Representations from OpenStreetMap [16.09047066527081]
  We present CityFM, a framework to train a foundation model within a selected geographical area of interest, such as a city. CityFM relies solely on open data from OpenStreetMap, and produces multimodal representations of entities of different types, spatial, visual, and textual information. In all the experiments, CityFM achieves performance superior to, or on par with, the baselines.
  arXiv  Detail & Related papers  (2023-10-01T05:55:30Z)
• SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation [8.636014676778682]
  Open data is frequently released spatially aggregated, usually to comply with privacy policies. But coarse, heterogeneous aggregations complicate coherent learning and integration for downstream AI/ML systems. We propose an overarching model named Structurally-Aware Recurrent Network (SARN), which integrates structurally-aware spatial attention layers into the Gated Recurrent Unit (GRU) model. For scenarios with limited historical training data, we show that a model pre-trained on one city variable can be fine-tuned for another city variable using only a few hundred samples.
  arXiv  Detail & Related papers  (2023-06-09T21:01:29Z)
• One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers [96.51828911883456]
  Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation. We explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization problem, where only one real-world data sample is available.
  arXiv  Detail & Related papers  (2022-12-14T15:54:15Z)
• Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
  We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
  arXiv  Detail & Related papers  (2021-02-14T05:36:25Z)

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.