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In the geospatial domain, universal representation models are significantly less prevalent than their extensive use in natural language processing and computer vision.
Graph wavenet for deep spatial-temporal graph modeling
Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C. 2019a · 1906
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Graph wavenet for deep spatial-temporal graph modeling
Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C. 2019b · 1906
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Domain adversarial spatial-temporal network: A transferable framework for short-term traffic forecasting across cities
Tang, Y.; Qu, A.; Chow, A. H.; Lam, W. H.; Wong, S. C.; and Ma, W. 2022 · 1915
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Auto-sklearn 2.0: The next generation
Feurer, M.; Eggensperger, K.; Falkner, S.; Lindauer, M.; and Hutter, F. 2020 · 2007
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Ridge regression
McDonald, G. C. 2009 · 2009
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Overpass API
Olbricht, R.; et al. 2011 · 2011
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Analyzing the contributor activity of a volunteered geographic information project—The case of OpenStreetMap
Neis, P.; and Zipf, A. 2012 · 2012
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Combining satellite imagery and machine learning to predict poverty
Jean, N.; Burke, M.; Xie, M.; Davis, W. M.; Lobell, D. B.; and Ermon, S. 2016 · 2016
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Can human development be measured with satellite imagery?
Head, A.; Manguin, M.; Tran, N.; and Blumenstock, J. E. 2017 · 2017
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y.; Yu, R.; Shahabi, C.; and Liu, Y. 2017 · 2017
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A deep learning approach for population estimation from satellite imagery
Robinson, C.; Hohman, F.; and Dilkina, B. 2017 · 2017
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Yu, B.; Yin, H.; and Zhu, Z. 2017 · 2017
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Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting
Geng, X.; Li, Y.; Wang, L.; Zhang, L.; Yang, Q.; Ye, J.; and Liu, Y. 2019 · 2019
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Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Guo, S.; Lin, Y.; Feng, N.; Song, C.; and Wan, H. 2019 · 2019
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Tile2vec: Unsupervised representation learning for spatially distributed data
Jean, N.; Wang, S.; Samar, A.; Azzari, G.; Lobell, D.; and Ermon, S. 2019 · 2019
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Predicting economic development using geolocated wikipedia articles
Sheehan, E.; Meng, C.; Tan, M.; Uzkent, B.; Jean, N.; Burke, M.; Lobell, D.; and Ermon, S. 2019 · 2019
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Adaptive graph convolutional recurrent network for traffic forecasting
Bai, L.; Yao, L.; Li, C.; Wang, X.; and Wang, C. 2020 · 2020
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Multi-range attentive bicomponent graph convolutional network for traffic forecasting
Chen, W.; Chen, L.; Xie, Y.; Cao, W.; Gao, Y.; and Feng, X. 2020 · 2020
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Urban2vec: Incorporating street view imagery and pois for multi-modal urban neighborhood embedding
Wang, Z.; Li, H.; and Rajagopal, R. 2020 · 2020
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Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
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Reversible instance normalization for accurate time-series forecasting against distribution shift
Kim, T.; Kim, J.; Tae, Y.; Park, C.; Choi, J.-H.; and Choo, J. 2021 · 2021
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Predicting livelihood indicators from community-generated street-level imagery
Lee, J.; Grosz, D.; Uzkent, B.; Zeng, S.; Burke, M.; Lobell, D.; and Ermon, S. 2021 · 2021
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A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators
Nilsen, K.; Tejedor-Garavito, N.; Leasure, D. R.; Utazi, C. E.; Ruktanonchai, C. W.; Wigley, A. S.; Dooley, C. A.; Matthews, Z.; and Tatem, A. J. 2021 · 2021
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Whitening sentence representations for better semantics and faster retrieval
Su, J.; Cao, J.; Liu, W.; and Ou, Y. 2021 · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Wu, H.; Xu, J.; Wang, J.; and Long, M. 2021 · 2021
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Sustainbench: Benchmarks for monitoring the sustainable development goals with machine learning
Yeh, C.; Meng, C.; Wang, S.; Driscoll, A.; Rozi, E.; Liu, P.; Lee, J.; Burke, M.; Lobell, D. B.; and Ermon, S. 2021 · 2021
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Multi-view joint graph representation learning for urban region embedding
Zhang, M.; Li, T.; Li, Y.; and Hui, P. 2021 · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H.; Zhang, S.; Peng, J.; Zhang, S.; Li, J.; Xiong, H.; and Zhang, W. 2021 · 2021
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The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level
Chang, T.; Hu, Y.; Taylor, D.; and Quigley, B. M. 2022 · 2022
Cited alongside, same era.
Microestimates of wealth for all low-and middle-income countries
Chi, G.; Fang, H.; Chatterjee, S.; and Blumenstock, J. E. 2022 · 2022
Cited alongside, same era.
Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia
Draidi Areed, W.; Price, A.; Arnett, K.; and Mengersen, K. 2022 · 2022
Cited alongside, same era.
Understanding economic development in rural Africa using satellite imagery, building footprints and deep models
Elmustafa, A.; Rozi, E.; He, Y.; Mai, G.; Ermon, S.; Burke, M.; and Lobell, D. 2022 · 2022
Geollm: Extracting geospatial knowledge from large language models
Manvi, R.; Khanna, S.; Mai, G.; Burke, M.; Lobell, D.; and Ermon, S. 2023 · 2023
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Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach
Nie, T.; Qin, G.; Wang, Y.; and Sun, J. 2023 · 2023
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Enhanced geocoding precision for location inference of tweet text using spaCy, Nominatim and Google Maps. A comparative analysis of the influence of data selection
Serere, H. N.; Resch, B.; and Havas, C. R. 2023 · 2023
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TEST: Text prototype aligned embedding to activate LLM’s ability for time series
Sun, C.; Li, Y.; Li, H.; and Hong, S. 2023 · 2023
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Cited alongside, same era.
Kim, N.; and Yoon, Y. 2022 · 2022
Cited alongside, same era.
Spatial-temporal hypergraph self-supervised learning for crime prediction
Li, Z.; Huang, C.; Xia, L.; Xu, Y.; and Pei, J. 2022b · 2022
Cited alongside, same era.
Sgpt: Gpt sentence embeddings for semantic search
Muennighoff, N. 2022 · 2022
Cited alongside, same era.
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Nie, Y.; Nguyen, N. H.; Sinthong, P.; and Kalagnanam, J. 2022 · 2022
Cited alongside, same era.
Activity-aware human mobility prediction with hierarchical graph attention recurrent network
Tang, Y.; He, J.; and Zhao, Z. 2022 · 2022
Cited alongside, same era.
Self-supervised learning in remote sensing: A review
Wang, Y.; Albrecht, C. M.; Braham, N. A. A.; Mou, L.; and Zhu, X. X. 2022 · 2022
Cited alongside, same era.
Timesnet: Temporal 2d-variation modeling for general time series analysis
Wu, H.; Hu, T.; Liu, Y.; Zhou, H.; Wang, J.; and Long, M. 2022 · 2022
Cited alongside, same era.
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
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Improving text embeddings with large language models
Wang, L.; Yang, N.; Huang, X.; Yang, L.; Majumder, R.; and Wei, F. 2023 · 2023
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Lade: The first comprehensive last-mile delivery dataset from industry
Wu, L.; Wen, H.; Hu, H.; Mao, X.; Xia, Y.; Shan, E.; Zhen, J.; Lou, J.; Liang, Y.; Yang, L.; et al. 2023 · 2023
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Investigating the catastrophic forgetting in multimodal large language models
Zhai, Y.; Tong, S.; Li, X.; Cai, M.; Qu, Q.; Lee, Y. J.; and Ma, Y. 2023 · 2023
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DFNet: Decomposition fusion model for long sequence time-series forecasting
Zhang, F.; Guo, T.; and Wang, H. 2023 · 2023
Later among the works it cites.
Hierarchical knowledge graph learning enabled socioeconomic indicator prediction in location-based social network
Zhou, Z.; Liu, Y.; Ding, J.; Jin, D.; and Li, Y. 2023b · 2023
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Llm2vec: Large language models are secretly powerful text encoders
BehnamGhader, P.; Adlakha, V.; Mosbach, M.; Bahdanau, D.; Chapados, N.; and Reddy, S. 2024 · 2024
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Llm4ts: Aligning pre-trained llms as data-efficient time-series forecasters
Chang, C.; Wang, W.-Y.; Peng, W.-C.; and Chen, T.-F. 2024 · 2024
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K2: A foundation language model for geoscience knowledge understanding and utilization
Deng, C.; Zhang, T.; He, Z.; Chen, Q.; Shi, Y.; Xu, Y.; Fu, L.; Zhang, W.; Wang, X.; Zhou, C.; et al. 2024 · 2024
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Empowering time series analysis with large language models: A survey
Jiang, Y.; Pan, Z.; Zhang, X.; Garg, S.; Schneider, A.; Nevmyvaka, Y.; and Song, D. 2024 · 2024
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Urbangpt: Spatio-temporal large language models
Li, Z.; Xia, L.; Tang, J.; Xu, Y.; Shi, L.; Xia, L.; Yin, D.; and Huang, C. 2024 · 2024
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TinyViM: Frequency Decoupling for Tiny Hybrid Vision Mamba
Ma, X.; Ni, Z.; and Chen, X. 2024 · 2024
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Fine-tuning llama for multi-stage text retrieval
Ma, X.; Wang, L.; Yang, N.; Wei, F.; and Lin, J. 2024 · 2024
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Large language models are geographically biased
Manvi, R.; Khanna, S.; Burke, M.; Lobell, D.; and Ermon, S. 2024 · 2024
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Global poverty estimation using private and public sector big data sources
Marty, R.; and Duhaut, A. 2024 · 2024
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Repetition improves language model embeddings
Springer, J. M.; Kotha, S.; Fried, D.; Neubig, G.; and Raghunathan, A. 2024 · 2024
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Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer
Sun, H. 2024 · 2024
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PROGRAM: PROtotype GRAph Model based Pseudo-Label Learning for Test-Time Adaptation
Sun, H.; Xu, L.; Jin, S.; Luo, P.; Qian, C.; and Liu, W. 2024 · 2024
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Deep Time Series Models: A Comprehensive Survey and Benchmark
Wang, Y.; Wu, H.; Dong, J.; Liu, Y.; Long, M.; and Wang, J. 2024 · 2024
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Frequency-domain MLPs are more effective learners in time series forecasting
Yi, K.; Zhang, Q.; Fan, W.; Wang, S.; Wang, P.; He, H.; An, N.; Lian, D.; Cao, L.; and Niu, Z. 2024 · 2024
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UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
Yuan, Y.; Ding, J.; Feng, J.; Jin, D.; and Li, Y. 2024 · 2024
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Skip-Timeformer: Skip-Time Interaction Transformer for Long Sequence Time-Series Forecasting
Zhang, W.; Wang, H.; and Zhang, F. 2024 · 2024
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THATSN: Temporal hierarchical aggregation tree structure network for long-term time-series forecasting
Zhang, F.; Wang, M.; Zhang, W.; and Wang, H. 2025 · 2025
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